Debian Med Project
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Summary
Biology Development
paquets de Debian Med pour le développement d’applications bio-informatiques

Ce métapaquet installera des paquets Debian utiles pour le développement d’applications de recherche biologiques.

Description

For a better overview of the project's availability as a Debian package, each head row has a color code according to this scheme:

If you discover a project which looks like a good candidate for Debian Med to you, or if you have prepared an unofficial Debian package, please do not hesitate to send a description of that project to the Debian Med mailing list

Links to other tasks

Debian Med Biology Development packages

Official Debian packages with high relevance

bio-tradis
analyse des sorties d'analyses TraDIS de séquences de génomes
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Bio-Tradis fournit un ensemble d'outils pour analyser la sortie des analyses TraDIS.

La chaîne d'analyse Bio-Tradis est implémentée sous la forme d'une bibliothèque Perl extensible qui peut être utilisée telle quelle ou comme base de développement d'outils d'analyse plus évolués.

Note : il est nécessaire d’installer manuellement BioConductor Edger qui ne peut être distribué dans Debian dans une version récente car il utilise le code locfit qui n’est pas distribuable.

Please cite: Lars Barquist, Matthew Mayho, Carla Cummins, Amy K. Cain, Christine J. Boinett, Andrew J. Page, Gemma C. Langridge, Michael A. Quail, Jacqueline A. Keane and Julian Parkhill: The TraDIS toolkit: sequencing and analysis for dense transposon mutant libraries. (PubMed,eprint) Bioinformatics 32(7):1109-1111 (2016)
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biobambam2
tools for early stage alignment file processing
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This package contains some tools for processing BAM files, including

  bamsormadup:  parallel sorting and duplicate marking
  bamcollate2:  reads BAM and writes BAM reordered such that alignment
                or collated by query name
  bammarkduplicates: reads BAM and writes BAM with duplicate alignments
                marked using the BAM flags field
  bammaskflags: reads BAM and writes BAM while masking (removing) bits
                from the flags column
  bamrecompress: reads BAM and writes BAM with a defined compression
                 setting. This tool is capable of multi-threading.
  bamsort:       reads BAM and writes BAM resorted by coordinates or
                 query name
  bamtofastq:    reads BAM and writes FastQ; output can be collated
                 or uncollated by query name
The package is enhanced by the following packages: multiqc
Please cite: German Tischler and Steven Leonard: biobambam: tools for read pair collation based algorithms on BAM files. (eprint) Source Code Biol Med. 9:13 (2014)
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bioperl
outils en Perl pour la bio-informatique
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Le projet Bioperl est un effort coordonné ayant pour but de rassembler des méthodes utilisées couramment en bio-informatique dans un ensemble de modules Perl de type CPAN, bien documentés et disponibles librement. Il est bien accepté dans la communauté et est utilisé dans de nombreux projets de grande envergure, par exemple Ensembl.

Les paquets recommandés sont requis par certains des exécutables inclus. Une explication détaillée comprenant les modules spécifiques à Perl se trouve dans le fichier README.Debian.

Le paquet suggéré améliore les pages de manuel.

Please cite: Jason E Stajich, David Block, Kris Boulez, Steven E Brenner, Stephen A Chervitz, Chris Dagdigian, Georg Fuellen, James G R Gilbert, Ian Korf, Hilmar Lapp, Heikki Lehvaslaiho, Chad Matsalla, Chris J Mungall, Brian I Osborne, Matthew R Pocock, Peter Schattner, Martin Senger, Lincoln D Stein, Elia Stupka, Mark D Wilkinson and Ewan Birney: The Bioperl toolkit: Perl modules for the life sciences. (PubMed,eprint) Genome Res. 12(10):1611-1618 (2002)
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bioperl-run
scripts d'enveloppe BioPerl
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Ce paquet fournit des scripts du paquet BioPerl-Run. Il installe également toutes les applications enveloppables empaquetées dans Debian. Les applications non libres sont « suggérées » par ce paquet.

The package is enhanced by the following packages: clustalw exonerate kalign mcl
biosquid
utilitaire d'analyse de séquences biologiques
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BioSQUID est une bibliothèque de routines C pour l'analyse de séquences. Elle est livrée avec quelques petits programmes pour convertir, afficher quelques statistiques, manipuler et effectuer d'autres tâches sur des fichiers de séquence.

BioSQUID s'est d'abord appelée SQUID, mais un paquet Debian nommé Squid (un serveur mandataire cache) existant déjà, SQUID a été renommé.

Ce paquet fournit certains outils de démonstration des fonctions de la bibliothèque BioSQUID.

cwltool
implémentation de la référence du langage Common Workflow
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C'est l'implémentation de référence des standards de Common Workflow Language.

Les standards ouverts CWL servent à décrire des analyses de flux de travail et des outils afin de les rendre portables et déployabls sur une grande diversité d'environnements logiciels et matériels, des stations de travail aux clustrs en passant par le cloud et le calcul haute performance (HPC). CWL a été conçu pour satisfaire les besoins des scientifiques manipulant beaucoup de données comme la bioinformatique, l'imagerie médicaale, l'astronomie, la physique et la chimie.

L'implémentation de la référence CWL (cwltool) vise à être entièrement personnalisée et à offrir une validation exhaustive de fichiers CWL, mais aussi d'autres outils de travail avec des descriptions CWL.

Please cite: Peter Amstutz, Michael R. Crusoe, Nebojša Tijanić, Brad Chapman, John Chilton, Michael Heuer, Andrey Kartashov, Dan Leehr, Hervé Ménager, Maya Nedeljkovich, Matt Scales, Stian Soiland-Reyes and Luka Stojanovic: Common Workflow Language, v1.0. (2016)
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gffread
GFF/GTF format conversions, region filtering, FASTA sequence extraction
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Gffread is a GFF/GTF parsing utility providing format conversions, region filtering, FASTA sequence extraction and more.

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goby-java
next-generation sequencing data and results analysis tool
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Goby is a next-gen data management framework designed to facilitate the implementation of efficient data analysis pipelines.

Goby provides very efficient file formats to store next-generation sequencing data and intermediary analysis results.

Goby also provides utilities that implement common next-gen data computations. These utilities are designed to be relatively easy to use, yet very efficient.

This package provides the entire Goby framework, including application programs (i.e., Goby modes). It is released under the GPL3 license.

libace-perl
accès orienté objet aux bases de données AceDB
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AcePerl est une interface Perl orientée objet pour les bases de données AceDB. Il fournit les fonctionnalités pour la connexion à des bases de données AceDB distantes, réalisant les requêtes, la récupération des objets ACE et la mise à jour des bases de données. L'API de programmation est compatible avec l'API Java JADE et interopérable avec l'API utilisée par BoulderIO.

AceDB est un système de base de données génomique développée depuis 1989 essentiellement par Jean Thierry-Mieg (CNRS, Montpellier) et Richard Durbin (Sanger Institute). Il a été développé à l'origine pour le projet sur le génome de C. elegans, d'où dérive son nom (A C. elegans Database).

Please cite: Lincoln D. Stein and Jean Thierry-Mieg: Scriptable Access to the Caenorhabditis elegans Genome Sequence and Other ACEDB Databases. (PubMed,eprint) Genome Research 8(12):1308-1315 (1998)
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libai-fann-perl
enveloppe Perl pour la bibliothèque FANN
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Ce module fournit une enveloppe Perl pour la bibliothèque « Fast Artificial Neural Network » (FANN) (http://leenissen.dk/fann/wp/).

L'interface orientée objet AI::FANN fournit une correspondance presque directe à l'API de la bibliothèque C.

libbambamc-dev
fichiers de développement pour la lecture et l'écriture de fichiers BAM (alignement de génomes)
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Le format BAM est un format binaire pour le stockage de données de séquences. C'est une implémentation en⋅C du code de collation de noms lus provenant du projet plus important bambam⋅C++ pour gérer les fichiers BAM en entrée et en sortie.

Ce paquet fournit la bibliothèque statique et les fichiers d'en-tête.

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libbamtools-dev
API C++ de manipulation de fichiers BAM (alignement de génomes)
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BamTools facilite l'analyse de recherche et la gestion de données grâce aux fichiers BAM. Il arrive à gérer les énormes quantités de données produites par les technologies de séquençage actuelles qui sont généralement stockées dans des formats binaires compressés difficiles à gérer avec les analyseurs de textes couramment utilisés dans la recherche en bioinformatique.

BamTools fournit une API C++ pour la prise en charge des fichiers BAM ainsi qu'une boîte à outils en ligne de commande.

Ce paquet fournit l'API pour les développeurs.

Please cite: Derek W. Barnett, Erik K. Garrison, Aaron R. Quinlan, Michael P. Stromberg and Gabor T. Marth: BamTools: a C++ API and toolkit for analyzing and managing BAM files. (PubMed,eprint) Bioinformatics 27(12):1691-2 (2011)
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libbigwig-dev
C library for handling bigWig files - header files
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This package provides the files needed to develop with the libBigWig C library for reading/parsing local and remote bigWig and bigBed files.

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libbio-alignio-stockholm-perl
stockholm sequence input/output stream
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Indexes Stockholm format alignments such as those from Pfam and Rfam. Returns raw stream data using the ID or a Bio::SimpleAlign object (via Bio::AlignIO).

Bio::AlignIO::stockholm also allows for ID parsing using a callback:

libbio-asn1-entrezgene-perl
analyseur d'enregistrements Entrez Gene et Sequence du NCBI.
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Bio::ASN1::EntrezGene et Bio::ASN1::Sequence sont des analyseurs basés sur les expressions rationnelles pour les bases de données de génome Entrez Gene du NCBI (National Center for Biotechnology Information –⋅http://www.ncbi.nih.gov/entrez/query.fcgi?db=gene).

Ils analysent les enregistrements Entrez Gene au format ASN.1 et les séquences du NCBI, et renvoient les structures de données qui contiennent tous les éléments de donnée des enregistrements de gènes ou de séquences.

L'analyseur rendra compte des erreurs et de leur numéro de ligne si les données entrées ne sont pas conformes au format de fichier de génome Entrez Gene du NCBI ou d'annotation de Sequence du NCBI.

Bio::ASN1::Sequence est pour l'essentiel une version modifiée de l'analyseur à haute performance Bio::ASN1::EntrezGene. Néanmoins, ce module autonome existe dans la mesure où il est plus efficace pour extraire le code spécifique à Sequence d'EntrezGene.pm.

libbio-chado-schema-perl
couche DBIx::Class pour le schéma de base de données Chado
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Le module Bio::Chado::Schema est une couche standard de mappage objet-relationnel à utiliser avec le schéma de base de données Chado du projet GMOD. Chado est un schéma de base de données modulaire libre pour des données biologiques.

libbio-cluster-perl
BioPerl cluster modules
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The ClusterIO module works with the ClusterIO format module to read various cluster formats such as NCBI UniGene.

libbio-coordinate-perl
modules BioPerl pour travailler avec les coordonnées biologiques
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Le projet Bioperl a pour but de rassembler des méthodes utilisées couramment en bio-informatique dans un ensemble de modules Perl de type CPAN, bien documentés et disponibles librement.

Depuis la version⋅1.7 de BioPerl plusieurs modules ont été fractionnés en projets séparés. Ce paquet fournit le module Bio::Coordinate pour travailler avec les coordonnées biologiques.

libbio-das-lite-perl
implémentation du protocole BioDas
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Bio::Das::Lite est une implémentation du protocole BioDas pour récupérer des données biologiques à partir de sources XML sur HTTP.

Bio::Das::Lite est conçu comme une alternative légère et plus tolérante aux composants client, de récupération et d'analyse de Bio::Das. Bio::Das::Lite lui-même n'est pas une solution de remplacement de Bio::Das, mais il peut être transformé en sous-classe pour le faire.

libbio-db-biofetch-perl
Database object interface to BioFetch retrieval
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Bio::DB::BioFetch is a guaranteed best effort sequence entry fetching method. It goes to the Web-based dbfetch server located at the EBI (http://www.ebi.ac.uk/Tools/dbfetch/dbfetch) to retrieve sequences in the EMBL or GenBank sequence repositories.

Bio::DB::BioFetch implements all the Bio::DB::RandomAccessI interface, plus the get_Stream_by_id() and get_Stream_by_acc() methods that are found in the Bio::DB::SwissProt interface.

libbio-db-embl-perl
Database object interface for EMBL entry retrieval
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Allows the dynamic retrieval of sequence objects Bio::Seq from the EMBL database using the dbfetch script at EBI: http://www.ebi.ac.uk/Tools/dbfetch/dbfetch.

In order to make changes transparent host type (currently only ebi) and location (defaults to ebi) were separated out. This allows later additions of more servers in different geographical locations.

The functionality of this module is inherited from Bio::DB::DBFetch which implements Bio::DB::WebDBSeqI.

libbio-db-hts-perl
Perl interface to the HTS library
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HTSlib is an implementation of a unified C library for accessing common file formats, such as SAM (Sequence Alignment/Map), CRAM and VCF (Variant Call Format), used for high-throughput sequencing data, and is the core library used by samtools and bcftools. HTSlib only depends on zlib. It is known to be compatible with gcc, g++ and clang.

HTSlib implements a generalized BAM (binary SAM) index, with file extension 'csi' (coordinate-sorted index). The HTSlib file reader first looks for the new index and then for the old if the new index is absent.

This package provides a Perl interface to the HTS library.

libbio-db-ncbihelper-perl
collection of routines useful for queries to NCBI databases
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Provides a single place to setup some common methods for querying NCBI web databases. Bio::DB::NCBIHelper just centralizes the methods for constructing a URL for querying NCBI GenBank and NCBI GenPept and the common HTML stripping done in postprocess_data().

The base NCBI query URL used is: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi

libbio-db-seqfeature-perl
Normalized feature for use with Bio::DB::SeqFeature::Store
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The Bio::DB::SeqFeature object is the default SeqFeature class stored in Bio::DB::SeqFeature databases. It implements both the Bio::DB::SeqFeature::NormalizedFeatureI and Bio::DB::SeqFeature::NormalizedTableFeatureI interfaces, which means that its subfeatures, if any, are stored in the database in a normalized fashion, and that the parent/child hierarchy of features and subfeatures are also stored in the database as set of tuples. This provides efficiencies in both storage and retrieval speed.

Typically you will not create Bio::DB::SeqFeature directly, but will ask the database to do so on your behalf, as described in Bio::DB::SeqFeature::Store.

libbio-eutilities-perl
interface BioPerl pour les Entrez Programming Utilities (E-utilities)
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Le projet Bioperl a pour but de rassembler des méthodes utilisées couramment en bio-informatique dans un ensemble de modules Perl de type CPAN, bien documentés et disponibles librement. Ce paquet fournit une interface de programmation pour les «⋅Entrez Programming Utilities⋅» du NCBI, appelées habituellement E-utilities. En effet, il fournit les modules Perl Bio::DB::EUtilities et Bio::Tools::EUtilities.

Entrez est un moteur de recherche fédératif du NCBI (National Center for Biotechnology Information) pour un grand nombre de bases de données couvrant une grande variété de données bio-médicales, comme les séquences de nucléotides et de protéines, les enregistrements de gènes, les structures moléculaires en trois dimensions et la bibliographie biomédicale. Les E-utilities sont un ensemble de huit programmes côté serveur qui fournissent une interface stable au système de bases de données et de requêtes du NCBI.

libbio-featureio-perl
Modules for reading, writing, and manipulating sequence features
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An I/O iterator subsystem for genomic sequence features.

Bio::FeatureIO is a handler module for the formats in the FeatureIO set (eg, Bio::FeatureIO::GFF). It is the officially sanctioned way of getting at the format objects, which most people should use.

The Bio::FeatureIO system can be thought of like biological file handles. They are attached to filehandles with smart formatting rules (eg, GFF format, or BED format) and can either read or write feature objects (Bio::SeqFeature objects, or more correctly, Bio::FeatureHolderI implementing objects, of which Bio::SeqFeature is one such object). If you want to know what to do with a Bio::SeqFeatureI object, read Bio::SeqFeatureI.

The idea is that you request a stream object for a particular format. All the stream objects have a notion of an internal file that is read from or written to. A particular FeatureIO object instance is configured for either input or output. A specific example of a stream object is the Bio::FeatureIO::gff object.

libbio-graphics-perl
création d'images GD d'objets Bio::Seq
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La classe Bio::Graphics::Panel fournit des services de dessin et de formatage pour tout objet qui implémente l'interface Bio::SeqFeatureI, incluant les objets Ace::Sequence::Feature, Das::Segment::Feature et Bio::DB::Graphics. Elle peut être utilisée pour dessiner des annotations de séquence, des cartes physiques (séquence génomique continue –⋅contig), des domaines protéiques ou n'importe quel autre type de carte dans lequel un ensemble de séries discrètes doivent être présentées sur une droite.

libbio-mage-perl
module conteneur pour les classes du paquet MAGE
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Le format MAGE-TAB (MicroArray Gene Expression Tabular) est un standard de la Microarray Gene Expression Data Society (MGED). Ce paquet fournit des modules Perl dans la hiérarchie Bio::MAGE pour manipuler des enregistrements conformes à MIAME (Minimum Information About a Microarray Experiment) d'expérimentations de micromatrice (puces à ADN –⋅DNA chips).

Le module Bio::MAGE fournit les classes Bio::MAGE suivantes⋅:

 –⋅NameValueType
 –⋅Extendable
 –⋅Identifiable
 –⋅Describable
libbio-mage-utils-perl
modules supplémentaires pour les classes du paquet MAGE
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Le format MAGE-TAB (MicroArray Gene Expression Tabular) est un standard de la Microarray Gene Expression Data Society (MGED). Ce paquet fournit des modules Perl dans la hiérarchie Bio::MAGE pour manipuler des enregistrements conformes à MIAME (Minimum Information About a Microarray Experiment) d'expérimentations de micromatrice (puces à ADN –⋅DNA chips).

Bio-MAGE-Utils fournit des modules supplémentaires pour gérer le XML de MAGE et l'ontologie MGED, ainsi que des utilitaires SQL.

libbio-primerdesigner-perl
module Perl pour la conception d'amorce de PCR avec primer3 et epcr
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Bio::PrimerDesigner offre une interface de bas niveau pour les exécutables binaires primer3 et epcr, et fournit des méthodes pour renvoyer les résultats. En plus de l'accès à des installations locales de primer3 ou e-PCR, il offre la possibilité d'accéder à l'exécutable primer3 à travers un serveur distant.

libbio-samtools-perl
interface Perl à la bibliothèque SamTools pour le séquençage ADN
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Bio::SamTools fournit une interface Perl à la bibliothèque libbam pour les bases de données indexées ou non indexées d'alignement de séquence SAM/BAM. Il fournit la prise en charge de la recherche d'informations sur des alignements individuels, de paires de lectures («⋅reads⋅»), et d'information de chevauchement d'alignements sur de grandes régions. Il fournit aussi une fonctionnalité de rappel pour appeler des SNP et exécuter des fonctions base par base. La plupart des opérations sont compatibles avec l'interface Bio::SeqFeatureI de BioPerl, permettant aux fichiers BAM d'être utilisés comme dorsal à l'application de navigation génomique GBrowse.

libbio-scf-perl
extension Perl de lecture et d'écriture de fichiers de séquence SCF
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Le module Bio::SCF (Standard Chromatogram Format) permet de lire et de mettre à jour (d'une façon limitée) des fichiers de séquences chromatographiques SCF. Il s'agit d'une interface à la bibliothèque io-lib de Roger Staden. Il dispose à la fois d'une interface de table de hachage et d'une interface orientée objet. Il permet la lecture de champs de fichiers SCF et offre une possibilité limitée de les modifier et de les réécrire.

libbio-tools-phylo-paml-perl
Bioperl interface to the PAML suite
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This distribution provides a Perl interface to PAML, a suite of programs (baseml, codeml, evolver, and yn00) for phylogenetic analyses of DNA or protein sequences using maximum likelihood.

The Bio::Tools::Run::Phylo::PAML::* modules provide an interface to run the PAML programs while Bio::Tools::Phylo::PAML provides an interface to parse their output files.

This distribution is part of the Bioperl project.

libbio-tools-run-alignment-clustalw-perl
Bioperl interface to Clustal W
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Bio::Tools::Run::Alignment::Clustalw provides a Perl interface to Clustal W, a program for alignment of multiple nucleotide and peptide sequences.

This module distribution is part of the Bioperl project.

libbio-tools-run-alignment-tcoffee-perl
Bioperl interface to T-Coffee
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Bio::Tools::Run::Alignment::TCoffee provides a Perl interface to T-Coffee, a program for multiple alignments of DNA, RNA, and protein sequences and structures.

This module distribution is part of the Bioperl project.

libbio-tools-run-remoteblast-perl
Object for remote execution of the NCBI Blast via HTTP
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Class for remote execution of the NCBI Blast via HTTP.

For a description of the many CGI parameters see: https://www.ncbi.nlm.nih.gov/BLAST/Doc/urlapi.html

Various additional options and input formats are available.

libbio-variation-perl
BioPerl variation-related functionality
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The code in this distribution focuses on simple low-dependency variant-related functionality for BioPerl.

Bio::Variation name space contains modules to store sequence variation information as differences between the reference sequence and changes sequences. Also included are classes to write out and recrete objects from EMBL-like flat files and XML. Lastly, there are simple classes to calculate values for sequence change objects.

libbiojava-java
Java API to biological data and applications (default version)
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BioJava is an open-source project dedicated to providing a Java framework for processing biological data. It includes objects for manipulating sequences, file parsers, DAS client and server support, access to BioSQL and Ensembl databases, and powerful analysis and statistical routines including a dynamic programming toolkit.

BioJava is provided by a vibrant community which meets annually at the Bioinformatics Open Source Conference (BOSC) that traditionally accompanies the Intelligent Systems in Molecular Biology (ISMB) meeting. Much like BioPerl, the employment of this library is valuable for everybody active in the field because of the many tricks of the trade one learns just by communicating on the mailing list.

This is a dependency package which should enable smooth upgrades to new versions.

Please cite: R. C. G. Holland, T. Down, M. Pocock, A. Prlićand D. Huen, K. James, S. Foisy, A. Dräger, A. Yates, M. Heuer and M. J. Schreiber: BioJava: an Open-Source Framework for Bioinformatics. (PubMed,eprint) Bioinformatics 24(18):2096-2097 (2008)
Registry entries: Bio.tools 
libbiojava6-java
Java API to biological data and applications (version 6)
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This package presents the Open Source Java API to biological databases and a series of mostly sequence-based algorithms.

BioJava is an open-source project dedicated to providing a Java framework for processing biological data. It includes objects for manipulating sequences, file parsers, server support, access to BioSQL and Ensembl databases, and powerful analysis and statistical routines including a dynamic programming toolkit.

libbioparser-dev
library for parsing several formats in bioinformatics
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Bioparser is a c++ implementation of parsers for several bioinformatics formats. It consists of only one header file containing template parsers for FASTA, FASTQ, MHAP, PAF and SAM format. It also supports compressed files with gzip.

libblasr-dev
outils pour l'alignement de lectures PacBio sur des séquences cibles –⋅fichiers de développement
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Blasr_libcpp est une bibliothèque utilisée par blasr et d'autres exécutables tels que samtoh5 ou loadPulses pour analyser les séquences PacBio. Cette bibliothèque fournit trois sous bibliothèques, pbdata, hdf et alignment.

Ce paquet fournit les fichiers d'en-tête et la bibliothèque statique de la sous bibliothèque alignment.

libbpp-core-dev
fichiers de développement de la bibliothèque principale de Bio++
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Bio++ est un ensemble de bibliothèques pour la bio-informatique, comprenant l'analyse de séquences, la phylogénétique, l'évolution moléculaire et la génétique des populations. Bio++ est orienté objet et conçu pour être à la fois facile à utiliser et efficace d'un point de vue informatique. Bio++ vise à aider les programmeurs à écrire des programmes coûteux en leur fournissant un ensemble d'outils réutilisables.

Ce paquet fournit la bibliothèque statique et les fichiers d'en-tête des classes principales de Bio++.

libbpp-phyl-dev
fichiers de développement de la bibliothèque de phylogénétique de Bio++
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Bio++ est un ensemble de bibliothèques pour la bio-informatique, comprenant l'analyse de séquences, la phylogénétique, l'évolution moléculaire et la génétique des populations. Bio++ est orienté objet et conçu pour être à la fois facile à utiliser et efficace d'un point de vue informatique. Bio++ vise à aider les programmeurs à écrire des programmes coûteux en leur fournissant un ensemble d'outils réutilisables.

Ce paquet fournit la bibliothèque statique et les fichiers d'en-tête des classes de Bio++ pour la phylogénétique.

libbpp-phyl-omics-dev
bibliothèque de phylogénétique de Bio++ –⋅composants génomiques (fichiers de développement)
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Bio++ est un ensemble de bibliothèques pour la bio-informatique, comprenant l'analyse de séquences, la phylogénétique, l'évolution moléculaire et la génétique des populations. Bio++ est orienté objet et conçu pour être à la fois facile à utiliser et efficace d'un point de vue informatique. Bio++ vise à aider les programmeurs à écrire des programmes coûteux en leur fournissant un ensemble d'outils réutilisables.

Ce paquet fournit la bibliothèque statique et les fichiers d'en-tête des classes de Bio++ dédiées à la phylogénie du génome.

libbpp-popgen-dev
fichiers de développement de la bibliothèque de génétique des populations de Bio++
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Bio++ est un ensemble de bibliothèques pour la bio-informatique, comprenant l'analyse de séquences, la phylogénétique, l'évolution moléculaire et la génétique des populations. Bio++ est orienté objet et conçu pour être à la fois facile à utiliser et efficace d'un point de vue informatique. Bio++ vise à aider les programmeurs à écrire des programmes coûteux en leur fournissant un ensemble d'outils réutilisables.

Ce paquet fournit la bibliothèque statique et les fichiers d'en-tête des classes de génétique des populations de Bio++.

libbpp-qt-dev
fichiers de développement de la bibliothèque des classes graphiques⋅Qt de Bio++
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Bio++ est un ensemble de bibliothèques pour la bio-informatique, comprenant l'analyse de séquences, la phylogénétique, l'évolution moléculaire et la génétique des populations. Bio++ est orienté objet et conçu pour être à la fois facile à utiliser et efficace d'un point de vue informatique. Bio++ vise à aider les programmeurs à écrire des programmes coûteux en leur fournissant un ensemble d'outils réutilisables.

Ce paquet fournit les fichiers de développement des classes graphiques de Bio++ développées en⋅Qt.

libbpp-raa-dev
fichiers de développement de la bibliothèque d'accès distant ACNUC de Bio++
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Cette bibliothèque fournit un utilitaire et des classes pour interroger des bases de données publiques (GenBank, EMBL, SwissProt,⋅etc.) avec ACNUC. Elle fait partie du projet Bio++.

Ce paquet fournit les fichiers d'en-tête et la bibliothèque statique.

libbpp-seq-dev
fichiers de développement de la bibliothèque de séquences pour Bio++
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Bio++ est un ensemble de bibliothèques pour la bio-informatique, comprenant l'analyse de séquences, la phylogénétique, l'évolution moléculaire et la génétique des populations. Bio++ est orienté objet et conçu pour être à la fois facile à utiliser et efficace d'un point de vue informatique. Bio++ vise à aider les programmeurs à écrire des programmes coûteux en leur fournissant un ensemble d'outils réutilisables.

Ce paquet fournit la bibliothèque statique et les fichiers d'en-tête des classes de Bio++ pour l'analyse de séquences.

libbpp-seq-omics-dev
bibliothèque de séquences pour Bio++ – composants génomiques (fichiers de développement)
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Bio++ est un ensemble de bibliothèques pour la bio-informatique, comprenant l'analyse de séquences, la phylogénétique, l'évolution moléculaire et la génétique des populations. Bio++ est orienté objet et conçu pour être à la fois facile à utiliser et efficace d'un point de vue informatique. Bio++ vise à aider les programmeurs à écrire des programmes coûteux en leur fournissant un ensemble d'outils réutilisables.

Ce paquet fournit la bibliothèque statique et les fichiers d'en-tête des classes de Bio++ dédiées au séquençage du génome.

libcdk-java
Chemistry Development Kit (CDK) Java libraries
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The CDK is a library of Java classes used in computational and information chemistry and in bioinformatics. It includes renderers, file IO, SMILES generation/parsing, maximal common substructure algorithms, fingerprinting and much, much more.

libchado-perl
database schema and tools for genomic data
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Chado is a relational database schema that underlies many GMOD installations. It is capable of representing many of the general classes of data frequently encountered in modern biology such as sequence, sequence comparisons, phenotypes, genotypes, ontologies, publications, and phylogeny. It has been designed to handle complex representations of biological knowledge and should be considered one of the most sophisticated relational schemas currently available in molecular biology. The price of this capability is that the new user must spend some time becoming familiar with its fundamentals.

libcifpp-dev
Development files for libcifpp
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Libcifpp is a C++ library used to create and manipulate mmCIF and PDB files containing macro molecular structure information.

This specific package contains all files needed to develop new software using libcifpp.

libconsensuscore-dev
algorithms for PacBio multiple sequence consensus -- development files
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ConsensusCore is a library of C++ algorithms for Pacific Biosciences multiple sequence consensus that powers Quiver (Python) and ConsensusTools (.NET). This library primarily exists as the backend for GenomicConsensus, which implements Quiver.

This package is part of the SMRT Analysis suite. It provides the header files and static library.

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libdivsufsort-dev
libdivsufsort header files
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The libdivsufsort project provides a fast, lightweight, and robust C API library to construct the suffix array and the Burrows-Wheeler transformed string for any input string of a constant-size alphabet.

This package installs files needed for development.

libedlib-dev
library for sequence alignment using edit distance (devel)
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A lightweight and super fast C/C++ library for sequence alignment using edit distance.

Calculating edit distance of two strings is as simple as:

 edlibAlign("hello", 5, "world!", 6,
            edlibDefaultAlignConfig()).editDistance;
Features
  • Calculates edit distance (Levehnstein distance).
  • It can find optimal alignment path (instructions how to transform first sequence into the second sequence).
  • It can find just the start and/or end locations of alignment path - can be useful when speed is more important than having exact alignment path.
  • Supports multiple alignment methods: global(NW), prefix(SHW) and infix(HW), each of them useful for different scenarios.
  • You can extend character equality definition, enabling you to e.g. have wildcard characters, to have case insensitive alignment or to work with degenerate nucleotides.
  • It can easily handle small or very large sequences, even when finding alignment path, while consuming very little memory.
  • Super fast thanks to Myers's bit-vector algorithm.

This package contains the static library and the header files.

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libfast5-dev
library for reading Oxford Nanopore Fast5 files -- headers
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A lightweight C++11 library to read raw signal data from Oxford Nanopore's FAST5 files.

This package provides the header files for development with fast5.

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libfastahack-dev
library for indexing and sequence extraction from FASTA files (devel)
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fastahack is a small application for indexing and extracting sequences and subsequences from FASTA files. The included Fasta.cpp library provides a FASTA reader and indexer that can be embedded into applications which would benefit from directly reading subsequences from FASTA files. The library automatically handles index file generation and use.

Features:

  • FASTA index (.fai) generation for FASTA files
  • Sequence extraction
  • Subsequence extraction
  • Sequence statistics (currently only entropy is provided)

Sequence and subsequence extraction use fseek64 to provide fastest-possible extraction without RAM-intensive file loading operations. This makes fastahack a useful tool for bioinformaticists who need to quickly extract many subsequences from a reference FASTA sequence.

This is the development package containing the statically linked library and the header files.

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libffindex0-dev
index/base de données simple pour d'énormes quantités de petits fichiers — développement
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FFindex est un index et base de données très simples pour d'énormes quantités de petits fichiers. Les fichiers sont stockés concaténés dans un grand fichier de données, séparés par « \0 ». Un second fichier contient un index en texte brut, donnant le nom, la position et la longueur des petits fichiers. La recherche est actuellement réalisée avec une recherche binaire sur un tableau fabriqué à partir du fichier d'index.

Ce paquet fournit les fichiers d'en-tête et la documentation nécessaires au développement d'applications avec libffindex.

libfml-dev
development headers for libfml
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Fermi-lite is a standalone C library tool for assembling Illumina short reads in regions from 100bp to 10 million bp in size.

This package contains the C library headers for using libfml in custom tools, along with a static library.

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libgatbcore-dev
development library of the Genome Analysis Toolbox
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The GATB-CORE project provides a set of highly efficient algorithms to analyse NGS data sets. These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (e.g. metagenomes). Read more about GATB at https://gatb.inria.fr/. By itself GATB-CORE is not an NGS data analysis tool. However, it can be used to create such tools. There already exist a set of ready-to-use tools relying on GATB-CORE library: see https://gatb.inria.fr/software/

This package contains the static library and the header files of the gatb-core library.

Please cite: Erwan Drezen, Guillaume Rizk, Rayan Chikhi, Charles Deltel, Claire Lemaitre, Pierre Peterlongo and Dominique Lavenier: GATB: Genome Assembly & Analysis Tool Box. Bioinformatics 30(20):2959-2961 (2014)
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libgclib-dev
header files for Genome Code Lib (GCLib)
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This is an eclectic gathering of (mostly) C++ code which upstream used for some bioinformatics projects. The main idea is to provide lean code and efficient data structures, trying to avoid too many code dependencies of heavy libraries while minimizing production cycles (and this also implies a decent compile/build time -- looking at you, bloated configure scripts and lengthy compile times of Boost code or other heavy C++ template code..).

This code was gathered even before the C++ STL had been fully adopted as a cross-platform "standard". Since STL by itself is a bit heavier for most of the C++ needs, it is preferred to use simpler&leaner C++ classes or templates for basic strings, containers, basic algorithms etc.

Header files of Genome Code Lib. It is mainly known for being used by StringTie but with its own release cycle.

libgenome-dev
toolkit for developing bioinformatic related software (devel)
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libGenome is a freely available toolkit for developing bioinformatic related software in C++. It is intended to take the hassle out of performing common tasks on genetic sequence and annotation data.

Among other things, libGenome can help you:

  • Read and write Multi-FastA format files
  • Read and write GenBank flat file database entries
  • Append, chop, truncate, reverse, complement, translate, and otherwise mangle sequence data
  • Access annotation in GenBank flat files

This is the development package containing the statically linked library and the header files.

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libgenome-model-tools-music-perl
module for finding mutations of significance in cancer
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The MuSiC suite is a set of tools aimed at discovering the significance of somatic mutations found within a given cohort of cancer samples, and with respect to a variety of external data sources.

libgenome-perl
pipelines, tools, and data management for genomics
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This is the base namespace module for the Genome software tree.

That tree has several primary components:

Genome::Model: a data modeling pipeline management system for genomics

Genome::Model::Tools a tree of >1000 tools and tool wrappers for genomics

Genome::* a variety of sample tracking classes with an RDBMS back-end

Only the tools system is currently released.

See genome for a complete inventory of all tool packages, and for command-line access to those tools.

libgenometools0-dev
development files for GenomeTools
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This package contains the GenomeTools static library and necessary header files.

Besides basic bioinformatics data structures, the library contains components for sequence and annotation handling, sequence compression, index structure generation and access, efficient matching, annotation visualization and much more.

Please cite: Gordon Gremme, Sascha Steinbiss and Stefan Kurtz: GenomeTools: a comprehensive software library for efficient processing of structured genome annotations.. (PubMed) IEEE/ACM Transactions on Computational Biology and Bioinformatics 10(3):645-656 (2013)
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libgff-dev
GFF/GTF parsing from cufflinks as a library
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This is a simple "libraryfication" of the GFF/GTF parsing code that is used in the Cufflinks codebase. There are not many (any?) relatively lightweight GTF/GFF parsers exposing a C++ interface, and the goal of this library is to provide this functionality without the necessity of drawing in a heavy-weight dependency like SeqAn.

libgkarrays-dev
library to query large collection of NGS sequences (development)
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Gk-arrays are provided as a simple-to-use C++ library dedicated to queries on large collection of sequences as produced by high-throughput sequencers (e.g. HiSeq 2000 from Illumina, 454 from Roche).

Gk-arrays index k-mers of reads and allows one to answer different queries on that read collection (e.g. how many reads share this k-mer? where does this k-mer occur in the read collection?).

Gk-arrays consist of a space-efficient alternative to hash tables while being similar in terms of query times.

This is the development library for libgkarrays.

Please cite: Nicolas Philippe, Mikaël Salson, Thierry Lecroq, Martine Léonard, Thérèse Commes and Eric Rivals: Querying large read collections in main memory: a versatile data structure. (PubMed) BMC Bioinformatics 12:242 (2011)
libgo-perl
perl modules for GO and other OBO ontologies
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This is a collection of perl code for dealing with Gene Ontologies (GO) and Open Biomedical Ontologies (OBO) style ontologies. It is part of the ‘go-dev’ distribution, but this Debian package is made from the CPAN archive. This package contains both scripts (which can be used with no knowledge of perl), and libraries which will be of use to perl programmers using GO or OBO.

libhdf5-dev
HDF5 - development files - serial version
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Hierarchical Data Format 5 (HDF5) is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O.

This package contains development files for serial platforms.

libhmsbeagle-dev
High-performance lib for Bayesian and Maximum Likelihood phylogenetics (devel)
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BEAGLE is a high-performance library that can perform the core calculations at the heart of most Bayesian and Maximum Likelihood phylogenetics packages. It can make use of highly-parallel processors such as those in graphics cards (GPUs) found in many PCs.

The project involves an open API and fast implementations of a library for evaluating phylogenetic likelihoods (continuous time Markov processes) of biomolecular sequence evolution.

The aim is to provide high performance evaluation 'services' to a wide range of phylogenetic software, both Bayesian samplers and Maximum Likelihood optimizers. This allows these packages to make use of implementations that make use of optimized hardware such as graphics processing units.

This package contains development files needed to build against Beagle library.

Please cite: Daniel L. Ayres, Aaron Darling, Derrick J. Zwickl, Peter Beerli, Mark T. Holder, Paul O. Lewis, John P. Huelsenbeck, Fredrik Ronquist, David L. Swofford, Michael P. Cummings, Andrew Rambaut and Marc A. Suchard: BEAGLE: an Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics. (PubMed,eprint) Systematic Biology 61(1):170-3 (2012)
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libhts-dev
development files for the HTSlib
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HTSlib is an implementation of a unified C library for accessing common file formats, such as SAM (Sequence Alignment/Map), CRAM and VCF (Variant Call Format), used for high-throughput sequencing data, and is the core library used by samtools and bcftools. HTSlib only depends on zlib. It is known to be compatible with gcc, g++ and clang.

HTSlib implements a generalized BAM (binary SAM) index, with file extension ‘csi’ (coordinate-sorted index). The HTSlib file reader first looks for the new index and then for the old if the new index is absent.

This package contains development files for the HTSlib: headers, static library, manual pages, etc.

For compatibility with sambamba, the internal routine cram_to_bam was exported, hereto adopting a patch found in guix.

Please cite: Heng Li: Tabix: fast retrieval of sequence features from generic TAB-delimited files. (PubMed,eprint) Bioinformatics 27(5):718-719 (2011)
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libhtscodecs-dev
Development headers for custom compression for CRAM and others
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This library implements the custom CRAM codecs used for "EXTERNAL" block types. These consist of two variants of the rANS codec (8-bit and 16-bit renormalisation, with run-length encoding and bit-packing also supported in the latter), a dynamic arithmetic coder, and custom codecs for name/ID compression and quality score compression derived from fqzcomp.

They come with small command line test tools to act as both compression exploration programs and as part of the test harness.

This package contains the development headers

libhtsjdk-java
Java API for high-throughput sequencing data (HTS) formats
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HTSJDK is an implementation of a unified Java library for accessing common file formats, such as SAM (Sequence Alignment/Map) and VCF, used for high-throughput sequencing data. There are also an number of useful utilities for manipulating HTS data.

libjebl2-java
Java Evolutionary Biology Library
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A Java library for evolutionary biology and bioinformatics, including objects representing biomolecular sequences, multiple sequence alignments and phylogenetic trees.

This is a branch of the original JEBL on http://sourceforge.net/projects/jebl/ to develop a new API and class library.

Remark of Debian Med team: Fork from jebl

This is a branch of the original JEBL on http://sourceforge.net/projects/jebl/ to develop a new API and class library.

libjloda-java
Java library of data structures and algorithms for bioinformatics
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The jloda Java library provides some basic data structures and algorithms used by bioinformatics applications like SplitsTree, Dendroscope and MEGAN.

Please cite: Alexander Herbig, Frank Maixner, Kirsten I. Bos, Albert Zink, Johannes Krause and Daniel H. Huson: MALT: Fast alignment and analysis of metagenomic DNA sequence data applied to the Tyrolean Iceman. (eprint) bioRxiv (2016)
libkmer-dev
suite of tools for DNA sequence analysis (development lib)
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The kmer package is a suite of tools for DNA sequence analysis. It provides tools for searching (ESTs, mRNAs, sequencing reads); aligning (ESTs, mRNAs, whole genomes); and a variety of analyses based on kmers.

This package contains headers and static libraries for kmer.

Please cite: B. Walenz and L. Florea: Sim4db and leaff: Utilities for fast batched spliced alignment and sequence indexing. (PubMed) Bioinformatics 27(13):1869-1870 (2011)
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libmems-dev
development library to support DNA string matching and comparative genomics
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libMems is a freely available software development library to support DNA string matching and comparative genomics. Among other things, libMems implements an algorithm to perform approximate multi-MUM and multi-MEM identification. The algorithm uses spaced seed patterns in conjunction with a seed-and-extend style hashing method to identify matches. The method is efficient, requiring a maximum of only 16 bytes per base of the largest input sequence, and this data can be stored externally (i.e. on disk) to further reduce memory requirements.

This is the development package containing the statically linked library and the header files.

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libminimap2-dev
development headers for libminimap
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Minimap2 is a versatile sequence alignment program that aligns DNA or mRNA sequences against a large reference database. Typical use cases include: (1) mapping PacBio or Oxford Nanopore genomic reads to the human genome; (2) finding overlaps between long reads with error rate up to ~15%; (3) splice-aware alignment of PacBio Iso-Seq or Nanopore cDNA or Direct RNA reads against a reference genome; (4) aligning Illumina single- or paired-end reads; (5) assembly-to-assembly alignment; (6) full- genome alignment between two closely related species with divergence below ~15%.

This package contains the C library headers for using minimap in custom tools, along with a static library.

Please cite: Heng Li: Minimap2: pairwise alignment for nucleotide sequences. (PubMed,eprint) Bioinformatics :2103-2110 (2018)
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libmmblib-dev
development files of MacroMoleculeBuilder
Maintainer: Debichem Team
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MacroMoleculeBuilder, previously known as RNABuilder, can be used for morphing, homology modeling, folding (e.g. using base pairing contacts), redesigning complexes, fitting to low-resolution density maps, predicting local rearrangements upon mutation, and many other applications.

This package contains the development files.

libmuscle-dev
multiple alignment development library for protein sequences
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MUSCLE is a multiple alignment program for protein sequences. MUSCLE stands for multiple sequence comparison by log-expectation. In the authors tests, MUSCLE achieved the highest scores of all tested programs on several alignment accuracy benchmarks, and is also one of the fastest programs out there.

This library was derived from the original MUSCLE and turned into a library.

This package contains the static library and header files.

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libncbi-vdb-dev
libraries for using data in the INSDC Sequence Read Archives (devel)
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The (US) National Center for Biotechnology Information (NCBI)'s Virtual/Vertical Database (VDB) is a highly compressed column-oriented data warehousing technology developed initially to address the needs of the Sequence Read Archive (SRA). It is unique in that it builds databases from smaller parts that can function independently as documents, supports effective and efficient compression, supports encryption while remaining encrypted on disk, transparent distribution and remote access.

This is the development package for reading VDB data.

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libncbi6-dev
bibliothèques du NCBI pour les applications de biologie –⋅fichiers de développement
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Ce paquet fournit les versions de développement des bibliothèques⋅C du NCBI non graphiques avec les fichiers d'en-tête correspondants.

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libncl-dev
NEXUS Class Library (static lib and header files)
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The NEXUS Class Library is a C++ library for parsing NEXUS files.

The NEXUS file format is widely used in bioinformatics. Several popular phylogenetic programs such as Paup, MrBayes, Mesquite, and MacClade use this format.

This package contains the static library and header files of the NEXUS library.

Please cite: Paul O. Lewis: NCL: a C++ class library for interpreting data files in NEXUS format. (PubMed,eprint) Bioinformatics 19(17):2330-2331 (2003)
libngs-java
liaisons du langage de séquençage de nouvelle génération –⋅liaisons Java
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NGS est une nouvelle interface dédiée pour accéder aux lectures, alignements et fichiers au format pileup produits par les séquençages de nouvelle génération (Next Generation Sequencing). L'API elle-même ne dépend pas de l'implémentation d'un dorsal particulier et prend en charge l'utilisation de plusieurs dorsaux simultanément. Elle fournit aussi une bibliothèque pour construire de nouveaux moteurs de dorsaux. Le moteur pour accéder aux données SRA est fournit par le dépôt jumeau ncbi-vdb.

Cette interface de programmation est actuellement exprimée en langages⋅C++, Java et Python. Cette conception rend possible le maintien d'un haut degré de similarité du code entre deux langages –⋅surtout entre⋅C++ et Java.

Ce paquet fournit les liaisons Java.

Please cite: Rasko Leinonen, Ruth Akhtar, Ewan Birney, James Bonfield, Lawrence Bower, Matt Corbett, Ying Cheng, Fehmi Demiralp, Nadeem Faruque, Neil Goodgame, Richard Gibson, Gemma Hoad, Christopher Hunter, Mikyung Jang, Steven Leonard, Quan Lin, Rodrigo Lopez, Michael Maguire, Hamish McWilliam, Sheila Plaister, Rajesh Radhakrishnan, Siamak Sobhany, Guy Slater, Petra Ten Hoopen, Franck Valentin, Robert Vaughan, Vadim Zalunin, Daniel Zerbino and Guy Cochrane: Improvements to services at the European Nucleotide Archive. (PubMed,eprint) Nucleic Acids Research 38(Database issue):D39-45 (2010)
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libngs-sdk-dev
liaisons du langage de séquençage de nouvelle génération –⋅fichiers de développement
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NGS est une nouvelle interface dédiée pour accéder aux lectures, alignements et fichiers au format pileup produits par les séquençages de nouvelle génération (Next Generation Sequencing). L'API elle-même ne dépend pas de l'implémentation d'un dorsal particulier et prend en charge l'utilisation de plusieurs dorsaux simultanément. Elle fournit aussi une bibliothèque pour construire de nouveaux moteurs de dorsaux. Le moteur pour accéder aux données SRA est fournit par le dépôt jumeau ncbi-vdb.

Cette interface de programmation est actuellement exprimée en langages⋅C++, Java et Python. Cette conception rend possible le maintien d'un haut degré de similarité du code entre deux langages –⋅surtout entre⋅C++ et Java.

Il s'agit du paquet de développement.

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libnhgri-blastall-perl
Perl extension for running and parsing NCBI's BLAST 2.x
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NHGRI::Blastall will enable usage of BLAST out of a Perl script, if BLAST2 or WU-BLAST are installed locally. Main features are:

  • run BLAST (also via network, which requires blastcl3)
  • BLAST single sequences against each other or against a given library
  • format databases
  • mask out repetitive DNA
  • read, parse and filter existing BLAST reports
libopenmm-dev
C++ header files for the OpenMM library
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OpenMM is a software toolkit for performing molecular simulations on a range of high performance computing architectures. This package provides C++ header files for the development with that library.

Please cite: P. Eastman, J. Swails, J. D. Chodera, R. T. McGibbon, Y. Zhao, K. A. Beauchamp, L.-P. Wang, A. C. Simmonett, M. P. Harrigan, C. D. Stern, R. P. Wiewiora, B. R. Brooks and V. S. Pande: OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. (PubMed,eprint) PLOS Comp. Biol. 13(7):e1005659 (2017)
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libopenms-dev
library for LC/MS data management and analysis - dev files
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OpenMS is a library for LC/MS data management and analysis. OpenMS offers an infrastructure for the development of mass spectrometry-related software and powerful 2D and 3D visualization solutions.

OpenMS offers analyses for various quantitation protocols, including label-free quantitation, SILAC, iTRAQ, SRM, SWATH…

It provides built-in algorithms for de-novo identification and database search, as well as adapters to other state-of-the art tools like X!Tandem, Mascot, OMSSA…

OpenMS supports the Proteomics Standard Initiative (PSI) formats for MS data and supports easy integration of tools into workflow engines like Knime, Galaxy, WS-Pgrade, and TOPPAS via the TOPPtools concept and a unified parameter handling.

This package ships the library development files.

Please cite: Marc Sturm, Andreas Bertsch, Clemens Gröpl, Andreas Hildebrandt, Rene Hussong, Eva Lange, Nico Pfeifer, Ole Schulz-Trieglaff, Alexandra Zerck, Knut Reinert and Oliver Kohlbacher: OpenMS – an Open-Source Software Framework for Mass Spectrometry. (PubMed,eprint) BMC Bioinformatics 9(163) (2008)
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libpal-java
Phylogenetic Analysis Library
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The PAL project is a collaborative effort to provide a high quality Java library for use in molecular evolution and phylogenetics. At present PAL consists of approximately 200 public classes/interfaces in 16 packages Please refer to the API documentation for a detailed description of all classes and methods available, and to the release history for an overview of the development history of PAL.

Please cite: Alexei Drummond and Korbinian Strimmer: PAL: an object-oriented programming library for molecular evolution and phylogenetics. (PubMed,eprint) Bioinformatics 17(7):662-663 (2001)
libparasail-dev
Development heaaders and static libraries for parasail
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This package provides development headers and static libraries for parasail. It is a SIMD C library containing implementations of the Smith-Waterman, Needleman-Wunsch, and various semi-global pairwise sequence alignment algorithm.

libpbbam-dev
Pacific Biosciences binary alignment/map (BAM) library (headers)
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The BAM format is a binary, compressed, record-oriented container format for raw or aligned sequence reads. The associated SAM format is a text representation of the same data. The specifications for BAM/SAM are maintained by the SAM/BAM Format Specification Working Group.

PacBio-produced BAM files are fully compatible with the BAM specification, but makes use of the extensibility mechanisms of the BAM specification to encode PacBio-specific information. The pbbam library provides tools to work with these files

This package contains the library header files.

libpbdata-dev
tools for handling PacBio sequences (development files)
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Blasr_libcpp is a library used by blasr and other executables such as samtoh5, loadPulses for analyzing PacBio sequences. This library contains three sub-libraries, including pbdata, hdf and alignment.

This package contains the header files and static library for the pbdata sublibrary.

libpbihdf-dev
tools for handling PacBio hdf5 files (development files)
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Blasr_libcpp is a library used by blasr and other executables such as samtoh5, loadPulses for analyzing PacBio sequences. This library contains three sub-libraries, including pbdata, hdf and alignment.

This package contains the header files and static library for the hdf sublibrary.

libpbseq-dev
library for analyzing PacBio sequencing data (development files)
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Blasr_libcpp is a library used by blasr and other executables such as samtoh5, loadPulses for analyzing PacBio sequences. This library contains three sub-libraries, including pbdata, hdf and alignment.

This is a metapackage that depends on the pbseqlib component development files.

libpdb-redo-dev
Development files for libpdb-redo
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This library contains shared code for the various programs in the project PDB-REDO.

This specific package contains all files needed to develop new software using libpdb-redo.

libpll-dev
Phylogenetic Likelihood Library (development)
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PLL is a highly optimized, parallelized software library to ease the development of new software tools dealing with phylogenetic inference.

Among the functions included in PLL are parsing multiple sequence alignments (MSA) from PHYLIP and FASTA files, reading Newick trees, performing topological moves such as SPR and NNI, model optimization, likelihood evaluation and partitioned analysis by assigning different substitution models to each partition of the MSA. PLL fully implements the GTR nucleotide substitution model for DNA data and a number of models for aminoacid data.

This package contains the static library and the header file.

libpwiz-dev
library to perform proteomics data analyses (devel files)
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The libpwiz library from the ProteoWizard project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs standard chemistry and LCMS dataset computations.

The primary goal of ProteoWizard is to eliminate the existing barriers to proteomic software development so that researchers can focus on the development of new analytic approaches, rather than having to dedicate significant resources to mundane (if important) tasks, like reading data files.

This package ships the library development files.

libqes-dev
DNA sequence parsing library -- development
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A small C library, with a bioinformatic focus. Optimised for speed and a clean API. Handles sequence parsing and miscellaneous manipulation of DNA sequences.

These are the development headers required to use libqes in your own applications.

librcsb-core-wrapper0-dev
development files for librcsb-core-wrapper0t64
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The RCSB Core Wrapper library was developed to provide an object-oriented application interface to information in mmCIF format. It includes several classes for accessing data dictionaries and mmCIF format data files.

This package contains files necessary for developing applications with the library.

The package is enhanced by the following packages: librcsb-core-wrapper0-dbg
librdp-taxonomy-tree-java
taxonomy tree library from Ribosomal Database Project (RDP)
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The TaxonomyTree project is a library used by other Ribosomal Database Project (RDP) tools.

librelion-dev
C++ API for RELION (3D reconstructions in cryo-electron microscopy)
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RELION (for REgularised LIkelihood OptimisatioN) is a stand-alone computer program for Maximum A Posteriori refinement of (multiple) 3D reconstructions or 2D class averages in cryo-electron microscopy.

RELION provides a GUI, several command-line tools in parallel (MPI) and serial versions as well as a C++ API.

This is the developers API package for use without GUI and MPI.

Please cite: Sjors H. W. Scheres: RELION: implementation of a Bayesian approach to cryo-EM structure determination. (PubMed) J. Struct. Biol. 180(3):519-30 (2012)
librg-blast-parser-perl
very fast NCBI BLAST parser - binding for Perl
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This package contains Perl binding for a very fast C++ library that parses the default output of NCBI BLAST programs. BLAST results are returned in a convenient hash structure.

Evaluated on a very small test set, this parser is considerably faster than Zerg::Report from libzerg-perl.

librg-reprof-bundle-perl
protein secondary structure and accessibility predictor (perl module)
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'reprof' is an improved implementation of 'prof', a popular protein secondary structure and accessibility predictor. Prediction is either done from protein sequence alone or from an alignment - the latter should be used for optimal performance.

This package provides the perl modules implementing 'reprof' along with the necessary data files.

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librostlab-blast0-dev
very fast C++ library for parsing the output of NCBI BLAST programs (devel)
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This package provides a very fast library for parsing the default output of NCBI BLAST programs into a C++ structure.

libzerg is faster, but it provides only lexing (i.e. it only returns pairs of token identifiers and token string values). librostlab-blast uses a parser generated with bison on top of a flex-generated lexer very similar to that of libzerg.

This package contains files necessary for developing applications with librostlab-blast.

librostlab3-dev
C++ library for computational biology (development)
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This library was developed by the Rost Lab. The lab's research is driven by a conviction that protein and DNA sequences encode a significant core of information about the ultimate structure and function of genetic material and its gene products.

The library provides the following facilities:

  • current working directory resource
  • exception with stack backtrace
  • file lock resource
  • passwd and group structures for C++
  • effective uid and gid resource
  • rostlab::bio::seq class with stream input operator for FASTA format
  • umask resource

This package contains files necessary for developing applications with librostlab.

libsbml5-dev
System Biology Markup Language library - development files
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LibSBML is a library designed to help you read, write, manipulate, translate, and validate SBML files and data streams. It is not an application itself (though it does come with many example programs), but rather a library you can embed in your own applications.

This package contains files necessary for development with libsbml.

libseqan2-dev
C++ library for the analysis of biological sequences (development)
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SeqAn is a C++ template library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data. This library applies a unique generic design that guarantees high performance, generality, extensibility, and integration with other libraries. SeqAn is easy to use and simplifies the development of new software tools with a minimal loss of performance.

This package contains the developer files.

Please cite: Andreas Doring, David Weese, Tobias Rausch and Knut Reinert: SeqAn An efficient, generic C++ library for sequence analysis. (PubMed,eprint) BMC Bioinformatics 9(1):11 (2008)
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libseqan3-dev
C++ library for the analysis of biological sequences v3 (development)
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SeqAn is a C++ template library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data. This library applies a unique generic design that guarantees high performance, generality, extensibility, and integration with other libraries. SeqAn is easy to use and simplifies the development of new software tools with a minimal loss of performance.

This package contains the developer files.

Please cite: Andreas Doring, David Weese, Tobias Rausch and Knut Reinert: SeqAn An efficient, generic C++ library for sequence analysis. (PubMed,eprint) BMC Bioinformatics 9(1):11 (2008)
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libseqlib-dev
C++ htslib/bwa-mem/fermi interface for interrogating sequence data (dev)
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C++ API and command line tool that provides a rapid and user-friendly interface to BAM/SAM/CRAM files, global sequence alignment operations and sequence assembly. Four C libraries perform core operations in SeqLib: HTSlib for BAM access, BWA-MEM and BLAT for sequence alignment and Fermi for error correction and sequence assembly. Benchmarking indicates that SeqLib has lower CPU and memory requirements than leading C++ sequence analysis APIs. Minimal SeqLib code can extract, error-correct and assemble reads from a CRAM file and then align with BWA-MEM. SeqLib also provides additional capabilities, including chromosome-aware interval queries and read plotting. Command line tools are available for performing integrated error correction, micro-assemblies and alignment.

This package contains the header files and static library.

libslow5-dev
header and static library for reading & writing SLOW5 files
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Slow5lib is a software library for reading & writing SLOW5 files. Slow5lib is designed to facilitate use of data in SLOW5 format by third- party software packages. Existing packages that read/write data in FAST5 format can be easily modified to support SLOW5.

SLOW5 is a new file format for storing signal data from Oxford Nanopore Technologies (ONT) devices. SLOW5 was developed to overcome inherent limitations in the standard FAST5 signal data format that prevent efficient, scalable analysis and cause many headaches for developers. SLOW5 can be encoded in human-readable ASCII format, or a more compact and efficient binary format (BLOW5) - this is analogous to the seminal SAM/BAM format for storing DNA sequence alignments. The BLOW5 binary format supports zlib (DEFLATE) compression, or other compression methods, thereby minimising the data storage footprint while still permitting efficient parallel access. Detailed benchmarking experiments have shown that SLOW5 format is an order of magnitude faster and significantly smaller than FAST5.

This is the development package containing headers and static library.

libsmithwaterman-dev
determine similar regions between two strings or genomic sequences (devel)
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The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings or nucleotide or protein sequences. Instead of looking at the total sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure.

This is the development package containing the statically linked library and the header files.

libsnp-sites1-dev
bibliothèques statiques et fichiers d'en-tête pour le paquet snp-sites
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Ce programme découvre les positions de polymorphisme d'un seul nucléotide (SNP) dans les fichiers d’entrée d’alignements au format multi-fasta (pouvant être compressés). Sa sortie peut être dans divers formats largement utilisés (Multi Fasta Alignment, Vcf, phylip).

Ce logiciel a été développé à l’institut Wellcome Trust Sanger.

Ce paquet fournit les fichiers de développement pour l'inclusion de snp- sites dans son code. La bibliothèque permet aux développeurs en Python de faire des appels de fonctions de snp-sites (liaisons Python) au travers de la bibliothèque Boost Python.

Please cite: Andrew J. Page, Ben Taylor, Aidan J. Delaney, Jorge Soares, Torsten Seemann, Jacqueline A. Keane and Simon R. Harris: SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments. (eprint) Microbial Genomics 2(4) (2016)
libsort-key-top-perl
Perl module to select and sort top n elements of a list
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The functions available from this module select the top n elements from a list using several common orderings and custom key extraction procedures.

They are all variations around 'keytopsort { CALC_KEY($_) } $n => @data;'.

In array context, this function calculates the ordering key for every element in @data using the expression inside the block. Then it selects and orders the $n elements with the lower keys when compared lexicographically.

In scalar context, the value returned by the functions on this module is the cutoff value allowing to select nth element from the array.

libspoa-dev
SIMD partial order alignment library (development files)
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Spoa (SIMD POA) is a c++ implementation of the partial order alignment (POA) algorithm (as described in 10.1093/bioinformatics/18.3.452) which is used to generate consensus sequences (as described in 10.1093/bioinformatics/btg109). It supports three alignment modes: local (Smith-Waterman), global (Needleman-Wunsch) and semi-global alignment (overlap).

This package contains the static library and the header files.

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libsrf-dev
C++ implementation of the SRF format for DNA sequence data
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SRF (sort for Sequence Read Format) is a generic format capable of storing data generated by any DNA sequencing technology. This library is an implementation of SRF and provides basic input-output functions.

libssm-dev
macromolecular superposition library - development files
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SSM is a macromolecular coordinate superposition library, written by Eugene Krissinel of the EBI.

The library implements the SSM algorithm of protein structure comparison in three dimensions, which includes an original procedure of matching graphs built on the protein's secondary-structure elements, followed by an iterative three-dimensional alignment of protein backbone Calpha atoms.

This package contains libraries and header files needed for program development.

Please cite: E. Krissinel and K. Henrick: Secondary-structure matching (SSM), a new tool for fast protein structure alignment in three dimensions. (PubMed,eprint) Acta Crystallogr D Biol Crystallogr. 60(1):2256-68 (2004)
libssu-dev
high-performance phylogenetic diversity calculations (dev)
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The de facto repository for high-performance phylogenetic diversity calculations. The methods in this repository are based on an implementation of the Strided State UniFrac algorithm which is faster, and uses less memory than Fast UniFrac. Strided State UniFrac supports Unweighted UniFrac, Weighted UniFrac, Generalized UniFrac, Variance Adjusted UniFrac and meta UniFrac. This repository also includes Stacked Faith (manuscript in preparation), a method for calculating Faith's PD that is faster and uses less memory than the Fast UniFrac-based reference implementation.

This package contains the static library and header files.

Please cite: Daniel McDonald, Yoshiki Vázquez-Baeza, David Koslicki, Jason McClelland, Nicolai Reeve, Zhenjiang Xu, Antonio Gonzalez and Rob Knight: Striped UniFrac: enabling microbiome analysis at unprecedented scale. (PubMed) Nature Methods 15(11):847–848 (2018)
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libssw-dev
Development headers and static libraries for libssw
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This package provides development headers and static libraries for libssw, a fast implementation of the Smith-Waterman algorithm using Single-Instruction Multiple-Data (SIMD) instructions to parallelize the algorithm at the instruction level.

libssw-java
Java bindings for libssw
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This package provides JNI based Java bindings for libssw, a fast implementation of the Smith-Waterman algorithm using Single-Instruction Multiple-Data (SIMD) instructions to parallelize the algorithm at the instruction level.

libstaden-read-dev
development files for libstaden-read
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This package contains the header and development files needed to build programs and packages using the Staden io_lib.

The io_lib from the Staden package is a library of file reading and writing code to provide a general purpose trace file (and Experiment File) reading interface. It has been compiled and tested on a variety of unix systems, MacOS X and MS Windows.

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libstatgen-dev
development files for the libStatGen
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libStatGen is a library for statistical genetic programs. It includes some: A. General Operation Classes including: File/Stream I/O, String processing

 and Parameter Parsing.
B. Statistical Genetic Specific Classes including: Handling Common file
 formats (Accessors to get/set values, Indexed access to BAM files) and
 some utility classes, including: 1. Cigar: interpretation and mapping
 between query and reference. 2. Pileup: structured access to data by
 individual reference position.

This package provides the development files for libstatgen.

libswiss-perl
Perl API to the UniProt database
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UniProt, SwissProt and TrEMBL are different views on protein sequence data that is prepared by groups at the European Bioinformatics Institute (EMBL-EBI) in Cambridge and the Swiss Bioinformatics Institute (SIB) at the University Hospital in Geneva.

The SwissKnife Perl library is used by the developers of these databases to perform all the automated editing and sytax checks. The users of this package will profit from the stable API on an ever evolving representation of biological knowledge.

libtabixpp-dev
C++ wrapper to tabix indexer (development files)
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This package provides development headers and static libraries for libtabixpp, a C++ interface wrapper for Tabix. Tabix is a part of htslib to index tabular files in which some columns indicate sequence coordinates.

libthread-pool-dev
C++ header-only thread pool library (devel)
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A thread pool is a software design pattern for achieving concurrency of execution in a computer program. Often also called a replicated workers or worker-crew model, a thread pool maintains multiple threads waiting for tasks to be allocated for concurrent execution by the supervising program.

libvcflib-dev
C++ library for parsing and manipulating VCF files (development)
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The Variant Call Format (VCF) is a flat-file, tab-delimited textual format intended to concisely describe reference-indexed variations between individuals. VCF provides a common interchange format for the description of variation in individuals and populations of samples, and has become the defacto standard reporting format for a wide array of genomic variant detectors.

vcflib provides methods to manipulate and interpret sequence variation as it can be described by VCF. It is both:

  • an API for parsing and operating on records of genomic variation as it can be described by the VCF format,
  • and a collection of command-line utilities for executing complex manipulations on VCF files.

This package contains the static library and the header files.

libvibrant6-dev
bibliothèques du NCBI pour applications graphiques de biologie –⋅fichiers de développement
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Vibrant permet de développer des applications graphiques pour la biologie qui sont portables (Motif, MS-Windows, Mac-OS).

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libwfa2-dev
exact gap-affine algorithm (development)
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The wavefront alignment (WFA) algorithm is an exact gap-affine algorithm that takes advantage of homologous regions between the sequences to accelerate the alignment process. Unlike to traditional dynamic programming algorithms that run in quadratic time, the WFA runs in time O(ns+s^2), proportional to the sequence length n and the alignment score s, using O(s^2) memory (or O(s) using the ultralow/BiWFA mode). Moreover, the WFA algorithm exhibits simple computational patterns that the modern compilers can automatically vectorize for different architectures without adapting the code. To intuitively illustrate why the WFA algorithm is so interesting, look at the following figure. The left panel shows the cells computed by a classical dynamic programming based algorithm (like Smith-Waterman or Needleman Wunsch). In contrast, the right panel shows the cells computed by the WFA algorithm to obtain the same result (i.e., the optimal alignment).

This package contains the static library and the header files.

libzerg-perl
fast perl module for parsing the output of NCBI BLAST programs
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The Zerg library contains a C/flex lexical scanner for BLAST reports and a set of supporting functions. It is centered on a "get_token" function that scans the input for specified lexical elements and, when one is found, returns its code and value to the user.

It is intended to be fast: for that the authors used flex, which provides simple regular expression matching and input buffering in the generated C scanner. And it is intended to be simple in the sense of providing just a lexical scanner, with no features whose support could slow down its main function.

libzerg0-dev
development libraries and header files for libzerg
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Zerg is a C library for lexing - lexically scanning - the output of NCBI BLAST programs.

Based on a GNU Flex-generated lexical scanner, it runs extremely fast, being especially useful for processing large volumes of data. Benchmark tests show that Zerg is over two orders of magnitude faster than some widely used BLAST parsers.

If you need a parser and not only a lexer, check out librostlab-blast.

This package contains the header files and documentation needed to develop applications with libzerg.

mcl
algorithme de Markov pour les grappes
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Le paquet MCL implémente l'algorithme MCL (Markov Cluster Algorithm) et fournit les utilitaires nécessaires à la manipulation de matrices creuses (type de matrices essentielles dans l'algorithme MCL) et à la réalisation d’expérimentations dans les grappes.

MCL est utilisé dans divers domaines comme en biologie (détection de familles de protéines, génomiques), en informatique (grappe de nœuds dans un réseau pair à pair) ou la linguistique (analyse de textes).

The package is enhanced by the following packages: zoem
Please cite: Stijn van Dongen and Cei Abreu-Goodger: Using MCL to extract clusters from networks. (PubMed,eprint) Methods Mol Biol. 804:281-95 (2012)
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nim-hts-dev
wrapper for hts C library
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The hts library is well accepted for the handling of millions of DNA sequences from what once was the high-throughput sequencing machines in biological research and medical diagnostics/therapy control.

The package is enhanced by the following packages: nim-hts-examples
nim-kexpr-dev
kexpr math expressions for nim
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This package contains the nim wrapper for Heng Li's kexpr math expression library.

nim-lapper-dev
simple, fast interval searches for nim
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This package uses a binary search in a sorted list of intervals along with knowledge of the longest interval. It works when the size of the largest interval is smaller than the average distance between intervals. As that ratio of largest-size::mean-distance increases, the performance decreases. On realistic (for the author's use-case) data, this is 1000 times faster to query results and >5000 times faster to check for presence than a brute-force method.

Lapper also has a special case seek method when queries are expected to be in order. This method uses a cursor to indicate that start of the last search and does a linear search from that cursor to find matching intervals. This gives an additional 2-fold speedup over the find method.

ont-fast5-api
simple interface to HDF5 files of the Oxford Nanopore .fast5 file format
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Ont_fast5_api is a simple interface to HDF5 files of the Oxford Nanopore .fast5 file format.

It provides:

  • Concrete implementation of the fast5 file schema using the generic h5py library
  • Plain-english-named methods to interact with and reflect the fast5 file schema
  • Tools to convert between multi_read and single_read formats
  • Tools to compress/decompress raw data in files
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pyfai
Fast Azimuthal Integration scripts
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PyFAI is a Python library for azimuthal integration; it allows the conversion of diffraction images taken with 2D detectors like CCD cameras into X-Ray powder patterns that can be used by other software like Rietveld refinement tools (i.e. FullProf), phase analysis or texture analysis.

As PyFAI is a library, its main goal is to be integrated in other tools like PyMca, LiMa or EDNA. To perform online data analysis, the precise description of the experimental setup has to be known. This is the reason why PyFAI includes geometry optimization code working on "powder rings" of reference samples. Alternatively, PyFAI can also import geometries fitted with other tools like Fit2D.

PyFAI has been designed to work with any kind of detector with any geometry (transmission, reflection, off-axis, ...). It uses the Python library FabIO to read most images taken by diffractometer.

python3-airr
Data Representation Standard library for antibody and TCR sequences
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This package provides a library by the AIRR community to for describing, reporting, storing, and sharing adaptive immune receptor repertoire (AIRR) data, such as sequences of antibodies and T cell receptors (TCRs). Some specific efforts include:

  • The MiAIRR standard for describing minimal information about AIRR datasets, including sample collection and data processing information.
  • Data representations (file format) specifications for storing large amounts of annotated AIRR data.
  • APIs for exposing a common interface to repositories/databases containing AIRR data.
  • A community standard for software tools which will allow conforming tools to gain community recognition.

This package installs the library for Python 3.

python3-anndata
annotated gene by sample numpy matrix
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AnnData provides a scalable way of keeping track of data together with learned annotations. It is used within Scanpy, for which it was initially developed. Both packages have been introduced in Genome Biology (2018).

Please cite: F. Alexander Wolf, Philipp Angerer and Fabian J. Theis: SCANPY: large-scale single-cell gene expression data analysis.. (PubMed) Genome Biol. 19:15 (2018)
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python3-bcbio-gff
Python3 library to read and write Generic Feature Format
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A Python library to read and write Generic Feature Format (GFF).

Generic Feature Format (GFF) is a biological sequence file format for representing features and annotations on sequences. It is a tab delimited format, making it accessible to biologists and editable in text editors and spreadsheet programs. It is also well defined and can be parsed via automated programs. GFF files are available from many of the large sequencing and annotation centers.

python3-bioframe
library to enable flexible, scalable operations on genomic interval dataframes
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Building bioframe directly on top of pandas enables immediate access to a rich set of dataframe operations. Working in Python enables rapid visualization (e.g. matplotlib, seaborn) and iteration of genomic analyses.

Bioframe implements a variety of genomic interval operations directly on dataframes. Bioframe also includes functions for loading diverse genomic data formats, and performing operations on special classes of genomic intervals, including chromosome arms and fixed size bins.

python3-biom-format
format de matrice d'observation biologique (BIOM) – Python 3
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Le format de fichiers BIOM (prononcé biome) est conçu pour être un format d'usage général pour représenter un échantillon biologique avec des tables de contingence d'observations. BIOM est un standard reconnu pour le projet Earth Microbiome et est un projet candidat du Genomics Standards Consortium.

Le format BIOM est conçu pour une utilisation générale dans de vastes disciplines de -omique comparative. Par exemple, dans les études de gènes marqueurs, l'utilisation principale de ce format est de représenter des tables OTU : dans ce cas, les observations sont des OTU et la matrice contient les comptes correspondant au nombre de fois que chaque OTU est observé dans chaque échantillon. Par rapport aux données de métagénomes, ce format servirait à représenter les tables de métagénomes : dans ce cas, les observations pourraient correspondre aux sous-systèmes SEED et la matrice contiendrait les comptes correspondant au nombre de fois que chaque sous-système est observé dans chaque métagénome. De même, pour les données de génome, ce format pourrait servir à représenter un ensemble de génomes : dans ce cas, les observations pourraient à nouveau correspondre aux sous-systèmes SEED et les comptes pourraient correspondre au nombre de fois que chaque sous-système est observé dans chaque génome.

Ce paquet fournit la bibliothèque du format BIOM pour l'interpréteur Python 3.

Please cite: Daniel McDonald, Jose C. Clemente, Justin Kuczynski, Jai R. Rideout, Jesse Stombaugh, Doug Wendel, Andreas Wilke, Susan Huse, John Hufnagle, Folker Meyer, Rob Knight and J. G. Caporaso: The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. (eprint) GigaScience 1:7 (2012)
python3-biomaj3
BioMAJ workflow management library
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BioMAJ downloads remote data banks, checks their status and applies transformation workflows, with consistent state, to provide ready-to-use data for biologists and bioinformaticians. For example, it can transform original FASTA files into BLAST indexes. It is very flexible and its post-processing facilities can be extended very easily.

BioMAJ3 is a rewrite of BioMAJ v1.x, see online documentation for migration.

This package contains the library to manage the workflow update in BioMAJ3, it is managed via python3-biomaj3-daemon (for microservices remote operations) or biomaj3-cli (local or remote) packages

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python3-biopython
Python3 library for bioinformatics
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The Biopython Project is an international association of developers of freely available Python tools for computational molecular biology.

It is a distributed collaborative effort to develop Python3 libraries and applications which address the needs of current and future work in bioinformatics. The source code is made available under the Biopython License, which is extremely liberal and compatible with almost every license in the world. The project works along with the Open Bioinformatics Foundation, who generously provide web and CVS space for the project.

Please cite: Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski and Michiel J. L. de Hoon: Biopython: freely available Python tools for computational molecular biology and bioinformatics. (PubMed,eprint) Bioinformatics 25(11):1422-1423 (2009)
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python3-biotools
Python3 bioinformatics utilities for high-throughput genomic sequencing
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This package contains utilities like

 biotools.align - align sequences (hybrid between Needleman-Wunsch and
                  Smith-Waterman which is used to find the subsequence
                  within a larger sequence that best aligns to a reference)
 biotools.annotation - create annotation files. The annotations can be used
                       to create a hierarchy among the annotations
 biotools.BLAST - manage BLAST databases and interface with the BLAST+
                  standalone program available from NCBI.
 biotools.clustal - interface to clustalw global (multiple nucleotide or
                    peptide sequence alignment)
 biotools.complement - creates the complement of a sequence, which can then be
                       reversed
 biotools.sequence - various tools to deal with sequences
 biotools.translate - translate a nucleotide using the standard genetic code

This package contains the Python3 module.

Please cite: Rebecca Bart, Megan Cohn, Andrew Kassen, Emily J. McCallum, Mikel Shybut, Annalise Petriello, Ksenia Krasileva, Douglas Dahlbeck, Cesar Medina, Titus Alicai, Lava Kumar, Leandro M. Moreira, Júlio Rodrigues Neto, Valerie Verdier, María Angélica Santana, Nuttima Kositcharoenkul, Hervé Vanderschuren, Wilhelm Gruissem, Adriana Bernal and Brian J. Staskawicz: High-throughput genomic sequencing of cassava bacterial blight strains identifies conserved effectors to target for durable resistance. (PubMed) PNAS 109(28):E1972-9 (2012)
python3-bx
library to manage genomic data and its alignment
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The bx-python project is a Python3 library and associated set of scripts to allow for rapid implementation of genome scale analyses. The library contains a variety of useful modules, but the particular strengths are:

  • Classes for reading and working with genome-scale multiple local alignments (in MAF, AXT, and LAV formats)
  • Generic data structure for indexing on disk files that contain blocks of data associated with intervals on various sequences (used, for example, to provide random access to individual alignments in huge files; optimized for use over network filesystems)
  • Data structures for working with intervals on sequences
  • "Binned bitsets" which act just like chromosome sized bit arrays, but lazily allocate regions and allow large blocks of all set or all unset bits to be stored compactly
  • "Intersecter" for performing fast intersection tests that preserve both query and target intervals and associated annotation
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python3-cgecore
Python3 module for the Center for Genomic Epidemiology
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This Python3 module contains classes and functions needed to run the service wrappers and pipeline scripts developed by the Center for Genomic Epidemiology.

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python3-cigar
manipulate SAM cigar strings
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Cigar is a simple Python3 library for dealing with cigar strings. the most useful feature now is soft-masking from left or right. This allows one to adjust a SAM record only by changing the cigar string to soft-mask a number of bases such that the rest of the SAM record (pos, tlen, etc.) remain valid, but downstream tools will not consider the soft-masked bases in further analysis.

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python3-cobra
constraint-based modeling of biological networks with Python 3
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COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. COBRApy is a constraint-based modeling package that is designed to accommodate the biological complexity of the next generation of COBRA models and provides access to commonly used COBRA methods, such as flux balance analysis, flux variability analysis, and gene deletion analyses.

Please cite: Ali Ebrahim, Joshua A Lerman, Bernhard O Palsson and Daniel R Hyduke: COBRApy: COnstraints-Based Reconstruction and Analysis for Python. (PubMed) BMC Systems Biology 7(74) (2013)
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python3-cogent3
framework pour la biologie du génome
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PyCogent est une bibliothèque pour la biologie du génome. C'est un framework complètement intégré et testé en profondeur pour :

  • le pilotage d'applications tierces ;
  • la conception de workflows ; l'interrogation des bases de données ;
  • la conduite de nouvelles analyses probabilistes de l'évolution de séquences biologiques ;
  • la génération de publications avec des graphismes de qualité. Il se distingue par de nombreuses fonctionnalités uniques intégrées (telles que l'alignement de codons véritables) et l'ajout fréquent de méthodes complètement nouvelles pour l'analyse de données de génomes.
Please cite: Rob Knight, Peter Maxwell, Amanda Birmingham, Jason Carnes, J Gregory Caporaso, Brett C Easton, Michael Eaton, Micah Hamady, Helen Lindsay, Zongzhi Liu, Catherine Lozupone, Daniel McDonald, Michael Robeson, Raymond Sammut, Sandra Smit, Matthew J Wakefield, Jeremy Widmann, Shandy Wikman, Stephanie Wilson, Hua Ying and Gavin A Huttley: PyCogent: a toolkit for making sense from sequence. (PubMed,eprint) Genome Biology 8(8):R171 (2007)
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python3-cooler
library for a sparse, compressed, binary persistent storage
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Cooler is a support library for a sparse, compressed, binary persistent storage format, also called cooler, used to store genomic interaction data, such as Hi-C contact matrices.

The cooler file format is an implementation of a genomic matrix data model using HDF5 as the container format. The cooler package includes a suite of command line tools and a Python API to facilitate creating, querying and manipulating cooler files.

The package is enhanced by the following packages: python3-cooler-examples
Please cite: Nezar Abdennur and Leonid A Mirny: Cooler: scalable storage for Hi-C data and other genomically labeled arrays. (PubMed) Bioinformatics 36(1):311–316 (2019)
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python3-corepywrap
library that exports C++ mmCIF accessors to Python3
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The RCSB Core Wrapper library was developed to provide an object-oriented application interface to information in mmCIF format. It includes several classes for accessing data dictionaries and mmCIF format data files.

This library provides Python3 bindings for librcsb-core-wrapper.

python3-csb
Python framework for structural bioinformatics (Python3 version)
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Computational Structural Biology Toolbox (CSB) is a Python class library for reading, storing and analyzing biomolecular structures in a variety of formats with rich support for statistical analyses.

CSB is designed for reusability and extensibility and comes with a clean, well-documented API following good object-oriented engineering practice.

This is the Python3 version of the package.

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python3-cutadapt
Clean biological sequences from high-throughput sequencing reads (Python 3)
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Cutadapt helps with biological sequence clean tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.

This package contains the Python 3 module.

Please cite: Marcel Martin: Cutadapt removes adapter sequences from high-throughput sequencing reads. (eprint) EMBnet.journal 17(1):10-12 (2015)
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python3-cyvcf2
VCF parser based on htslib (Python 3)
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This modules allows fast parsing of VCF and BCF including region-queries with Python. This is essential for efficient analyses of nucleotide variation with Python on high-throughput sequencing data.

cyvcf2 is a cython wrapper around htslib. Attributes like variant.gt_ref_depths return a numpy array directly so they are immediately ready for downstream use.

This package installs the library for Python 3.

Please cite: Brent S. Pedersen and Aaron R. Quinlan: cyvcf2: fast, flexible variant analysis with Python. (eprint) Bioinformatics 33(12):1867–1869 (2017)
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python3-deeptools
platform for exploring biological deep-sequencing data
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Aiming for compatibility with the Galaxy worklfow environment, but also independently contributing to a series of workflows in genomics, this package provides a series of tools to address common tasks for the processing of high-throughput DNA/RNA sequencing.

Please cite: Fidel Ramirez, Devon P. Ryan, Björn Grüning, Sarah Diehl, Vivek Bhardwaj, Fabian Kilpert, Andreas S Richter, Steffen Heyne, Friederike Dündar and Thomas Manke: deepTools2: a next generation web server for deep-sequencing data analysis. (eprint) Nucleic Acids Research :W160–W165 (2016)
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python3-deeptoolsintervals
gestion de style GTF de séquences, intervalles associés, annotations
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Les régions dans des séquences biologiques sont décrites (annotées) comme gènes, sites de facteur de transcription, régions de basse complexité… et tout ce qui se rapporte à la recherche en biologie.

Ce paquet fournit une bonne exploitation de ces informations.

python3-dendropy
DendroPy Phylogenetic Computing Library (Python 3)
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DendroPy is a Python library for phylogenetic computing. It provides classes and functions for the simulation, processing, and manipulation of phylogenetic trees and character matrices, and supports the reading and writing of phylogenetic data in a range of formats, such as NEXUS, NEWICK, NeXML, Phylip, FASTA, etc. Application scripts for performing some useful phylogenetic operations, such as data conversion and tree posterior distribution summarization, are also distributed and installed as part of the library. DendroPy can thus function as a stand-alone library for phylogenetics, a component of more complex multi-library phyloinformatic pipelines, or as a scripting “glue” that assembles and drives such pipelines.

This package provides python3 modules.

Please cite: Jeet Sukumaran and Mark T. Holder: DendroPy: a Python library for phylogenetic computing. (PubMed,eprint) Bioinformatics 26(12):1569-1571 (2010)
python3-dnaio
Python 3 library for fast parsing of FASTQ and FASTA files
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dnaio is a Python 3 library for fast parsing of FASTQ and also FASTA files. The code was previously part of the cutadapt tool and has been improved since it has been split out.

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python3-ete3
Python Environment for (phylogenetic) Tree Exploration - Python 3.X
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The Environment for Tree Exploration (ETE) is a Python programming toolkit that assists in the recontruction, manipulation, analysis and visualization of phylogenetic trees (although clustering trees or any other tree-like data structure are also supported).

ETE is currently developed as a tool for researchers working in phylogenetics and genomics. If you use ETE for a published work, please cite:

Visit http://etetoolkit.org for more info.

Please cite: Jaime Huerta-Cepas, François Serra and Peer Bork: ETE 3: Reconstruction analysis and visualization of phylogenomic data. (eprint) Mol. Biol. Evol. (2016)
python3-fast5
library for reading Oxford Nanopore Fast5 files -- Python 3
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A lightweight C++11 library to read raw signal data from Oxford Nanopore's FAST5 files.

This package provides the Python 3 library

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python3-freecontact
fast protein contact predictor - binding for Python3
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FreeContact is a protein residue contact predictor optimized for speed. Its input is a multiple sequence alignment. FreeContact can function as an accelerated drop-in for the published contact predictors EVfold-mfDCA of DS. Marks (2011) and PSICOV of D. Jones (2011).

FreeContact is accelerated by a combination of vector instructions, multiple threads, and faster implementation of key parts. Depending on the alignment, 8-fold or higher speedups are possible.

A sufficiently large alignment is required for meaningful results. As a minimum, an alignment with an effective (after-weighting) sequence count bigger than the length of the query sequence should be used. Alignments with tens of thousands of (effective) sequences are considered good input.

jackhmmer(1) from the hmmer package, or hhblits(1) from hhsuite can be used to generate the alignments, for example.

This package contains the Python3 binding.

Please cite: László Kaján, Thomas A. Hopf, Matúš Kalaš, Debora S. Marks and Burkhard Rost: FreeContact: ... BMC Bioinformatics (201?)
python3-gfapy
flexible and extensible software library for handling sequence graphs
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The Graphical Fragment Assembly (GFA) are formats for the representation of sequence graphs, including assembly, variation and splicing graphs. Two versions of GFA have been defined (GFA1 and GFA2) and several sequence analysis programs have been adopting the formats as an interchange format, which allow the user to easily combine different sequence analysis tools.

This library implements the GFA1 and GFA2 specification. It is possible to create a Gfa object from a file in the GFA format or from scratch, to enumerate the graph elements (segments, links, containments, paths and header lines), to traverse the graph (by traversing all links outgoing from or incoming to a segment), to search for elements (e.g. which links connect two segments) and to manipulate the graph (e.g. to eliminate a link or a segment or to duplicate a segment distributing the read counts evenly on the copies).

The GFA format can be easily extended by users by defining own custom tags and record types. In Gfapy, it is easy to write extensions modules, which allow one to define custom record types and datatypes for the parsing and validation of custom fields. The custom lines can be connected, using references, to each other and to lines of the standard record types.

Please cite: Giorgio Gonnella and Stefan Kurtz: GfaPy: a flexible and extensible software library for handling sequence graphs in Python. (PubMed) Bioinformatics (2017)
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python3-gffutils
Work with GFF and GTF files in a flexible database framework
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A Python package for working with and manipulating the GFF and GTF format files typically used for genomic annotations. Files are loaded into a sqlite3 database, allowing much more complex manipulation of hierarchical features (e.g., genes, transcripts, and exons) than is possible with plain-text methods alone.

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python3-gtfparse
parser for gene transfer format (aka GFF2)
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You find a gene in the genome? Or a feature about it? The gene transfer format (GTF, identical to GFF2) allows your program or your database to exchange this information so it can be presented with genome browsers or used e.g. as a selection for other features like nucleotide variants.

This package provides a parser for GTF/GFF2 files, i.e. sets of routines that read that file and support the computational interpretation of these data.

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python3-htseq
Python3 high-throughput genome sequencing read analysis utilities
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HTSeq can be used to performing a number of common analysis tasks when working with high-throughput genome sequencing reads:

  • Getting statistical summaries about the base-call quality scores to study the data quality.
  • Calculating a coverage vector and exporting it for visualization in a genome browser.
  • Reading in annotation data from a GFF file.
  • Assigning aligned reads from an RNA-Seq experiments to exons and genes.

This package contains the Python 3 module.

Please cite: Simon Anders, Paul Theodor Pyl and Wolfgang Huber: HTSeq—a Python framework to work with high-throughput sequencing data. (PubMed,eprint) Bioinformatics 31(2):166-169 (2015)
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python3-intervaltree-bio
Interval tree convenience classes for genomic data -- Python 3 library
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Convenience classes for loading UCSC genomic annotation records into a set of interval tree data structures.

This package provides the Python 3 library.

python3-kineticstools
detection of DNA modifications (Python 3 library)
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Tools for detecting DNA modifications from single molecule, real-time (SMRT®) sequencing data. This tool implements the P_ModificationDetection module in SMRT® Portal, used by the RS_Modification_Detection and RS_Modifications_and_Motif_Detection protocol. Researchers interested in understanding or extending the modification detection algorithms can use these tools as a starting point.

This package is part of the SMRTAnalysis suite and contains the backend Python 3 library.

python3-loompy
access loom formatted files for bioinformatics
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Loom is an efficient file format for very large omics datasets, consisting of a main matrix, optional additional layers, a variable number of row and column annotations. Loom also supports sparse graphs. Loom files are used to store single-cell gene expression data: the main matrix contains the actual expression values (one column per cell, one row per gene); row and column annotations contain metadata for genes and cells, such as Name, Chromosome, Position (for genes), and Strain, Sex, Age (for cells).

Loom files (.loom) are created in the HDF5 file format, which supports an internal collection of numerical multidimensional datasets. HDF5 is supported by many computer languages, including Java, MATLAB, Mathematica, Python, R, and Julia. .loom files are accessible from any language that supports HDF5.

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python3-mirtop
annotate miRNAs with a standard mirna/isomir naming (Python 3)
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The main goal of this project is to create a reflection group on metazoan microRNAs (miRNAs), open to all interested researchers, to identify blockages and develop standards and guidelines to improve miRNA research, resources and communication. This can go through the use of standardized file formats, gene and variants nomenclature guidelines, and advancements in miRNA biology understanding. The group will eventually also aim at expanding its breadth to the development of novel tools, data resources, and best-practices guidelines to benefit the scientific community by providing high confidence validated research and analysis strategies, regardless the expertise in this field. This package provides the Python modules for mirtop to execute correctly.

Please cite: Thomas Desvignes, Karen Eilbeck, Ioannis S. Vlachos, Bastian Fromm, Yin Lu, Marc K. Halushka, Michael Hackenberg, Gianvito Urgese, Elisa Ficarra, Shruthi Bandyadka, Jason Sydes, Peter Batzel, John H. Postlethwait, Phillipe Loher, Eric Londin, Aristeidis G. Telonis, Isidore Rigoutsos and Lorena Pantano Rubino: miRTOP: An open source community project for the development of a unified format file for miRNA data [version 1; not peer reviewed]. (eprint) F1000Research 7(ISCB Comm. J.):953 (Slides) (2018)
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python3-nanoget
extract information from Oxford Nanopore sequencing data and alignments
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The Python3 module nanoget provides functions to extract useful metrics from Oxford Nanopore sequencing reads and alignments.

Data can be presented in the following formats, using the following functions:

  • sorted bam file process_bam(bamfile, threads)
  • standard fastq file process_fastq_plain(fastqfile, 'threads')
  • fastq file with metadata from MinKNOW or Albacore process_fastq_rich(fastqfile)
  • sequencing_summary file generated by Albacore process_summary(sequencing_summary.txt, 'readtype')

Fastq files can be compressed using gzip, bzip2 or bgzip. The data is returned as a pandas DataFrame with standardized headernames for convenient extraction. The functions perform logging while being called and extracting data.

The package is enhanced by the following packages: python3-nanoget-examples
python3-ngs
liaisons du langage de séquençage de nouvelle génération –⋅liaisons Python⋅3
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NGS est une nouvelle interface dédiée pour accéder aux lectures, alignements et fichiers au format pileup produits par les séquençages de nouvelle génération (Next Generation Sequencing). L'API elle-même ne dépend pas de l'implémentation d'un dorsal particulier et prend en charge l'utilisation de plusieurs dorsaux simultanément. Elle fournit aussi une bibliothèque pour construire de nouveaux moteurs de dorsaux. Le moteur pour accéder aux données SRA est fournit par le dépôt jumeau ncbi-vdb.

Cette interface de programmation est actuellement exprimée en langages⋅C++, Java et Python. Cette conception rend possible le maintien d'un haut degré de similarité du code entre deux langages –⋅surtout entre⋅C++ et Java.

Ce paquet fournit les liaisons Python⋅3.

Please cite: Rasko Leinonen, Ruth Akhtar, Ewan Birney, James Bonfield, Lawrence Bower, Matt Corbett, Ying Cheng, Fehmi Demiralp, Nadeem Faruque, Neil Goodgame, Richard Gibson, Gemma Hoad, Christopher Hunter, Mikyung Jang, Steven Leonard, Quan Lin, Rodrigo Lopez, Michael Maguire, Hamish McWilliam, Sheila Plaister, Rajesh Radhakrishnan, Siamak Sobhany, Guy Slater, Petra Ten Hoopen, Franck Valentin, Robert Vaughan, Vadim Zalunin, Daniel Zerbino and Guy Cochrane: Improvements to services at the European Nucleotide Archive. (PubMed,eprint) Nucleic Acids Research 38(Database issue):D39-45 (2010)
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python3-pairix
1D/2D indexing and querying with a pair of genomic coordinates
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Pairix is a tool for indexing and querying on a block-compressed text file containing pairs of genomic coordinates.

Pairix is a stand-alone C program that was written on top of tabix as a tool for the 4DN-standard pairs file format describing Hi-C data: pairs_format_specification.md

However, Pairix can be used as a generic tool for indexing and querying any bgzipped text file containing genomic coordinates, for either 2D- or 1D- indexing and querying.

For example: given the custom text file below, you want to extract specific lines from the Pairs file further below. An awk command would read the Pairs file from beginning to end. Pairix creates an index and uses it to access the file from a relevant position by taking advantage of bgzf compression, allowing for a fast query on large files.

The package is enhanced by the following packages: python-pairix-examples
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python3-pangolearn
store of the trained model for pangolin to access
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Pangolin runs a multinomial logistic regression model trained against lineage assignments based on GISAID data.

Legacy pangolin runs using a guide tree and alignment hosted at cov-lineages/lineages. Some of this data is sourced from GISAID, but anonymised and encrypted to fit with guidelines. Appropriate permissions have been given and acknowledgements for the teams that have worked to provide the original SARS-CoV-2 genome sequences to GISAID are also hosted here.

This package contains the store of the trained model for pangolin.

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python3-parasail
Python3 bindings for the parasail C library
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This package provides the Python3 bindings for parasail. Parasail is a SIMD C library containing implementations of the Smith-Waterman, Needleman-Wunsch, and various semi-global pairwise sequence alignment algorithm.

python3-pbcommand
common command-line interface for Pacific Biosciences analysis modules
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To integrate with the pbsmrtpipe workflow engine, one must to be able to generate a Tool Contract and to be able to run from a Resolved Tool Contract. A Tool Contract contains the metadata of the exe, such as the file types of inputs, outputs and options. There are two principal use cases, first wrapping/calling Python functions that have been defined in external Python 3 packages, or scripts. Second, creating a CLI tool that supports emitting tool contracts, running resolved tool contracts and complete argparse-style CLI.

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python3-pbconsensuscore
algorithms for PacBio multiple sequence consensus -- Python 3
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ConsensusCore is a library of C++ algorithms for Pacific Biosciences multiple sequence consensus that powers Quiver (Python) and ConsensusTools (.NET). This library primarily exists as the backend for GenomicConsensus, which implements Quiver.

This package is part of the SMRT Analysis suite. It provides the Python3 bindings.

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python3-pbcore
bibliothèque de Python 3 pour traiter les fichiers de données de PacBio
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Le paquet pbcore fournit des modules de Python pour traiter les fichiers de données de Pacific Biosciences et construire des applications bioinformatiques PacBio. Ces modules incluent des outils pour lire et écrire aux formats de données de PacBio, des fichiers de données d’échantillons pour le test et le débogage, des classes basiques et des utilitaires pour construire des applications bioinformatiques.

Ce paquet fait partie de la suite SMRTAnalysis.

Ce paquet fournit le module en Python 3.

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python3-peptidebuilder
generate atomic oligopeptide 3D structure from sequence
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PeptideBuilder is a simple Python library to generate model peptides. Typically on daisychains a few residues in e.g. biopython, and so does PeptideBuilder, but it does it right.

Parameters like the backbone formation can be specified ab initio, rotamers/energy minimisation is left to respective specialist tools.

Please cite: Matthew Z. Tien, Dariya K. Sydykova, Austin G. Meyer and Claus O. Wilke: PeptideBuilder: A simple Python library to generate model peptides. (PubMed,eprint) PeerJ 1:e80 (2013)
python3-presto
toolkit for processing B and T cell sequences (Python3 module)
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pRESTO is a toolkit for processing raw reads from high-throughput sequencing of B cell and T cell repertoires.

Dramatic improvements in high-throughput sequencing technologies now enable large-scale characterization of lymphocyte repertoires, defined as the collection of trans-membrane antigen-receptor proteins located on the surface of B cells and T cells. The REpertoire Sequencing TOolkit (pRESTO) is composed of a suite of utilities to handle all stages of sequence processing prior to germline segment assignment. pRESTO is designed to handle either single reads or paired-end reads. It includes features for quality control, primer masking, annotation of reads with sequence embedded barcodes, generation of unique molecular identifier (UMI) consensus sequences, assembly of paired-end reads and identification of duplicate sequences. Numerous options for sequence sorting, sampling and conversion operations are also included.

This package provides the presto Python3 module.

Please cite: Jason A. Vander Heiden, Gur Yaari, Mohamed Uduman, Joel N.H. Stern, Kevin C. O’Connor, David A. Hafler, Francois Vigneault and Steven H. Kleinstein: pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. (PubMed,eprint) Bioinformatics 30(13):1930-1932 (2014)
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python3-propka
heuristic pKa calculations with ligands (Python 3)
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PROPKA predicts the pKa values of ionizable groups in proteins (version 3.0) and protein-ligand complexes (version 3.1 and later) based on the 3D structure.

For proteins without ligands both versions should produce the same result.

This package installs the library for Python 3.

Please cite: Chresten R. Søndergaard, Mats H. M. Olsson, Michał Rostkowski and Jan H. Jensen: Improved Treatment of Ligands and Coupling Effects in Empirical Calculation and Rationalization of pKa Values. Journal of Chemical Theory and Computation 7(7):2284–2295 (2011)
python3-py2bit
access to 2bit files
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From https://genome.ucsc.edu/FAQ/FAQformat.html#format7: A .2bit file stores multiple DNA sequences (up to 4 Gb total) in a compact randomly-accessible format. The file contains masking information as well as the DNA itself.

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python3-pyabpoa
adaptive banded Partial Order Alignment - python3 module
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abPOA is an extended version of Partial Order Alignment (POA) that performs adaptive banded dynamic programming (DP) with an SIMD implementation. abPOA can perform multiple sequence alignment (MSA) on a set of input sequences and generate a consensus sequence by applying the heaviest bundling algorithm to the final alignment graph.

abPOA can generate high-quality consensus sequences from error-prone long reads and offer significant speed improvement over existing tools.

abPOA supports three alignment modes (global, local, extension) and flexible scoring schemes that allow linear, affine and convex gap penalties. It right now supports SSE2/SSE4.1/AVX2 vectorization.

This package provides the python3 module of abPOA.

Please cite: Yan Gao, Yongzhuang Liu, Yanmei Ma, Bo Liu, Yadong Wang and Yi Xing: abPOA: an SIMD-based C library for fast partial order alignment using adaptive band. Bioinformatics 37(15):2209–2211 (2021)
python3-pyani
Python3 module for average nucleotide identity analyses
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Pyani is a Python3 module and script that provides support for calculating average nucleotide identity (ANI) and related measures for whole genome comparisons, and rendering relevant graphical summary output. Where available, it takes advantage of multicore systems, and can integrate with SGE/OGE-type job schedulers for the sequence comparisons.

Please cite: Leighton Pritchard, Rachel H. Glover, Sonia Humphris, John G. Elphinstone and Ian K. Toth: Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens. (eprint) Anal. Methods 8(1):12-24 (2016)
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python3-pybedtools
enveloppe de Python 3 pour BEDTools pour des tâches de bio-informatique
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La suite BEDTools de programmes est largement utilisée pour la manipulation d’intervalle génomique ou « l’algèbre génomique ». pybedtools enveloppe et étend BEDTools et propose des manipulations au niveau fonction à partir de Python.

Il s'agit de la version en Python⋅3.

Please cite: R. K. Dale, B. S. Pedersen and A. R. Quinlan: Pybedtools: a flexible Python library for manipulating genomic datasets and annotations". Bioinformatics 27(24):3423-3424 (2011)
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python3-pybel
Biological Expression Language
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PyBEL is a pure Python package for parsing and handling biological networks encoded in the Biological Expression Language (BEL) version 2. It also facilitates data interchange between common formats and databases such as NetworkX, JSON, CSV, SIF, Cytoscape, CX, NDEx, SQL, and Neo4J.

This package installs the library for Python 3.

Please cite: Charles Tapley Hoyt, Andrej Konotopez and Christian Ebeling: PyBEL: a computational framework for Biological Expression Language. (eprint) Bioinformatics 34(4):703–704 (2018)
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python3-pybigwig
Python 3 module for quick access to bigBed and bigWig files
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This is a Python extension, written in C, for quick access to bigBed files, and access to and creation of bigWig files.

The bigWig format was originally created in the context of genome browsers. There, computing exact summary statistics for a given interval is less important than quickly being able to compute an approximate statistic. Because of this, bigWig files contain not only interval-value associations, but also sum of values/sum of squared values/minimum value/maximum value/number of bases covered for equally sized bins of various sizes. These different sizes are referred to as "zoom levels". The smallest zoom level has bins that are 16 times the mean interval size in the file and each subsequent zoom level has bins 4 times larger than the previous. This methodology is used in Kent's tools and, therefore, likely used in almost every currently existing bigWig file.

When a bigWig file is queried for a summary statistic, the size of the interval is used to determine whether to use a zoom level and, if so, which one. The optimal zoom level is that which has the largest bins no more than half the width of the desired interval. If no such zoom level exists, the original intervals are instead used for the calculation.

python3-pyfaidx
efficient random access to fasta subsequences for Python 3
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Samtools provides a function "faidx" (FAsta InDeX), which creates a small flat index file ".fai" allowing for fast random access to any subsequence in the indexed FASTA file, while loading a minimal amount of the file in to memory. This Python module implements pure Python classes for indexing, retrieval, and in-place modification of FASTA files using a samtools compatible index. The pyfaidx module is API compatible with the pygr seqdb module. A command-line script "faidx" is installed alongside the pyfaidx module, and facilitates complex manipulation of FASTA files without any programming knowledge.

This package provides the Python 3 modules to access fasta files.

Please cite: Matthew D. Shirley, Zhaorong Ma, Brent S. Pedersen and Sarah J. Wheelan: Efficient "pythonic" access to FASTA files using pyfaidx. PeerJ PrePrints 3:e1196 (2015)
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python3-pyfastx
fast random access to sequences from FASTA/Q file - python3 module
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The pyfastx is a lightweight Python C extension that enables users to randomly access to sequences from plain and gzipped FASTA/Q files. This module aims to provide simple APIs for users to extract sequence from FASTA and reads from FASTQ by identifier and index number. The pyfastx will build indexes stored in a sqlite3 database file for random access to avoid consuming excessive amount of memory. In addition, the pyfastx can parse standard (sequence is spread into multiple lines with same length) and nonstandard (sequence is spread into one or more lines with different length) FASTA format.

It features:

  • a single file for the Python extension;
  • lightweight, memory efficient FASTA/Q file parsing;
  • fast random access to sequences from gzipped FASTA/Q file;
  • sequences reading from FASTA file line by line;
  • N50 and L50 calculation of sequences in FASTA file;
  • GC content and nucleotides composition calculation;
  • reverse, complement and antisense sequences extraction;
  • excellent compatibility: support for parsing nonstandard FASTA file;
  • support for FASTQ quality score conversion;
  • a command line interface for splitting FASTA/Q file.

This package provides the python3 module.

Please cite: Lianming Du, Qin Liu, Zhenxin Fan, Jie Tang, Xiuyue Zhang, Megan Price, Bisong Yue and Kelei Zhao: Pyfastx: a robust Python package for fast random access to sequences from plain and gzipped FASTA/Q files. (PubMed) Briefings in Bioinformatics 22(4) (2021)
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python3-pymummer
Python 3 interface to MUMmer
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pymummer is a Python wrapper for running the programs of the MUMmer sequence alignment suite and parsing their output.

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python3-pyranges
2D representation of genomic intervals and their annotations
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A PyRanges object must have the columns Chromosome, Start and End. These describe the genomic position and function as implicit row labels. A Strand column is optional and adds strand information to the intervals. Any other columns are allowed and are considered metadata.

The structure can be filled from .bed, .bam or .gff files, also from tabular or textual representations.

Please cite: Endre Bakken Stovner and Pål Sætrom: PyRanges: efficient comparison of genomic intervals in Python. Bioinformatics 36(3):918–919 (2020)
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python3-pysam
interface for the SAM/BAM sequence alignment and mapping format (Python 3)
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Pysam is a Python module for reading and manipulating Samfiles. It's a lightweight wrapper of the samtools C-API. Pysam also includes an interface for tabix.

This package installs the module for Python 3.

The package is enhanced by the following packages: python-pysam-tests
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python3-pyspoa
Python bindings to spoa
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Spoa (SIMD POA) is a c++ implementation of the partial order alignment (POA) algorithm (as described in 10.1093/bioinformatics/18.3.452) which is used to generate consensus sequences (as described in 10.1093/bioinformatics/btg109). It supports three alignment modes: local (Smith-Waterman), global (Needleman-Wunsch) and semi-global alignment (overlap).

This package presents Python bindings for the spoa library

python3-pyvcf
Variant Call Format (VCF) parser for Python 3
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The Variant Call Format (VCF) specifies the format of a text file used in bioinformatics for storing gene sequence variations. The format has been developed with the advent of large-scale genotyping and DNA sequencing projects, such as the 1000 Genomes Project.

The intent of this module is to mimic the csv module in the Python stdlib, as opposed to more flexible serialization formats like JSON or YAML. vcf will attempt to parse the content of each record based on the data types specified in the meta-information lines -- specifically the ##INFO and ##FORMAT lines. If these lines are missing or incomplete, it will check against the reserved types mentioned in the spec. Failing that, it will just return strings.

This package provides the Python 3 modules.

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python3-rdkit
Collection of cheminformatics and machine-learning software
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RDKit is a Python/C++ based cheminformatics and machine-learning software environment. Features Include:

  • Chemical reaction handling and transforms
  • Substructure searching with SMARTS
  • Canonical SMILES
  • Molecule-molecule alignment
  • Large number of molecular descriptors, including topological, compositional, EState, SlogP/SMR, VSA and Feature-map vectors
  • Fragmentation using RECAP rules
  • 2D coordinate generation and depiction, including constrained depiction
  • 3D coordinate generation using geometry embedding
  • UFF and MMFF94 forcefields
  • Chirality support, including calculation of (R/S) stereochemistry codes
  • 2D pharmacophore searching
  • Fingerprinting, including Daylight-like, atom pairs, topological torsions, Morgan algorithm and MACCS keys
  • Calculation of shape similarity
  • Multi-molecule maximum common substructure
  • Machine-learning via clustering and information theory algorithms
  • Gasteiger-Marsili partial charge calculation

File formats RDKit supports include MDL Mol, PDB, SDF, TDT, SMILES and RDKit binary format.

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python3-ruffus
Python3 computation pipeline library widely used in bioinformatics
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Ruffus is designed to allow scientific and other analyses to be automated with the minimum of fuss and the least effort.

  • Lightweight: Suitable for the simplest of tasks
  • Scalable: Handles even fiendishly complicated pipelines which would cause make or scons to go cross-eyed and recursive.
  • Standard Python: No "clever magic", no pre-processing.
  • Unintrusive: Unambitious, lightweight syntax which tries to do this one small thing well.

This package provides python3 modules.

Please cite: Leo Goodstadt: Ruffus: A Lightweight Python Library for Computational Pipelines. (PubMed,eprint) Bioinformatics 26(21):2778-9 (2010)
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python3-screed
short nucleotide read sequence utils in Python 3
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Screed parses FASTA and FASTQ files, generates databases, and lets you query these databases. Values such as sequence name, sequence description, sequence quality, and the sequence itself can be retrieved from these databases.

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python3-shasta
nanopore whole genome assembly (dynamic library)
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De novo assembly from Oxford Nanopore reads. The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by Oxford Nanopore flow cells.

Computational methods used by the Shasta assembler include:

  • Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.

  • Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

Shasta assembly quality is comparable or better than assembly quality achieved by other long read assemblers.

This package contains the dynamic library that can be interfaced and imported within Python.

Please cite: K. Shafin, T. Pesout and R. Lorig-Roach et al.: Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes. Nature Biotechnology (2020)
python3-skbio
Python3 data structures, algorithms, educational resources for bioinformatic
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Scikit-bio is a Python package providing data structures, algorithms, and educational resources for bioinformatics.

This is the package for Python3

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python3-slow5
Python3 modul for reading & writing SLOW5 files
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Slow5lib is a software library for reading & writing SLOW5 files. Slow5lib is designed to facilitate use of data in SLOW5 format by third- party software packages. Existing packages that read/write data in FAST5 format can be easily modified to support SLOW5.

SLOW5 is a new file format for storing signal data from Oxford Nanopore Technologies (ONT) devices. SLOW5 was developed to overcome inherent limitations in the standard FAST5 signal data format that prevent efficient, scalable analysis and cause many headaches for developers. SLOW5 can be encoded in human-readable ASCII format, or a more compact and efficient binary format (BLOW5) - this is analogous to the seminal SAM/BAM format for storing DNA sequence alignments. The BLOW5 binary format supports zlib (DEFLATE) compression, or other compression methods, thereby minimising the data storage footprint while still permitting efficient parallel access. Detailed benchmarking experiments have shown that SLOW5 format is an order of magnitude faster and significantly smaller than FAST5.

This is the Python3 module.

python3-sqt
SeQuencing Tools for biological DNA/RNA high-throughput data
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sqt is a collection of command-line tools for working with high-throughput sequencing data. Conceptionally not fixed to use any particular language, many sqt subcommands are currently implemented in Python. For them, a Python package is available with functions for reading and writing FASTA/FASTQ files, computing alignments, quality trimming, etc.

The following tools are offered:

  • sqt-coverage -- Compute per-reference statistics such as coverage and GC content
  • sqt-fastqmod -- FASTQ modifications: shorten, subset, reverse complement, quality trimming.
  • sqt-fastastats -- Compute N50, min/max length, GC content etc. of a FASTA file
  • sqt-qualityguess -- Guess quality encoding of one or more FASTA files.
  • sqt-globalalign -- Compute a global or semiglobal alignment of two strings.
  • sqt-chars -- Count length of the first word given on the command line.
  • sqt-sam-cscq -- Add the CS and CQ tags to a SAM file with colorspace reads.
  • sqt-fastamutate -- Add substitutions and indels to sequences in a FASTA file.
  • sqt-fastaextract -- Efficiently extract one or more regions from an indexed FASTA file.
  • sqt-translate -- Replace characters in FASTA files (like the 'tr' command).
  • sqt-sam-fixn -- Replace all non-ACGT characters within reads in a SAM file.
  • sqt-sam-insertsize -- Mean and standard deviation of paired-end insert sizes.
  • sqt-sam-set-op -- Set operations (union, intersection, ...) on SAM/BAM files.
  • sqt-bam-eof -- Check for the End-Of-File marker in compressed BAM files.
  • sqt-checkfastqpe -- Check whether two FASTQ files contain correctly paired paired-end data.
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python3-streamz
build pipelines to manage continuous streams of data
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It is simple to use in simple cases, but also supports complex pipelines that involve branching, joining, flow control, feedback, back pressure, and so on. Optionally, Streamz can also work with both Pandas and cuDF dataframes, to provide sensible streaming operations on continuous tabular data.

python3-tinyalign
numerical representation of differences between strings
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A small Python module providing edit distance (aka Levenshtein distance, that is, counting insertions, deletions and substitutions) and Hamming distance computation.

Its main purpose is to speed up computation of edit distance by allowing to specify a maximum number of differences maxdiff (banding). If that parameter is provided, the returned edit distance is anly accurate up to maxdiff. That is, if the actual edit distance is higher than maxdiff, a value larger than maxdiff is returned, but not necessarily the actual edit distance.

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python3-torch
Tensors and Dynamic neural networks in Python (Python Interface)
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PyTorch is a Python package that provides two high-level features:

(1) Tensor computation (like NumPy) with strong GPU acceleration (2) Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.

This is the CPU-only version of PyTorch (Python interface).

Please cite: Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai and Soumith Chintala:
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python3-treetime
inférence de phylogénies marquées temporellement et reconstruction ancestrale – Python 3
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TreeTime fournit des routines pour la reconstruction de séquences ancestrales (ASR) et l’inférence du maximum de vraisemblance de phylogénies selon l’horloge moléculaire, c’est-à-dire un arbre où toutes les branches sont échelonnées de façon que leurs emplacements de nœuds terminaux correspondent à leurs temps d’échantillonnage et les nœuds internes sont placés selon le moment de divergence le plus probable.

TreeTime essaie de trouver un compromis entre les modèles probabilistes sophistiqués de l’évolution et les heuristiques rapides. IL met en œuvre les modèles GTR (Generalised time reversible) d’inférence ancestrale et d’optimisation de longueur de branche, mais prend la topologie de l’arbre telle que donnée. Pour optimiser la vraisemblance de phylogénies échelonnées selon le temps, treetime utilise une approche itérative qui d’abord infère les séquences ancestrales selon la longueur de branche de l’arbre, puis optimise la position des nœuds non contraints sur l’axe de temps, et puis répète ce cycle. La seule optimisation de topologie est la résolution (facultative) des polytomies de façon que cela soit le plus (approximativement) cohérent avec les contraintes d’échantillonnage de temps de l’arbre. Ce paquet est conçu pour être utilisé comme un outil autonome ou comme une bibliothèque utilisée dans une suite d’analyse plus large de phylogénie.

Caractéristiques :

 – reconstruction de séquences ancestrale (maximum de vraisemblance marginale et jointe) ;
 – inférence d’arbre selon l’horloge moléculaire (maximum de vraisemblance marginale et jointe) ;
 – inférence de modèles GTR ;
 – changement de racine pour obtenir la meilleure régression racine-extrémité ;
 – horloge moléculaire auto-corrélée souple.

Ce paquet fournit le module avec Python 3.

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python3-unifrac
high-performance phylogenetic diversity calculations
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The de facto repository for high-performance phylogenetic diversity calculations. The methods in this repository are based on an implementation of the Strided State UniFrac algorithm which is faster, and uses less memory than Fast UniFrac. Strided State UniFrac supports Unweighted UniFrac, Weighted UniFrac, Generalized UniFrac, Variance Adjusted UniFrac and meta UniFrac. This repository also includes Stacked Faith (manuscript in preparation), a method for calculating Faith's PD that is faster and uses less memory than the Fast UniFrac-based reference implementation.

This package contains the Python3 module.

Please cite: Daniel McDonald, Yoshiki Vázquez-Baeza, David Koslicki, Jason McClelland, Nicolai Reeve, Zhenjiang Xu, Antonio Gonzalez and Rob Knight: Striped UniFrac: enabling microbiome analysis at unprecedented scale. (PubMed) Nature Methods 15(11):847–848 (2018)
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python3-wdlparse
Workflow Description Language (WDL) parser for Python
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A Python package that provides the generated Hermes and Antlr4 WDL parsers for Python.

r-bioc-biobase
fonctions de base pour Bioconductor
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Biobase fait partie du projet Bioconductor et est utilisé par de nombreux autres paquets. Biobase fournit des structures normalisées pour représenter des données génomiques et des fonctions nécessaires à de nombreux autres paquets ou qui remplacent des fonctions de R.

Bioconductor est un projet pour développer des outils logiciels innovants pour être utilisés en biologie informatique. Il est basé sur le langage R. Il est nécessaire d’être déjà assez familier avec ce langage avant d’utiliser Bioconductor. Les paquets de Bioconductor fournissent des outils interactifs flexibles pour mener à bien un certain nombre de tâches computationnelle.

Please cite: Robert C Gentleman, Vincent J Carey, Douglas M Bates, Ben Bolstad, Marcel Dettling, Sandrine Dudoit, Byron Ellis, Laurent Gautier, Yongchao Ge, Jeff Gentry, Kurt Hornik, Torsten Hothorn, Wolfgang Huber, Stefano Iacus, Rafael Irizarry, Friedrich Leisch, Cheng Li, Martin Maechler, Anthony J Rossini, Gunther Sawitzki, Colin Smith, Gordon Smyth, Luke Tierney, Jean Y H Yang and Jianhua Zhang: Bioconductor: Open software development for computational biology and bioinformatics. (PubMed,eprint) Genome Biology 5(10):R80 (2004)
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Remark of Debian Med team: This is a part of Bioconductor project

A nice overview about all modules of BioDonductor is given at http://www.bioconductor.org/packages/release/bioc/

r-cran-boolnet
assembling, analyzing and visualizing Boolean networks
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BoolNet is an R package that provides tools for assembling, analyzing and visualizing synchronous and asynchronous Boolean networks as well as probabilistic Boolean networks.

Please cite: Christoph Muessel, Martin Hopfensitz and Hans A. Kestler: BoolNet -- an R package for generation, reconstruction and analysis of Boolean networks. (eprint) Bioinformatics 26(6):1378-1380 (2010)
r-cran-corrplot
Visualization of a Correlation Matrix
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A graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc.

r-cran-distory
GNU R distance between phylogenetic histories
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This GNU R package enables calculation of geodesic distance between phylogenetic trees and associated functions.

r-cran-fitdistrplus
support fit of parametric distribution
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Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available. See e.g. Casella & Berger (2002). Statistical inference. Pacific Grove.

Please cite: Marie Laure Delignette-Muller Christophe Dutang: fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software 64(4):1-34 (2015)
r-cran-forecast
GNU R forecasting functions for time series and linear models
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Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

r-cran-genetics
paquet de GNU R pour la génétique des populations
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Il s'agit de classes et méthodes pour manipuler des données génétiques. Ce paquet fournit une bibliothèque pour l'environnement de statistiques R qui contient des classes pour représenter des génotypes et haplotypes avec des marqueurs simples jusqu'à des marqueurs multiples sur plusieurs chromosomes. Les fonctions comprennent la fréquence des allèles, le marquage homo/hétérozygote, le marquage de porteurs de certains allèles, l'estimation et le test du déséquilibre de Hardy-Weinberg, l'estimation et le test du déséquilibre de liaisons, etc.

REMARQUE : CE PAQUET EST DÉSORMAIS OBSOLÈTE.

Le projet R-Genetics a développé un ensemble de paquets améliorés pour la génétique pour remplacer « genetics ». Pour plus d'information, visiter la page d'accueil du projet sur http://rgenetics.org.

r-cran-gprofiler2
Interface to the 'g:Profiler' Toolset
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A toolset for functional enrichment analysis and visualization, gene/protein/SNP identifier conversion and mapping orthologous genes across species via 'g:Profiler' (https://biit.cs.ut.ee/gprofiler). The main tools are: (1) 'g:GOSt' - functional enrichment analysis and visualization of

    gene lists;
(2) 'g:Convert' - gene/protein/transcript identifier conversion across
    various namespaces;
(3) 'g:Orth' - orthology search across species;

(4) 'g:SNPense' - mapping SNP rs identifiers to chromosome positions,

    genes and variant effects This package is an R interface
    corresponding to the 2019 update of 'g:Profiler' and provides access
    to 'g:Profiler' for versions 'e94_eg41_p11' and higher. See the
    package 'gProfileR' for accessing older versions from the
    'g:Profiler' toolset.
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r-cran-haplo.stats
paquet de GNU R pour l’analyse d’haplotypes
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Ce paquet fournit des routines pour l’environnement de statistiques de GNU R pour l’analyse statistique d’haplotypes mesurés indirectement avec traits et covariables lorsque la phase de liaison est ambigüe. Les méthodes statistiques supposent que tous les sujets n’ont aucun lien de parenté et ces haplotypes sont ambigus (à cause de la phase de liaison inconnue des marqueurs génétiques). Les principales fonctions sont haplo.em, haplo.glm, haplo.score, haplo.power et seqhap.

r-cran-metamix
GNU R bayesian mixture analysis for metagenomic community profiling
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Resolves complex metagenomic mixtures by analysing deep sequencing data, using a mixture model based approach. The use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species more likely to contribute to the mixture.

Please cite: Sofia Morfopoulou and Vincent Plagnol: Bayesian mixture analysis for metagenomic community profiling. (eprint) Bioinformatics 31(18):2930-2938 (2015)
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r-cran-phangorn
GNU R package for phylogenetic analysis
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phangorn is a tool for reconstructing phylogenies, using distance-based methods, maximum parsimony or maximum likelihood, and performing Hadamard conjugation. It also offers functions for comparing trees, phylogenetic models or splits, simulating character data and performing congruence analysis.

Please cite: K.P. Schliep: phangorn: phylogenetic analysis in R. (PubMed) Bioinformatics 27(4):592-593 (2011)
r-cran-pheatmap
GNU R package to create pretty heatmaps
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GNU R implementation of heatmaps that offers more control over dimensions and appearance.

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r-cran-phylobase
GNU R base package for phylogenetic structures and comparative data
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This R package provides a base S4 class for comparative methods, incorporating one or more trees and trait data as these are used in other packages dealing with phylogenetic structures and comparative data.

r-cran-pscbs
R package: Analysis of Parent-Specific DNA Copy Numbers
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Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome. Both tumor-normal paired and tumoronly analyses are supported.

Please cite: Adam B. Olshen, Henrik Bengtsson, Pierre Neuvial, Paul T. Spellman, Richard A. Olshen and Venkatraman E. Seshan: Parent-specific copy number in paired tumor-normal studies using circular binary segmentation. (PubMed,eprint) Bioinformatics 27(15):2038-2046 (2011)
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r-cran-qqman
R package for visualizing GWAS results using Q-Q and manhattan plots
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qqman is an add-on package for the R statistical environment. This package provides functions for visualizing Genome-Wide Association Studies (GWAS) results using Manhattan plots and Quantile-Quantile plots.

r-cran-rentrez
interface GNU R pour l’API EUtils du NCBI
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Ce paquet fournit une interface R pour l’API EUtils du NCBI qui permet une recherche dans les bases de données telles que GenBank et PubMed, de traiter le résultat des recherches et d’intégrer les données dans une session R.

r-cran-rncl
GNU R interface to the Nexus Class Library
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This R package provides an interface to the Nexus Class Library which allows parsing of NEXUS, Newick and other phylogenetic tree file formats. It provides elements of the file that can be used to build phylogenetic objects such as ape's 'phylo' or phylobase's 'phylo4(d)'.

r-cran-rnexml
GNU R package for semantically rich I/O for the 'NeXML' format
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Provides access to phyloinformatic data in 'NeXML' format. The package should add new functionality to R such as the possibility to manipulate 'NeXML' objects in more various and refined way and compatibility with 'ape' objects.

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r-cran-rotl
GNU R interface to the 'Open Tree of Life' API
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An interface to the 'Open Tree of Life' API to retrieve phylogenetic trees, information about studies used to assemble the synthetic tree, and utilities to match taxonomic names to 'Open Tree identifiers'. The 'Open Tree of Life' aims at assembling a comprehensive phylogenetic tree for all named species.

r-cran-samr
GNU R significance analysis of microarrays
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This GNU R package provides significance analysis of microarrays. A microarray is a multiplex lab-on-a-chip. It is a 2D array on a solid substrate (usually a glass slide or silicon thin-film cell) that assays large amounts of biological material using high-throughput screening miniaturized, multiplexed and parallel processing and detection methods.

This package helps analysing this kind of microarrays.

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r-cran-sctransform
Variance Stabilizing Transformations for Single Cell UMI Data
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A normalization method for single-cell UMI count data using a variance stabilizing transformation. The transformation is based on a negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction. See Hafemeister and Satija 2019 for more details.

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r-cran-seqinr
GNU R biological sequences retrieval and analysis
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Exploratory data analysis and data visualization for biological sequence (DNA and protein) data. Includes also utilities for sequence data management under the ACNUC system.

r-cran-seurat
Tools for Single Cell Genomics
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A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , and Butler A and Satija R (2017) for more details.

Please cite: Rahul Satija, Jeffrey A. Farrell, David Gennert, Alexander F. Schier and Aviv Regev: Spatial reconstruction of single-cell gene expression data. (PubMed) Nature Biotechnology 33:495–502 (2015)
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r-cran-tsne
t-distributed stochastic neighbor embedding for R (t-SNE)
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A "pure R" implementation of the t-SNE algorithm.

r-cran-vegan
Community Ecology Package for R
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R package for community ecologists. It contains most multivariate analysis needed in analysing ecological communities, and tools for diversity analysis. Most diversity methods assume that data are counts of individuals.

These tools are sometimes used outside the field of ecology, for instance to study populations of white blood cells or RNA molecules.

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r-cran-webgestaltr
find over-represented properties in gene lists
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The web version WebGestalt http://www.webgestalt.org supports 12 organisms, 354 gene identifiers and 321,251 function categories. Users can upload the data and functional categories with their own gene identifiers. In addition to the Over-Representation Analysis, WebGestalt also supports Gene Set Enrichment Analysis and Network Topology Analysis. The user-friendly output report allows interactive and efficient exploration of enrichment results. The WebGestaltR package not only supports all above functions but also can be integrated into other pipeline or simultaneously analyze multiple gene lists.

ruby-bio
Ruby tools for computational molecular biology
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BioRuby project aims to implement an integrated environment for Bioinformatics with Ruby language. Design philosophy of the BioRuby library is KISS (keep it simple, stupid) to maximize the usability and the efficiency for biologists as a daily tool. The project was started in Japan and supported by University of Tokyo (Human Genome Center), Kyoto University (Bioinformatics Center) and the Open Bio Foundation.

ruby-crb-blast
Run conditional reciprocal best blast
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CRB-BLAST is a novel method for finding orthologs between one set of sequences and another. This is particularly useful in genome and transcriptome annotation.

CRB-BLAST initially performs a standard reciprocal best BLAST. It does this by performing BLAST alignments of query->target and target->query. Reciprocal best BLAST hits are those where the best match for any given query sequence in the query->target alignment is also the best hit of the match in the reverse (target->query) alignment.

Reciprocal best BLAST is a very conservative way to assign orthologs. The main innovation in CRB-BLAST is to learn an appropriate e-value cutoff to apply to each pairwise alignment by taking into account the overall relatedness of the two datasets being compared. This is done by fitting a function to the distribution of alignment e-values over sequence lengths. The function provides the e-value cutoff for a sequence of given length.

sbmltoolbox
libsbml toolbox for octave and matlab
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The SBMLToolbox provides a set of basic functions for reading, writing, manipulating, and simulating SBML (System Biology Meta Language) models. It is a free Open Source package on top of the libSBML with full compatibility to work with MATLAB and Octave alike and share models between the two systems.

The toolbox is not a complete turn key solution for Systems Biology. It has its emphasis on easing the handling of SBML data and serves as a starting point for users and developers to establish their own methods.

Please cite: S. M. Keating, B. J. Bornstein, A. Finney and M. Hucka: SBMLToolbox: an SBML toolbox for MATLAB users.. (eprint) Bioinformatics 22(10):1275-1277 (2006)
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snakemake
pythonic workflow management system
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Build systems like GNU Make are frequently used to create complicated workflows, e.g. in bioinformatics. This project aims to reduce the complexity of creating workflows by providing a clean and modern domain specific language (DSL) in Python style, together with a fast and comfortable execution environment.

Please cite: Johannes Köster and Sven Rahmann: Snakemake-a scalable bioinformatics workflow engine. Bioinformatics (2012)
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toil
cross-platform workflow engine
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Toil is a scalable, efficient, cross-platform and easy-to-use workflow engine in pure Python. It works with several well established load balancers like Slurm or the Sun Grid Engine. Toil is also compatible with the Common Workflow Language (CWL) via the "toil-cwl-runner" interface, which this package make available via the Debian alternativess system under the alias "cwl-runner".

Please cite: John Vivian, Arjun Arkal Rao, Frank Austin Nothaft, Christopher Ketchum, Joel Armstrong, Adam Novak, Jacob Pfeil, Jake Narkizian Alden D. Deran, Audrey Musselman-Brown, Hannes Schmidt, Peter Amstutz, Brian Craft, Mary Goldman, Kate Rosenbloom, Melissa Cline, Brian O'Connor, Megan Hanna, Chet Birger, W. James Kent David A. Patterson, Anthony D. Joseph, Jingchun Zhu, Sasha Zaranek, Gad Getz, David Haussler and Benedict Paten: Toil enables reproducible, open source, big biomedical data analyses. Nature Biotechnology 35(4):314–316 (2017)
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Official Debian packages with lower relevance

capsule-nextflow
packaging and deployment tool for Java applications
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A capsule is a single executable JAR that contains everything an application needs to run either in the form of embedded files or as declarative metadata. It can contain JAR artifacts, dependencies and resources, native libraries, the required Java Runtime Environment version, the Java Virtual Machine flags required to run the application well, Java or native agents and more. In short, a capsule is a self-contained JAR that knows everything there is to know about how to run the application the way it is meant to run.

One way of thinking about a capsule is as a fat JAR on steroids (that also allows native libraries and never interferes with your dependencies) and a declarative startup script rolled into one; another, is to see it is as the deploy-time counterpart to your build tool. Just as a build tool manages your build, Capsule manages the launching of your application.

This package contains a fork of the original capsule project. This fork is suited as a dependency of nextflow.

conda-package-handling
création et extraction de paquets conda de différents formats
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Cph est une abstraction de « conda package handling » et un outil pour extraire, créer et convertir des paquets de différents formats.

Au moment de cette écriture, le format standard de conda est un fichier .tar.bz2. Conda a été entretenu depuis longtemps, merci aux nombreuses personnes qui utilisent les anciennes versions de conda. Un nouveau format de conda, décrit sur https://docs.google.com/document/d/1HGKsbg_j69rKXP- ihhpCb1kNQSE8Iy3yOsUU2x68x8uw/edit?usp=sharing, existe. Ce nouveau format est conçu pour permettre un accès beaucoup plus rapide aux métadonnées et utilise des algorithmes de compression plus modernes, tout en facilitant la signature de paquet sans ajout de fichiers auxiliaires.

ctdconverter
Convert CTD files into Galaxy tool and CWL CommandLineTool files
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Common Tool Descriptors (CTDs) are XML documents that represent the inputs, outputs, parameters of command line tools in a platform-independent way.

CTDConverter, given one or more Common Tool Descriptors (CTD) XML files, generates Galaxy tool wrappers and Common Workflow Language (CWL) Command Line Tool v1.0 standard descriptions from CTD files.

cthreadpool-dev
minimal ANSI C thread pool - development files
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These are C development files for the C-Thread-Pool library. This is a minimal but advanced threadpool implementation.

  • ANCI C and POSIX compliant
  • Pause/resume/wait as you like
  • Simple easy-to-digest API
  • Well tested This software does not ship as a shared library since it is very small and there is a technical difficulty with this implementation.
cwlformat
code formatter for Common Workflow Language
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CWL Format is a specification and a reference implementation for a very opinionated CWL code formatter.

It outputs Common Workflow Language(CWL) in a standardized YAML format. It has no settings or options because you have better things to do with your time. And because CWL Format is always correct.

cwltest
Common Workflow Language testing framework
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This is a testing tool for checking the output of Tools and Workflows described with the Common Workflow Language. Among other uses, it is used to run the CWL conformance tests.

Please cite: Peter Amstutz, Michael R. Crusoe, Nebojša Tijanić, Brad Chapman, John Chilton, Michael Heuer, Andrey Kartashov, Dan Leehr, Hervé Ménager, Maya Nedeljkovich, Matt Scales, Stian Soiland-Reyes and Luka Stojanovic: Common Workflow Language, v1.0. (2016)
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libargs-dev
simple header-only C++ argument parser library
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Args is a simple, small, flexible, header-only C++ argument parssing library.

This is designed to appear somewhat similar to Python's argparse, but in C++, with static type checking, and hopefully a lot faster (also allowing fully nestable group logic, where Python's argparse does not).

libbam-dev
manipulation d'alignements de séquences de nucléotides au format BAM ou SAM
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La bibliothèque BAM fournit les entrées et sorties et diverses opérations de manipulation d'alignements de séquences de nucléotides au format BAM (Binary Alignment/Mapping) ou SAM (Sequence Alignment/Map). Elle gère actuellement l'importation à partir de et l'exportation vers SAM, le tri, la fusion, la génération au format «⋅pileup⋅» et la récupération rapide de lectures chevauchantes dans une région spécifique.

Cette bibliothèque fait partie de SAMtools version⋅0.1.19. Elle est obsolète et n'est fournie que pour la construction de logiciels qui n'ont pas encore fait leur transition vers HTSlib qui remplace cette bibliothèque.

Please cite: Heng Li, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth, Goncalo Abecasis, Richard Durbin and 1000 Genome Project Data Processing Subgroup: The Sequence Alignment/Map (SAM) Format and SAMtools. (PubMed,eprint) Bioinformatics 25(16):2078-2079 (2009)
libbbhash-dev
bloom-filter based minimal perfect hash function library
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BBHash is a simple library for building minimal perfect hash function. It is designed to handle large scale datasets. The function is just a little bit larger than other state-of-the-art libraries, it takes approximately 3 bits / elements (compared to 2.62 bits/elem for the emphf lib), but construction is faster and does not require additional memory.

Please cite: Antoine Limasset, Guillaume Rizk, Rayan Chikhi and Pierre Peterlongo: Fast and scalable minimal perfect hashing for massive key sets. HAL-Inria (2017)
libbifrost-dev
static library and header files for libbifrost
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Libbifrost-dev is the development library for the command-line tool bifrost for sequencing that features a broad range of functions, such as indexing, editing, and querying the graph, and includes a graph coloring method that maps each k-mer of the graph to the genomes it occurs in.

  • Build, index, color and query the compacted de Bruijn graph
  • No need to build the uncompacted de Bruijn graph
  • Reads or assembled genomes as input
  • Output graph in GFA (can be visualized with Bandage), FASTA or binary
  • Graph cleaning: short tip clipping, etc.
  • Multi-threaded
  • No parameters to estimate with other tools
  • Exact or approximate k-mer search of queries
Please cite: Guillaume Holley and Páll Melsted: Bifrost – Highly parallel construction and indexing of colored and compacted de Bruijn graphs. (PubMed,eprint) bioRxiv 21(1):249 (2020)
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libbiojava4-java
API Java pour des applications et des données biologiques –⋅version⋅par défaut
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BioJava est un projet à code source ouvert voulu pour fournir un cadre applicatif Java pour le traitement de données biologiques. Il inclut des objets pour la manipulation de séquences, l'analyse de fichiers, la gestion de serveur, l'accès aux bases de données BioSQL et Ensembl, ainsi que des routines puissantes d'analyse et de statistique comprenant une boîte à outils de programmation dynamique.

Biojava est fourni par une communauté animée qui se réunit chaque année à la «⋅Bioinformatics Open Source Conference⋅» (BOSC) qui traditionnellement accompagne le congrés «⋅Intelligent Systems in Molecular Biology⋅» (ISMB). Tout à fait comme pour BioPerl, l'emploi de cette bibliothèque est profitable pour tous les gens actifs dans la discipline, parce que beaucoup de trucs du métier s'apprennent juste en communiquant sur la liste de diffusion.

Il s'agit d'un paquet d'enveloppe qui peut permettre des mises à niveaux fluides vers de nouvelles versions.

libbiosoup-dev
C++ header-only support library for bioinformatics tools
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Biosoup is a c++ collection of header only data structures used for storage and logging in bioinformatics tools.

libbtllib-dev
Bioinformatics Technology Lab common code library
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Bioinformatics Technology Lab common code library in C++ with Python wrappers.

This package contains the header files and the static library.

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libcapsule-maven-nextflow-java
packaging tool for Java applications with Maven coordinates
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A capsule is a single executable JAR that contains everything an application needs to run either in the form of embedded files or as declarative metadata. Maven Capsule is a capsule that allows the creations of capsules that, instead of embedding their dependencies, download all or some of them from a Maven repository. The dependencies are downloaded, cached locally, and shared among other capsules that also depend on them. In addition, this capsule allows specifying capsule metadata in a POM file in addition to the manifest.

A capsule with the Maven caplet that has all (or almost all) of its dependencies downloaded rather than embedded is known as a "thin" capsule (as opposed to a "fat" capsule, which embeds all of its dependencies). In fact, a capsule may not contain any of the application's classes/JARs at all. Instead, the capsule's manifest may contain these attributes alone (and no files in the capsule JAR besides the manifest). When the capsule is launched, the newest available version of the application will be downloaded, cached and launched.

This package contains a fork of the original capsule-maven project. This fork is suited as a dependency of nextflow.

libconcurrentqueue-dev
industrial-strength lock-free queue for C++
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Features

  • Knock-your-socks-off blazing fast performance.
  • Single-header implementation. Just drop it in your project.
  • Fully thread-safe lock-free queue. Use concurrently from any number of threads.
  • C++11 implementation -- elements are moved (instead of copied) where possible.
  • Templated, obviating the need to deal exclusively with pointers -- memory is managed for you.
  • No artificial limitations on element types or maximum count. Memory can be allocated once up-front, or dynamically as needed.
  • Fully portable (no assembly; all is done through standard C++11 primitives).
  • Supports super-fast bulk operations.
  • Includes a low-overhead blocking version (BlockingConcurrentQueue).
  • Exception safe.

Reasons to use

There are not that many full-fledged lock-free queues for C++. Boost has one, but it's limited to objects with trivial assignment operators and trivial destructors, for example. Intel's TBB queue isn't lock-free, and requires trivial constructors too. There're many academic papers that implement lock-free queues in C++, but usable source code is hard to find, and tests even more so.

This queue not only has less limitations than others (for the most part), but it's also faster. It's been fairly well-tested, and offers advanced features like bulk enqueueing/dequeueing (which, with the new design, is much faster than one element at a time, approaching and even surpassing the speed of a non-concurrent queue even under heavy contention).

libdisorder-dev
library for entropy measurement of byte streams (devel)
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This library provides a function to calculate the Shannon index (H) of byte streams.

This is the development package containing the statically linked library and the header files.

libfreecontact-dev
fast protein contact predictor library - development files
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FreeContact is a protein residue contact predictor optimized for speed. Its input is a multiple sequence alignment. FreeContact can function as an accelerated drop-in for the published contact predictors EVfold-mfDCA of DS. Marks (2011) and PSICOV of D. Jones (2011).

FreeContact is accelerated by a combination of vector instructions, multiple threads, and faster implementation of key parts. Depending on the alignment, 8-fold or higher speedups are possible.

A sufficiently large alignment is required for meaningful results. As a minimum, an alignment with an effective (after-weighting) sequence count bigger than the length of the query sequence should be used. Alignments with tens of thousands of (effective) sequences are considered good input.

jackhmmer(1) from the hmmer package, or hhblits(1) from hhsuite can be used to generate the alignments, for example.

This package contains files necessary for developing applications with libfreecontact.

Please cite: László Kaján, Thomas A. Hopf, Matúš Kalaš, Debora S. Marks and Burkhard Rost: FreeContact: fast and free software for protein contact prediction from residue co-evolution. BMC Bioinformatics (2014)
libfreecontact-doc
documentation for libfreecontact
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FreeContact is a protein residue contact predictor optimized for speed. Its input is a multiple sequence alignment. FreeContact can function as an accelerated drop-in for the published contact predictors EVfold-mfDCA of DS. Marks (2011) and PSICOV of D. Jones (2011).

FreeContact is accelerated by a combination of vector instructions, multiple threads, and faster implementation of key parts. Depending on the alignment, 8-fold or higher speedups are possible.

A sufficiently large alignment is required for meaningful results. As a minimum, an alignment with an effective (after-weighting) sequence count bigger than the length of the query sequence should be used. Alignments with tens of thousands of (effective) sequences are considered good input.

jackhmmer(1) from the hmmer package, or hhblits(1) from hhsuite can be used to generate the alignments, for example.

This package contains HTML documentation for libfreecontact.

Please cite: László Kaján, Thomas A. Hopf, Matúš Kalaš, Debora S. Marks and Burkhard Rost: FreeContact: fast and free software for protein contact prediction from residue co-evolution. BMC Bioinformatics (2014)
libfreecontact-perl
fast protein contact predictor - binding for Perl
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FreeContact is a protein residue contact predictor optimized for speed. Its input is a multiple sequence alignment. FreeContact can function as an accelerated drop-in for the published contact predictors EVfold-mfDCA of DS. Marks (2011) and PSICOV of D. Jones (2011).

FreeContact is accelerated by a combination of vector instructions, multiple threads, and faster implementation of key parts. Depending on the alignment, 8-fold or higher speedups are possible.

A sufficiently large alignment is required for meaningful results. As a minimum, an alignment with an effective (after-weighting) sequence count bigger than the length of the query sequence should be used. Alignments with tens of thousands of (effective) sequences are considered good input.

jackhmmer(1) from the hmmer package, or hhblits(1) from hhsuite can be used to generate the alignments, for example.

This package contains the Perl binding.

Please cite: László Kaján, Thomas A. Hopf, Matúš Kalaš, Debora S. Marks and Burkhard Rost: FreeContact: fast and free software for protein contact prediction from residue co-evolution.. (PubMed,eprint) BMC Bioinformatics 15:85 (2014)
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libgatk-bwamem-java
interface to call Heng Li's bwa mem aligner from Java code
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BWA (Burrows-Wheeler Aligner) is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome. It is written in C.

gatk-bwamem provides a Java library and a shared library to allow one to use BWA from Java code.

This package contains the Java library.

libgatk-bwamem-jni
interface to call Heng Li's bwa mem aligner from Java code (jni)
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BWA (Burrows-Wheeler Aligner) is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome. It is written in C.

gatk-bwamem provides a Java library and a shared library to allow one to use BWA from Java code.

This package contains the native library.

libgatk-fermilite-java
interface to call Heng Li's fermi-lite assembler from Java code
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Fml-asm (fermi-lite assembler) is a command-line tool for assembling Illumina short reads in regions from 100bp to 10 million bp in size, based on the fermi-lite library.

gatk-fermilite provides a Java library and a shared library to allow one to use fermilite from Java code.

This package contains the Java library.

libgatk-fermilite-jni
interface to call Heng Li's fermi-lite assembler from Java code (jni)
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Fml-asm (fermi-lite assembler) is a command-line tool for assembling Illumina short reads in regions from 100bp to 10 million bp in size, based on the fermi-lite library.

gatk-fermilite provides a Java library and a shared library to allow one to use fermilite from Java code.

This package contains the JNI.

libgatk-native-bindings-java
library of native bindings for gatk and picard-tools
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Utilitary library for gatk and picard-tools, bringing in pairhmm and smithwaterman classes.

libgenomicsdb-dev
sparse array storage library for genomics (development files)
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GenomicsDB is built on top of a htslib fork and an internal array storage system for importing, querying and transforming variant data. Variant data is sparse by nature (sparse relative to the whole genome) and using sparse array data stores is a perfect fit for storing such data.

The GenomicsDB stores variant data in a 2D array where:

  • Each column corresponds to a genomic position (chromosome + position);
  • Each row corresponds to a sample in a VCF (or CallSet in the GA4GH terminology);
  • Each cell contains data for a given sample/CallSet at a given position; data is stored in the form of cell attributes;
  • Cells are stored in column major order - this makes accessing cells with the same column index (i.e. data for a given genomic position over all samples) fast.
  • Variant interval/gVCF interval data is stored in a cell at the start of the interval. The END is stored as a cell attribute. For variant intervals (such as deletions and gVCF REF blocks), an additional cell is stored at the END value of the variant interval. When queried for a given genomic position, the query library performs an efficient sweep to determine all intervals that intersect with the queried position.

This package contains the development files and the static library.

libgenomicsdb-java
sparse array storage library for genomics (Java library)
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GenomicsDB is built on top of a htslib fork and an internal array storage system for importing, querying and transforming variant data. Variant data is sparse by nature (sparse relative to the whole genome) and using sparse array data stores is a perfect fit for storing such data.

The GenomicsDB stores variant data in a 2D array where:

  • Each column corresponds to a genomic position (chromosome + position);
  • Each row corresponds to a sample in a VCF (or CallSet in the GA4GH terminology);
  • Each cell contains data for a given sample/CallSet at a given position; data is stored in the form of cell attributes;
  • Cells are stored in column major order - this makes accessing cells with the same column index (i.e. data for a given genomic position over all samples) fast.
  • Variant interval/gVCF interval data is stored in a cell at the start of the interval. The END is stored as a cell attribute. For variant intervals (such as deletions and gVCF REF blocks), an additional cell is stored at the END value of the variant interval. When queried for a given genomic position, the query library performs an efficient sweep to determine all intervals that intersect with the queried position.

This package contains the Java library.

libicb-utils-java
Java library of utilities to manage files and compute statistics
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icb-utils is a group of tools originally designed by the Campagne laboratory for computational biomedicine software. They include extensions of standard Java to manage io, extended iterator classes, hashtables, network-related classes, as well as a set of classes allowing for the computation of statistics.

libmaus2-dev
collection of data structures and algorithms for biobambam (devel)
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Libmaus2 is a collection of data structures and algorithms. It contains

  • I/O classes (single byte and UTF-8)
  • bitio classes (input, output and various forms of bit level manipulation)
  • text indexing classes (suffix and LCP array, fulltext and minute (FM), ...)
  • BAM sequence alignment files input/output (simple and collating)

and many lower level support classes.

This package contains header files and static libraries.

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libmilib-java
library for Next Generation Sequencing (NGS) data processing
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A helping Java package adopted by a range of Open Source tools for the analysis of B and T cell repertoires.

libminimap-dev
development headers for libminimap
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Minimap is an experimental tool to efficiently find multiple approximate mapping positions between two sets of long sequences, such as between DNA reads and reference genomes, between genomes and between long noisy reads.

This package contains the C library headers for using minimap in custom tools, along with a static library.

Please cite: Heng Li: Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. (eprint) Bioinformatics :2103-2110 (2016)
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libmodhmm-dev
library for constructing, training and scoring hidden Markov models (dev)
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Library for constructing, training and scoring hidden Markov models. It is provided with PSORTb but might be used separately.

PSORTb enables prediction of bacterial protein subcellular localization (SCL) and provides a quick and inexpensive means for gaining insight into protein function, verifying experimental results, annotating newly sequenced bacterial genomes, detecting potential cell surface/secreted drug targets, as well as identifying biomarkers for microbes.

This library needed by PSORTb is distributed separately by upstream.

This package contains the static library which is needed to link PSORTb.

libpbcopper-dev
data structures, algorithms, and utilities for C++ applications -- header files
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pbcopper provides general tools for C++ applications. It is developed for use by applications of the Pacific Biosciences SMRT Analysis suite.

This package contains the header files and static library

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librostlab-blast-doc
very fast C++ library for parsing the output of NCBI BLAST programs (doc)
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This package provides a very fast library for parsing the default output of NCBI BLAST programs into a C++ structure.

libzerg is faster, but it provides only lexing (i.e. it only returns pairs of token identifiers and token string values). librostlab-blast uses a parser generated with bison on top of a flex-generated lexer very similar to that of libzerg.

This package contains html and pdf documentation.

librostlab-doc
C++ library for computational biology (documentation)
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This library was developed by the Rost Lab. The lab's research is driven by a conviction that protein and DNA sequences encode a significant core of information about the ultimate structure and function of genetic material and its gene products.

The library provides the following facilities:

  • current working directory resource
  • exception with stack backtrace
  • file lock resource
  • passwd and group structures for C++
  • effective uid and gid resource
  • rostlab::bio::seq class with stream input operator for FASTA format
  • umask resource

This package contains html documentation.

libsavvy-dev
C++ interface for the SAV file format
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Savvy is the official C++ interface for the SAV file format and offers seamless support for BCF and VCF files.

This package contains the header files for development.

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libsuma-dev
headers and static library for sumatra and sumaclust
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Sumatra is a tool for fast and exact comparison and clustering of sequences and sumaclust can be used for fast and exact clustering of genomic sequences. Both tools are using this common library.

This package provides the static library and header files.

libsvmloc-dev
PSORTb adapted library for svm machine-learning library (dev)
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Libsvm is a machine-learning library which is an easy-to-use package for support vector classification, regression and one-class SVM. It supports multi-class classification, probability outputs, and parameter selection.

PSORTb was featuring a code copy plus some local additions. This library is linked against the Debian packaged libsvn and just contains the PSORTb extensions.

PSORTb enables prediction of bacterial protein subcellular localization (SCL) and provides a quick and inexpensive means for gaining insight into protein function, verifying experimental results, annotating newly sequenced bacterial genomes, detecting potential cell surface/secreted drug targets, as well as identifying biomarkers for microbes.

This library needed by PSORTb is distributed separately by upstream.

This package contains the static library which is needed to link PSORTb.

libterraces-dev
enumerate terraces in phylogenetic tree space (development lib)
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Terraphast takes a .nkw file in Newick format and a genes/sites file, which denotes whether (1) or not (0) gene i is present in species j.

Program output states some imput data properties, the species whose leaf edge is used as a new tree root, and the resulting supertree in compressed newick format.

This package contains a library to use the terraphast algorithm in own projects.

Please cite: Michael J. Sanderson, Michelle M. McMahon and Mike Steel: Terraces in phylogenetic tree space. (PubMed) Science 333(6041):448-450 (2011)
libtfbs-perl
scanning DNA sequence with a position weight matrix
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The TFBS perl modules comprise a set of routines to interact with the Transfac and Jaspar databases that describe a special family of proteins, the transcription factors. These bind to genomic DNA to initiate (or prevent) the readout of a gene. Once multiple binding sites are known for a transcription factor, these are gathered in a single file and are aligned in order to find position-specific characteristica that might be used to predict such binding events in novel DNA sequences.

If you use TFBS in your work, please cite "Lenhard B., Wasserman W.W. (2002) TFBS: Computational framework for transcription factor binding site analysis. Bioinformatics 18:1135-1136".

Note: the TFBS perl module is no longer under active development. All the functionality can be found in the TFBSTools Bioconductor package; users are highly encouraged to switch. http://bioconductor.org/packages/TFBSTools/

libvbz-hdf-plugin-dev
VBZ compression plugin for nanopore signal data (devel)
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VBZ Compression uses variable byte integer encoding to compress nanopore signal data.

The performance of VBZ is achieved by taking advantage of the properties of the raw signal and therefore is most effective when applied to the signal dataset. Other datasets you may have in your Fast5 files will not be able to take advantage of the default VBZ settings for compression. VBZ will be used as the default compression scheme in a future release of MinKNOW.

This package contains the header files.

libxxsds-dynamic-dev
succinct and compressed fully-dynamic data structures library
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This library offers space- and time-efficient implementations of some basic succinct/compressed dynamic data structures. It only ships header files, i.e. is inclusion only.

DYNAMIC features:

  • A succinct Searchable Partial Sums with Indels (SPSI) structure representing a list of integers s_1, s_2, ..., s_m. Space: about 1.2 * m * ( log(M/m) + log log m ) bits, where M = m + s_1 + s_2 + ... + s_m. The structure supports also update operations (i.e. s_i = s_i + delta).
  • A Succinct dynamic bitvector supporting rank/select/access/Indel (RSAI) operations. Space: about 1.2 * n bits.
  • A gap-compressed dynamic bitvector supporting rank/select/access/Indel operations. Space: about 1.2 * b * ( log(n/b) + log log b ) bits, b being the number of bits set and n being the bitvector length. All operations take log(b) time.
  • A dynamic sparse vector (of integers) with access/Indel operations.
  • A dynamic string supporting rank/select/access/Indel operations. The user can choose at construction time between fixed-length/gamma/Huffman encoding of the alphabet. All operations take log(n) * log(sigma) time (or log(n) * H0 with Huffman encoding).
  • A run-length encoded dynamic string supporting rank/select/access/insert operations (removes are not yet implemented). Space: approximately R*(1.2 * log(sigma) + 2.4 * (log(n/R)+log log R) ) bits, where R is the number of runs in the string. All operations take log(R) time.
  • A dynamic (left-extend only) entropy/run-length compressed BWT
  • A dynamic (left-extend only) entropy/run-length compressed FM-index. This structure consists in the above BWT + a dynamic suffix array sampling

Algorithms

  • Two algorithms to build LZ77 in repetition-aware RAM working space. Both algorithms use a run-length encoded BWT with sparse Suffix array sampling. The first algorithm stores 2 SA samples per BWT run. The second algorithm (much more space efficient) stores 1 SA sample per LZ factor. From the papers "Computing LZ77 in Run-Compressed Space", Alberto Policriti and Nicola Prezza, DCC2016 and " LZ77 Computation Based on the Run-Length Encoded BWT", Alberto Policriti and Nicola Prezza (Algorithmica)
  • An algorithm to build the BWT in run-compressed space
  • An algorithm to build LZ77 in nH0(2+o(1)) space and n * log n * H0 time. From the paper "Fast Online Lempel-Ziv Factorization in Compressed Space", Alberto Policriti and Nicola Prezza, SPIRE2015
  • An algorithm to build the BWT in high-order compressed space. The algorithm runs in O(n * H_k * log log n) average-case time (e.g. good for DNA) and O(n * H_k * log n) worst-case time. From the paper "Average linear time and compressed space construction of the Burrows-Wheeler transform" Policriti A., Gigante N. and Prezza N., LATA 2015 (the paper discusses a theoretically faster variant)

The SPSI structure is the building block on which all other structures are based. This structure is implemented with cache-efficient B-trees.

python-biopython-doc
documentation pour la bibliothèque Biopython
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Il s'agit de la documentation et d'exemples pour l'utilisation de la bibliothèque Biopython.

Ce paquet fournit aussi les tests unitaires de la suite de tests pour permettre la reproduction des résultats de test.

Please cite: Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski and Michiel J. L. de Hoon: Biopython: freely available Python tools for computational molecular biology and bioinformatics. (PubMed,eprint) Bioinformatics 25(11):1422-1423 (2009)
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python3-alignlib
edit and Hamming distances for biological sequences
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A small Python module providing edit distance and Hamming distance computation. It is a dependency for the IgDiscover package and likely future others.

python3-bel-resources
Python3 utilities for BEL resource files
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This package provides a Python3 interface and utilities for BEL resource files.

python3-bioblend
CloudMan and Galaxy API library (Python 3)
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BioBlend is a Python library for interacting with CloudMan and Galaxy's API. BioBlend is supported and tested on:

 · Python 2.6, 2.7, 3.3 and 3.4
 · Galaxy release_14.02 and later.
Conceptually, it makes it possible to script and automate  the  process

of cloud infrastructure provisioning and scaling via CloudMan, and run‐ ning of analyses via Galaxy. In reality, it makes it possible to do things like this:

 · Create  a CloudMan compute cluster, via an API and directly from your
   local machine:
 · Reconnect to an existing CloudMan instance and manipulate it
 · Interact with Galaxy via a straightforward API

Although this library allows you to blend these two services into a cohesive unit, the library itself can be used with either service irrespective of the other. For example, you can use it to just manipulate CloudMan clusters or to script the interactions with an instance of Galaxy running on your laptop.

This package installs the library for Python 3.

python3-biopython-sql
prise en charge par Biopython de la base de données BioSQL –⋅Python⋅3
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Il s’agit de l’interface Biopython pour la base de données BioSQL (consulter www.biosql.org pour plus de détails). BioPerl, BioJava et BioRuby fournissent aussi leurs propres interfaces BioSQL en partageant le même schéma SQL.

Please cite: Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski and Michiel J. L. de Hoon: Biopython: freely available Python tools for computational molecular biology and bioinformatics. (PubMed,eprint) Bioinformatics 25(11):1422-1423 (2009)
Registry entries: Bio.tools  SciCrunch 
python3-cgelib
Python3 code to be utilized across the CGE tools
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This package will in time replace the cgecore package. The package contains classes and functions intended to be utilized across the tools provide by the Center for Genomic Epidemiology. It is for instance needed by resfinder package.

python3-conda-package-streaming
fetch conda metadata
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Download conda metadata from packages without transferring entire file. Get metadata from local .tar.bz2 packages without reading entire files.

python3-ctdopts
Gives your Python tools a CTD-compatible interface
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Common Tool Descriptors (CTDs) are XML documents that represent the inputs, outputs, parameters of command line tools in a platform-independent way.

CTDopts is a module for enabling tools with CTD reading/writing, argument parsing, validating and manipulating capabilities.

python3-intake
lightweight package for finding and investigating data
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Intake is a lightweight set of tools for loading and sharing data in data science projects. Intake helps you:

 1. Load data from a variety of formats into containers you already know,
    like Pandas dataframes, Python lists, NumPy arrays and more.
 2. Convert boilerplate data loading code into reusable intake plugins.
 3. Describe data sets in catalog files for easy reuse and sharing
    between projects and with others.
 4. Share catalog information (and data sets) over the network with the
    Intake server.
python3-joypy
ridgeline-/joyplots plotting routine
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JoyPy is a one-function Python package based on matplotlib + pandas with a single purpose: drawing joyplots (a.k.a. ridgeline plots). Joyplots are stacked, partially overlapping density plots. They are a nice way to plot data to visually compare distributions, especially those that change across one dimension (e.g., over time). Though hardly a new technique, they have become very popular lately thanks to the R packages ggridges and ggjoy.

python3-ncls
datastructure for interval overlap queries
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The Nested Containment List is a datastructure for interval overlap queries, like the interval tree. It is usually an order of magnitude faster than the interval tree both for building and query lookups.

The implementation here is a revived version of the one used in the now defunct PyGr library, which died of bitrot. It was now made less memory-consuming and wrapper functions allow batch-querying the NCLS for further speed gains.

This package was implemented to be the cornerstone of the PyRanges project, but was made available to the Python community as a stand-alone library.

Please cite: Endre Bakken Stovner and Pål Sætrom: PyRanges: efficient comparison of genomic intervals in Python. Bioinformatics 36(3):918–919 (2020)
python3-networkx
tool to create, manipulate and study complex networks (Python3)
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NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph it's meant a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network it's usually meant a graph with weights (fields, properties) on nodes and/or edges.

The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists.

This package contains the Python 3 version of NetworkX.

python3-pycosat
Python bindings to picosat
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PicoSAT is a popular SAT solver written by Armin Biere in pure C. This package provides efficient Python bindings to picosat on the C level, i.e. when importing pycosat, the picosat solver becomes part of the Python process itself.

python3-pyflow
lightweight parallel task engine for Python
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pyFlow is a tool to manage tasks in the context of a task dependency graph. It has some similarities to make. pyFlow is not a program – it is a Python module, and workflows are defined using pyFlow by writing regular Python code with the pyFlow API.

q2-alignment
QIIME 2 plugin for generating and manipulating alignments
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-cutadapt
QIIME 2 plugin to work with adapters in sequence data
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-dada2
QIIME 2 plugin to work with adapters in sequence data
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

This package wraps the dada2 R package in BioConductor for modeling and correcting Illumina-sequenced amplicon errors. This was shown to improve the sensitivity of diversity analyses.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-demux
QIIME 2 plugin for demultiplexing of sequence reads
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-emperor
QIIME2 plugin for display of ordination plots
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-feature-classifier
classification taxinomique gérant le greffon QIIME 2
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QIIME 2 est un paquet pour l’analyse de microbiome, extensible et décentralisée avec une attention particulière sur la transparence des données et analyses. QIIME 2 permet aux chercheurs de commencer une analyse avec des données de séquences d’ADN et de terminer avec des figures et des résultats statistiques de qualité professionnelle. Principales caractéristiques :

 — suivi intégré et automatique de la provenance des données ;
 — système de type sémantique ;
 — système de greffons pour étendre les fonctionnalités d’analyse de
   microbiome ;
 — prise en charge de plusieurs types d’interface (par exemple, API,
   ligne de commande, graphique) ;

QIIME 2 est une nouvelle conception et une réécriture de la tuyauterie d’analyse de microbiome, QIIME 1. QIIME 2 corrigera la plupart des limitations de QIIME 1, tout en conservant les fonctions qui font de QIIME 1 une tuyauterie d’analyse puissante et largement utilisée.

QIIME 2 actuellement prend en charge une première tuyauterie d’analyse de microbiome de bout en bout. De nouvelles fonctionnalités seront régulièrement disponibles à travers des greffons de QIIME 2. Une liste de greffons peut être consultée sur la page de disponibilité de greffons de QIIME 2. La page des futurs greffons liste les greffons en cours de développement.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
Registry entries: Bio.tools 
q2-feature-table
QIIME 2 plugin supporting operations on feature tables
Versions of package q2-feature-table
ReleaseVersionArchitectures
sid2024.2.0+dfsg-1all
bookworm2022.11.1+dfsg-2all
trixie2023.9.0+dfsg-1all
bullseye2020.11.1+dfsg-1all
upstream2024.2.2
Popcon: 35 users (3 upd.)*
Newer upstream!
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-fragment-insertion
QIIME 2 plugin for fragment insertion
Versions of package q2-fragment-insertion
ReleaseVersionArchitectures
sid2024.2.0-1amd64,arm64,mips64el,ppc64el,riscv64
trixie2023.9.0-1amd64,arm64,mips64el,ppc64el
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Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-metadata
QIIME 2 plugin for working with and visualizing Metadata
Versions of package q2-metadata
ReleaseVersionArchitectures
trixie2023.9.0+dfsg-1amd64,arm64,mips64el,ppc64el
bookworm2022.8.0-1amd64,arm64,mips64el,ppc64el
sid2024.2.0+dfsg-1amd64,arm64,mips64el,ppc64el,riscv64
bullseye2020.11.1+dfsg-1amd64,arm64,mips64el,ppc64el
Popcon: 35 users (3 upd.)*
Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-phylogeny
QIIME 2 plugin for phylogeny
Versions of package q2-phylogeny
ReleaseVersionArchitectures
sid2024.2.0-1amd64
experimental2022.11.1-1all
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License: DFSG free
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QIIME 2 plugin for phylogenetic reconstruction, and operations on phylogenetic trees.

q2-quality-control
QIIME 2 plugin for quality assurance of feature and sequence data
Versions of package q2-quality-control
ReleaseVersionArchitectures
sid2024.2.0-2all
bullseye2020.11.1-3all
bookworm2022.11.1-2all
Popcon: 36 users (1 upd.)*
Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-quality-filter
QIIME2 plugin for PHRED-based filtering and trimming
Versions of package q2-quality-filter
ReleaseVersionArchitectures
sid2024.2.0-1all
bookworm2022.11.1-2all
trixie2023.9.0-1all
bullseye2020.11.1-2all
Popcon: 35 users (3 upd.)*
Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-sample-classifier
QIIME 2 plugin for machine learning prediction of sample data
Versions of package q2-sample-classifier
ReleaseVersionArchitectures
trixie2023.9.0-1all
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sid2024.2.0-1all
bullseye2020.11.1-3all
Popcon: 32 users (1 upd.)*
Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Microbiome studies often aim to predict outcomes or differentiate samples based on their microbial compositions, tasks that can be efficiently performed by supervised learning methods. The q2-sample-classifier plugin makes these methods more accessible, reproducible, and interpretable to a broad audience of microbiologists, clinicians, and others who wish to utilize supervised learning methods for predicting sample characteristics based on microbiome composition or other "omics" data

Registry entries: Bio.tools 
q2-taxa
QIIME 2 plugin for working with feature taxonomy annotations
Versions of package q2-taxa
ReleaseVersionArchitectures
sid2024.2.0+dfsg-1all
bullseye2020.11.1+dfsg-2all
trixie2024.2.0+dfsg-1all
bookworm2022.11.1+dfsg-2all
Popcon: 35 users (2 upd.)*
Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-types
QIIME 2 plugin defining types for microbiome analysis
Versions of package q2-types
ReleaseVersionArchitectures
bullseye2020.11.1-1all
bookworm2022.11.1-2all
trixie2024.2.0-1all
sid2024.2.0-1all
Popcon: 37 users (3 upd.)*
Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2cli
Click-based command line interface for QIIME 2
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2templates
Design template package for QIIME 2 Plugins
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

This package provides templates for QIIME 2 plugins.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
qiime
QIIME, Quantitative Insights Into Microbial Ecology
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Les microbes sont omniprésents autour des humains, des animaux, des plantes et de tous leurs parasites, avec un forte interaction sur eux et sur leur environnement. La qualité du sol vient à l’esprit, mais aussi l’effet qu’ont les bactéries sur tous. Les humains influent sur leur abondance relative et absolue par des antibiotiques, des aliments, des engrais ou tout ce qui vient à l’esprit, et ces changements affectent tout le monde.

QIIME 2 est un paquet pour l’analyse de microbiome, extensible et décentralisée avec une attention particulière sur la transparence des données et analyses. QIIME 2 permet aux chercheurs de commencer une analyse avec des données de séquences d’ADN et de terminer avec des figures et des résultats statistiques de qualité professionnelle. Principales caractéristiques :

 — suivi intégré et automatique de la provenance des données ;
 — système de type sémantique ;
 — système de greffons pour étendre les fonctionnalités d’analyse de
   microbiome ;
 — prise en charge de plusieurs types d’interface (par exemple, API,
   ligne de commande, graphique) ;

QIIME 2 est une nouvelle conception et une réécriture de la tuyauterie d’analyse de microbiome, QIIME 1. QIIME 2 corrigera la plupart des limitations de QIIME 1, tout en conservant les fonctions qui font de QIIME 1 une tuyauterie d’analyse puissante et largement utilisée.

QIIME 2 actuellement prend en charge une première tuyauterie d’analyse de microbiome de bout en bout. De nouvelles fonctionnalités seront régulièrement disponibles à travers des greffons de QIIME 2. Une liste de greffons peut être consultée sur la page de disponibilité de greffons de QIIME 2. La page des futurs greffons liste les greffons en cours de développement.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (PubMed,eprint) Nature Biotechnology 37:852 - 857 (2019)
Registry entries: Bio.tools  SciCrunch  Bioconda 
Topics: Microbial ecology
r-bioc-affxparser
kit de développement logiciel d'analyse de fichiers Affymetrix
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Paquet pour l'analyse de fichiers Affymetrix (CDF, CEL, CHP, BPMAP, BAR). Il fournit des méthodes pour une analyse rapide et avec une bonne gestion de la mémoire de fichiers Affymetrix utilisant le kit de développement logiciel Fusion d'Affymetrix. Les fichiers à base ASCII et binaire sont pris en charge. Actuellement, il y a des méthodes pour les fichiers de définition de puce (chip definition – CDF) et les fichiers d'intensité de cellule (cell intensity – CEL). Ces fichiers peuvent être lus totalement ou en partie. Par exemple, les signaux de sonde de quelques ensembles de sondes peuvent être extraits très rapidement à partir de fichiers CEL dans une structure de liste appropriée.

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r-bioc-affy
méthodes de BioConductor pour les matrices d'oligonucléotides d'Affymetrix
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Ce paquet fait partie de la suite GNU R Bioconductor. Il fournit des fonctions pour l'analyse exploratoire de matrices d'oligonucléotides.

Please cite: Laurent Gautier, Leslie Cope, Benjamin M. Bolstad and Rafael A. Irizarry: affy - analysis of Affymetrix GeneChip data at the probe level. (PubMed,eprint) Bioinformatics (3):307-315 (2004)
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r-bioc-affyio
outils BioConductor pour l'analyse de fichiers de données d'Affymetrix
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Ce paquet BioConductor fournit des routines pour l'analyse de fichiers de données d'Affymetrix basée sur l'information de format de fichier. L'objectif principal est l'accès aux formats de fichiers CEL et CDF.

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r-bioc-altcdfenvs
environnements CDF alternatifs pour BioConductor
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Ce module de BioConductor fournit des environnements CDF alternatifs (ou mappages d'ensemble de sondes) qui sont des structures de données appropriées et des fonctions pour gérer cdfenvs.

Please cite: Laurent Gautier, Morten Mooller, Lennart Friis-Hansen and Steen Knudsen: Alternative mapping of probes to genes for Affymetrix chips. (PubMed,eprint) BMC Bioinformatics (5):111 (2004)
r-bioc-annotate
annotations de BioConductor pour les puces à ADN
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Ce module de BioConductor fournit des méthodes pour l'annotation de puces à ADN.

Dans son état présent, l'objectif de base d'annotate est de fournir des routines d'interface qui prennent en charge les actions de l'utilisateur qui reposent sur les différents paquets de métadonnées fournis à travers le projet BioConductor.

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r-bioc-annotationdbi
GNU R Annotation Database Interface pour BioConductor
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Ce module de BioConductor fournit une interface utilisateur et le code de connexion aux bases de données pour les paquets de données d'annotation utilisant SQLite pour le stockage de données.

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r-bioc-annotationhub
client GNU R pour accéder aux ressources d'AnnotationHub
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Ce paquet fournit un client pour les ressources web d'AnnotationHub pour BioConductor. Les ressources web d'AnnotationHub fournissent un lieu centralisé où des fichiers génomiques (par exemple, VCF, bed, wig) et d'autres ressources d'emplacements standards (UCSC, Ensembl) peuvent être découvertes. Les ressources incluent des métadonnées sur chaque ressource, par exemple, une description textuelle, des étiquettes et les dates de modification. Le client crée et gère un cache local des fichiers récupérés par l’utilisateur, ce qui aide à un accès rapide et reproductible.

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r-bioc-aroma.light
méthodes de BioConductor de normalisation et de visualisation de données de puces à ADN
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Ce module de BioConductor fournit des méthodes légères pour la normalisation et la visualisation de données de puces à ADN utilisant seulement des types de données de base de R.

Les méthodes pour l'analyse de puces à ADN prennent des types de données de base comme les matrices et les listes de vecteurs. Ces méthodes peuvent être utilisées de façon autonome, utilisées dans d'autres paquets ou enveloppées dans des classes de plus haut niveau.

Please cite: Henrik Bengtsson, Pierre Neuvial and Terence P. Speed: TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays. (PubMed,eprint) BMC Bioinformatics 11:245 (2010)
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r-bioc-arrayexpress
accès à la base de données de puces à ADN ArrayExpress de l'EBI
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Ce paquet permet d'accéder à la base de données de puces à ADN ArrayExpress de l'EBI (European Bioinformatics Institute) et de construire des structures de données de BioConductor : ExpressionSet, AffyBatch, NChannelSet.

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r-bioc-biocgenerics
generic functions for Bioconductor
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S4 generic functions needed by many other Bioconductor packages.

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development.

Please cite: Wolfgang Huber, Vincent J Carey, Robert Gentleman, Simon Anders, Marc Carlson, Benilton S Carvalho, Hector Corrada Bravo, Sean Davis, Laurent Gatto, Thomas Girke, Raphael Gottardo, Florian Hahne, Kasper D Hansen, Rafael A Irizarry, Michael Lawrence, Michael I Love, James MacDonald, Valerie Obenchain, Andrzej K Oleś, Hervé Pagès, Alejandro Reyes, Paul Shannon, Gordon K Smyth, Dan Tenenbaum, Levi Waldron and Martin Morgan: Orchestrating high-throughput genomic analysis with Bioconductor. (PubMed) Nature Methods (2015)
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r-bioc-biocneighbors
Nearest Neighbor Detection for Bioconductor Packages
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Implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Exact searches can be performed using the k-means for k-nearest neighbors algorithm or with vantage point trees. Approximate searches can be performed using the Annoy or HNSW libraries. Searching on either Euclidean or Manhattan distances is supported. Parallelization is achieved for all methods by using BiocParallel. Functions are also provided to search for all neighbors within a given distance.

r-bioc-biomart
interface de GNU R aux bases de données BioMart (Ensembl, COSMIC, Wormbase et Gramene)
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Depuis ces dernières années, une foule de données biologiques sont devenues disponibles dans des dépôts publics de données. Un accès aisé à ces ressources précieuses de données et une incorporation solide avec l’analyse des données est nécessaire pour une analyse intégrale des données de bioinformatique. biomaRt fournit une interface à une collection croissante de bases de données mettant en œuvre la suite logicielle BioMart (http://www.biomart.org). Ce paquet permet de récupérer de grandes quantités de données de manière uniforme sans avoir besoin de comprendre les schémas sous-jacents de base de données ou d’écrire des requêtes SQL complexes. Des exemples de bases de données BioMart sont Ensembl, COSMIC, Uniprot, HGNC, Gramene, Wormbase et dbSNP mappé à Ensembl. Ces bases de données majeures procurent aux utilisateurs de biomaRt un accès direct à divers ensembles de données et permettent une large variété de requêtes en ligne puissantes, de l’annotation de gène à l’exploration de données.

Please cite: Steffen Durinck, Paul T. Spellman, Ewan Birney and Wolfgang Huber: Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. (PubMed) Nature Protocols 4(8):1184-1191 (2009)
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r-bioc-biomformat
GNU R interface package for the BIOM file format
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This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the Python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods.

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r-bioc-biostrings
GNU R string objects representing biological sequences
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Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or set of sequences.

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r-bioc-biovizbase
GNU R basic graphic utilities for visualization of genomic data
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The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency.

r-bioc-bitseq
inférence de transcription d’expression et analyse pour des données de RNA-seq
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Le paquet BitSeq est destiné à l’analyse d’expression de transcription et l’analyse d’expression différentielle de données de RNA-seq data dans un processus en deux étapes. Dans la première, il utilise la méthode d’inférence bayésienne pour inférer l’expression de transcriptions individuelles d’expériences de RNA-seq particulières. La seconde étape de BitSeq englobe l’analyse d’expression différentielle d’expression de transcription. Fournissant une expression estimée à partir de réplications dans plusieurs conditions, le modèle Log-Normal des estimations est utilisé pour déduire l’expression de transcription d’espérance conditionnelle et classer les transcriptions en se basant sur la probabilité de l’expression différentielle.

Please cite: Peter Glaus, Antti Honkela and Magnus Rattray: Identifying differentially expressed transcripts from RNA-seq data with biological variation. (PubMed,eprint) Bioinformatics 28(13):1721–1728 (2012)
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r-bioc-bsgenome
BioConductor infrastructure for Biostrings-based genome data packages
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This BioConductor module provides some basic infrastructure for Biostrings-based genome data packages.

r-bioc-cner
CNE Detection and Visualization
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Large-scale identification and advanced visualization of sets of conserved noncoding elements.

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r-bioc-complexheatmap
make complex heatmaps using GNU R
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Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics.

Please cite: Zuguang Gu, Roland Eils and Matthias Schlesner: Complex heatmaps reveal patterns and correlations in multidimensional genomic data.. (PubMed,eprint) Bioinformatics (2016)
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r-bioc-ctc
Cluster and Tree Conversion
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Tools for export and import classification trees and clusters to other programs.

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r-bioc-cummerbund
tool for analysis of Cufflinks RNA-Seq output
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Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations.

Please cite: L. Goff and C. Trapnell: cummeRbund: Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data (2012)
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r-bioc-dada2
sample inference from amplicon sequencing data
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The dada2 package contributes to software workflows to interpret sequencing data from microbiota - the relative abundance of bacterial and/or yeast, typically measured in the gut. It infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching.

Please cite: Benjamin J Callahan, Paul J McMurdie, Michael J Rosen, Andrew W Han, Amy Jo A Johnson and Susan P Holmes: DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods 13:581-583 (2016)
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r-bioc-deseq2
R package for RNA-Seq Differential Expression Analysis
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Differential gene expression analysis based on the negative binomial distribution. Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

Please cite: Michael I Love, Wolfgang Huber and Simon Anders: Moderated estimation of fold change and dispersion for {RNA}-seq data with {DESeq}2. (eprint) Genome Biol 15(12) (2014)
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r-bioc-dnacopy
paquet de R : analyse des données de nombre de copies d'ADN
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Ce paquet met en œuvre l’algorithme CBS (« circular binary segmentation » – segmentation binaire circulaire) pour segmenter les données de nombres de copies d’ADN et identifier les régions génomiques avec un nombre de copies anormal.

Ce paquet sert à analyser des données de nombres de copies de puces à ADN, qui sont habituellement (mais pas toujours) appelées données de puce d'hybridation génomique comparative (« Comparative Genomic Hybridization », array CGH). Il met en œuvre une méthodologie pour trouver les points de rupture dans ces données qui sont des points après lesquels le test (log) comparé aux ratios de référence ont changé l’emplacement. Le modèle est que les points de rupture correspondent aux positions où le nombre de copies d’ADN sous-jacente a changé. Pas conséquent, les points de rupture peuvent être utilisés pour identifier les régions du nombre de copies gagné ou perdu. Une fonction est aussi fournie pour faire des tracés relatifs à ces données.

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r-bioc-ebseq
R package for RNA-Seq Differential Expression Analysis
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r-bioc-ebseq is an R package for identifying genes and isoforms differentially expressed (DE) across two or more biological conditions in an RNA-seq experiment.

Please cite: Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M. G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart and Christina Kendziorski: EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. (eprint) Bioinformatics 29(8):1035-1043 (2013)
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r-bioc-ensembldb
GNU R utilities to create and use an Ensembl based annotation database
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The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, the ensembldb package provides also a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes.

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r-bioc-genefilter
methods for filtering genes from microarray experiments
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This BioConductor module provides methods for filtering genes from microarray experiments. It contains some basic functions for filtering genes.

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r-bioc-geneplotter
R package of functions for plotting genomic data
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Geneplotter contains plotting functions for microarrays.

The functions cPlot and cColor allow the user to associate microarray expression data with chromosomal location. The plots can include any subset (by default all chromosomes are shown) of chromosomes for the organism being considered.

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r-bioc-genomeinfodb
BioConductor utilities for manipulating chromosome identifiers
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This package contains BioConductor utilities for manipulating chromosome and other 'seqname' identifiers.

The Seqnames package contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order.

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r-bioc-genomicalignments
BioConductor representation and manipulation of short genomic alignments
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This BioConductor package provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments.

Please cite: Michael Lawrence, Wolfgang Huber, Hervé Pagès, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T. Morgan and Vincent J. Carey: Software for Computing and Annotating Genomic Ranges. (PubMed,eprint) PLoS Computational Biology 9(8):e1003118 (2013)
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r-bioc-genomicfeatures
GNU R tools for making and manipulating transcript centric annotations
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A set of tools and methods for making and manipulating transcript centric annotations. With these tools the user can easily download the genomic locations of the transcripts, exons and cds of a given organism, from either the UCSC Genome Browser or a BioMart database (more sources will be supported in the future). This information is then stored in a local database that keeps track of the relationship between transcripts, exons, cds and genes. Flexible methods are provided for extracting the desired features in a convenient format.

Please cite: M. Lawrence, W. Huber, H. Pagès, P. Aboyoun and M. Carlson et al.: Software for Computing and Annotating Genomic Ranges. (PubMed,eprint) PLoS Comput Biol 9(8):e1003118 (2013)
r-bioc-genomicranges
BioConductor representation and manipulation of genomic intervals
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The ability to efficiently store genomic annotations and alignments is playing a central role when it comes to analyze high-throughput sequencing data (a.k.a. NGS data). The package defines general purpose containers for storing genomic intervals as well as more specialized containers for storing alignments against a reference genome.

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r-bioc-geoquery
Get data from NCBI Gene Expression Omnibus (GEO)
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The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor.

Please cite: Sean Davis and Paul Meltzer: GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor Bioinformatics 14,:1846-1847, (2007,)
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r-bioc-go.db
annotation maps describing the entire Gene Ontology
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This package is part of BioConductor and provides a set of annotation maps describing the entire Gene Ontology assembled using data from GO.

The package helps running the test suites of some BioConductor packages.

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r-bioc-graph
handle graph data structures for BioConductor
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This BioConductor module implements some simple graph handling capabilities. These are for instance used in hypergraph module which in turn is used by several other BioConductor packages.

r-bioc-gseabase
Gene set enrichment data structures and methods
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This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA).

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r-bioc-gsva
Gene Set Variation Analysis for microarray and RNA-seq data
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Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene- set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross- tissue pathway analysis, in a pathway-centric manner.

Please cite: Sonja Hänzelmann, Robert Castelo and Justin Guinney: GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14(7) (2013)
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r-bioc-gviz
Plotting data and annotation information along genomic coordinates
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Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data.

Please cite: Michael Lawrence, Robert Gentleman and "Vincent Carey: rtracklayer: an R package for interfacing with genome browsers. (PubMed,eprint) Bioinformatics 25(14):1841-1842 (2009)
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r-bioc-hypergraph
BioConductor hypergraph data structures
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This package BioConductor implements some simple capabilities for representing and manipulating hypergraphs.

r-bioc-impute
Imputation for microarray data
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R package which provide a function to perform imputation for microarray data (currently KNN only).

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r-bioc-iranges
conteneurs de bas niveau de GNU R de stockage de plages d’entiers
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La classe IRanges et ses extensions sont des conteneurs de bas niveau pour stocker des ensembles de plages d’entiers. Une utilisation courante de ces conteneurs en biologie est la représentation d’un ensemble de régions de chromosome. Des extensions plus particulières de la classe IRanges permettent le stockage d’informations supplémentaires pour chaque région de chromosome ainsi que les relations hiérarchiques entre ces régions.

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r-bioc-limma
modèles linéaires pour des données de biopuce (microarray)
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Les biopuces (puces à gènes) sont des plaques microscopiques de brins de courtes séquences d’ADN soigneusement arrangées ou des surfaces préparées chimiquement auxquelles d’autres ADN se lient de préférence. Le montant des liaisons d’ADN dans des emplacements différents de ces puces, déterminé classiquement par un colorant fluorescent, est à déterminer. La technologie est utilisée habituellement avec de l’ADN dérivant d’ARN, c'est-à-dire pour déterminer l’activité d’un gène ou des variantes d’épissage. Mais la technologie est aussi utilisée pour déterminer les variations de séquence dans l’ADN génomique.

Le paquet Bioconductor prend en charge l’analyse de données d’expression de biopuces (microarray), particulièrement l’utilisation de modèles linéaires pour l’analyse d’expériences imaginées et l’évaluation d’expression différentielle. Le paquet fournit des possibilités de pré- traitement pour les puces décolorées en deux couleurs. Les méthodes d’expression différentielle s’appliquent à toutes les plateformes de puce et traitent les expérimentations simple canal et double canaux d’Affymetrix, de manière unifiée.

Please cite: Gordon K. Smyth: Limma: linear models for microarray data. (eprint) :397-420 (2005)
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r-bioc-makecdfenv
BioConductor CDF Environment Maker
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This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment.

r-bioc-mergeomics
Integrative network analysis of omics data
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The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts: 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA).

Please cite: Le Shu, Yuqi Zhao, Zeyneb Kurt, Sean Geoffrey Byars, Taru Tukiainen, Johannes Kettunen, Luz D. Orozco, Matteo Pellegrini, Aldons J. Lusis, Samuli Ripatti, Bin Zhang, Michael Inouye, Ville-Petteri Mäkinen and Xia Yang: Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. (eprint) BMC Genomics (2016)
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r-bioc-metagenomeseq
GNU R statistical analysis for sparse high-throughput sequencing
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MetagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.

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r-bioc-mofa
Multi-Omics Factor Analysis (MOFA)
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Multi-Omics Factor Analysis: an unsupervised framework for the integration of multi-omics data sets.

Upstream no longer supports this package. This package only still ships to help with rerunning/comparing/transitioning existing projects. For new projects please upgrade to MOFA2 (MOFA+). Actually, also when adding new data to old projects, MOFA2 has further improved the handling of multiple factors, and to compensate for a batch effect that is likely introduced with additional data, may be an immediate use case for that new version.

Please cite: Ricard Argelaguet, Britta Velten, Damien Arnol, Sascha Dietrich, Thorsten Zenz, John C Marioni, Florian Buettner, Wolfgang Huber and Oliver Stegle: Link to publication Mol Syst Biol 14:e8124 (2018)
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r-bioc-multiassayexperiment
Software for integrating multi-omics experiments in BioConductor
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MultiAssayExperiment harmonizes data management of multiple assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames.

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r-bioc-nanostringqcpro
??? missing short description for package r-bioc-nanostringqcpro :-(
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r-bioc-oligo
Preprocessing tools for oligonucleotide arrays
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A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files).

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r-bioc-oligoclasses
classes pour des tableaux de grande capacité gérés par oligo et crlmm
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Ce paquet fournit des définitions de classe, des vérifications de validité et des méthodes d’initialisation pour les classes utilisées par les paquets oligo et crlmm.

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r-bioc-org.hs.eg.db
genome-wide annotation for Human
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This package provides descriptions of parts of the human genome that have been identified to be coding for RNA, and likely also for proteins. It also offers links to entries of equivalent (orthologous) genes in other species.

This package is prepared from the BioConductor community and contributes to many workflows and routine analyses in computational biology.

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r-bioc-pcamethods
BioConductor collection of PCA methods
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Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany.

Please cite: Wolfram Stacklies, Henning Redestig, Matthias Scholz, Dirk Walther and Joachim Selbig: pcaMethods — a bioconductor package providing PCA methods for incomplete data. (PubMed,eprint) Bioinformatics 23(9):1164–1167 (2007)
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r-bioc-phyloseq
GNU R handling and analysis of high-throughput microbiome census data
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The Bioconductor module phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.

Please cite: Paul J. McMurdie and Susan Holmes: phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8(4):e61217 (2013)
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r-bioc-preprocesscore
collection de fonctions de prétraitement de BioConductor
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Ce module de BioConductor fournit une bibliothèque de fonctions de prétraitement. Il est importé par plusieurs autres modules de BioConductor.

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r-bioc-purecn
copy number calling and SNV classification using targeted short read sequencing
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This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples.

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r-bioc-qusage
qusage: Quantitative Set Analysis for Gene Expression
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This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu)

Please cite: Gur Yaari, Christopher R. Bolen, Juilee Thakar and Steven H. Kleinstein: Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations. (PubMed,eprint) Nucleic Acids Res. 41(18):e170 (2013)
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r-bioc-rbgl
R interface to the graph algorithms contained in the BOOST library
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RBGL is part of the BioConductor GNU R suite. It is a fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST C++ libraries.

r-bioc-rsamtools
importation de fichiers BAM, BCF et tabix pour GNU R
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Ce paquet fournit une interface pour les utilitaires samtools, bcftools et tabix pour la manipulation de fichiers SAM (Sequence Alignment/Map), BCF (binary variant call) et tabix compressés (indexed tab-delimited).

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r-bioc-rtracklayer
GNU R interface to genome browsers and their annotation tracks
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Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport.

Please cite: Michael Lawrence, Robert Gentleman and "Vincent Carey: rtracklayer: an R package for interfacing with genome browsers. (PubMed,eprint) Bioinformatics 25(14):1841-1842 (2009)
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r-bioc-s4vectors
BioConductor S4 implementation of vectors and lists
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The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages).

r-bioc-savr
GNU R parse and analyze Illumina SAV files
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This BioConductor module enables to parse Illumina Sequence Analysis Viewer (SAV) files, access data, and generate QC plots.

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r-bioc-shortread
GNU R classes and methods for high-throughput short-read sequencing data
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This BioConductor module is a package for input, quality assessment, manipulation and output of high-throughput sequencing data. ShortRead is provided in the R and Bioconductor environments, allowing ready access to additional facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources.

Please cite: Martin Morgan, Simon Anders, Michael Lawrence, Patrick Aboyoun, Hervé Pagès and Robert Gentleman: ShortRead: a Bioconductor package for input, quality assessment and exploration of high-throughput sequence data. (PubMed,eprint) Bioinformatics 25(19):2607-2608 (2009)
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r-bioc-snpstats
méthodes et les classes de SnpMatrix et XSnpMatrix de BioConductor
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Ce paquet de BioConductor fournit des fonctions R pour travailler avec les méthodes et les classes de SnpMatrix et XSnpMatrix.

La nécessité de SnpStats découle du besoin de stocker et d’analyser des données de génotypes PNS dans lesquels le sujet ne peut être à coup sûr assigné à un des trois génotypes possibles. Cela nécessitait un changement dans la manière dont les données sont stockées en interne, bien que snpStats puisse encore gérer les données de génotype nommées conventionnellement dans le mode de stockage originel de snpMatrix. snpStats manque actuellement de certaines fonctions qui étaient présentes dans snpMatrix (bien que, heureusement, les lacunes importantes seront bientôt comblées), mais inclut aussi plusieurs fonctions importantes.

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r-bioc-structuralvariantannotation
Variant annotations for structural variants
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StructuralVariantAnnotation contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects.

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r-bioc-tfbstools
GNU R Transcription Factor Binding Site (TFBS) Analysis
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TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software.

Please cite: Ge Tan and Boris Lenhard: TFBSTools: an R/bioconductor package for transcription factor binding site analysis. (PubMed,eprint) Bioinformatics 32(10):1555–1556 (2016)
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r-bioc-titancna
Subclonal copy number and LOH prediction from whole genome sequencing
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Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data.

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r-bioc-tximport
estimations au niveau transcription de séquences biologiques
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Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts.

Please cite: Charlotte Soneson, Michael I. Love and Mark D. Robinson: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4:1521 (2015)
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r-bioc-variantannotation
BioConductor annotation of genetic variants
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This BioConductor package provides R functions to annotate variants, compute amino acid coding changess and to predict coding outcomes.

Please cite: Valerie Obenchain, Michael Lawrence, Vincent Carey, Stephanie Gogarten, Paul Shannon and Martin Morgan: VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants. Bioinformatics (2014)
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r-bioc-xvector
BioConductor representation and manpulation of external sequences
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This BioConductor package provides memory efficient S4 classes for storing sequences "externally" (behind an R external pointer, or on disk).

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r-cran-adegenet
GNU R exploratory analysis of genetic and genomic data
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Toolset for the exploration of genetic and genomic data. Adegenet provides formal (S4) classes for storing and handling various genetic data, including genetic markers with varying ploidy and hierarchical population structure ('genind' class), alleles counts by populations ('genpop'), and genome-wide SNP data ('genlight'). It also implements original multivariate methods (DAPC, sPCA), graphics, statistical tests, simulation tools, distance and similarity measures, and several spatial methods. A range of both empirical and simulated datasets is also provided to illustrate various methods.

Please cite: Thibaut Jombart: adegenet: a R package for the multivariate analysis of genetic markers. (PubMed,eprint) Bioinformatics 24(11):1403-5 (2008)
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r-cran-adephylo
GNU R exploratory analyses for the phylogenetic comparative method
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This GNU R package provides multivariate tools to analyze comparative data, i.e. a phylogeny and some traits measured for each taxa.

Please cite: Thibaut Jombart, François Balloux and Stéphane Dray: adephylo: new tools for investigating the phylogenetic signal in biological traits. (PubMed,eprint) Bioinformatics 26(15):1907-1909 (2010)
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r-cran-amap
Another Multidimensional Analysis Package
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Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions).

r-cran-biwt
biweight mean vector and covariance and correlation
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Compute multivariate location, scale, and correlation estimates based on Tukey's biweight M-estimator.

r-cran-dt
GNU R wrapper of the JavaScript library 'DataTables'
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Data objects in R can be rendered as HTML tables using the JavaScript library 'DataTables' (typically via R Markdown or Shiny). The 'DataTables' library has been included in this R package. The package name 'DT' is an abbreviation of 'DataTables'.

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r-cran-dynamictreecut
Methods for Detection of Clusters in Hierarchical Clustering
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Dendrograms Contains methods for detection of clusters in hierarchical clustering dendrograms.

r-cran-fastcluster
Fast hierarchical clustering routines for GNU R
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Fastcluster implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: “linkage” in the SciPy package “scipy.cluster.hierarchy”, “hclust” in R's “stats” package, and the “flashClust” package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide. For information on how to install the Python files, see the file INSTALL in the source distribution.

Please cite: Daniel Müllner: fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python. (eprint) Journal of Statistical Software 53(9):1-18 (2013)
r-cran-future.apply
apply function to elements in parallel using futures
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Implementations of apply(), by(), eapply(), lapply(), Map(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. These future_apply() functions come with the same pros and cons as the corresponding base-R apply() functions but with the additional feature of being able to be processed via the future framework.

r-cran-future.batchtools
Future API for Parallel and Distributed Processing
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Implementation of the Future API on top of the 'batchtools' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS', e.g. 'y <- future.apply::future_lapply(files, FUN = process)'.

r-cran-ica
Independent Component Analysis
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Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).

r-cran-itertools
outils pour itérateurs
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Il s’agit d’outils divers pour créer des itérateurs, beaucoup basés sur des fonctions présentes dans le module itertools de Python et d’autres basés sur des fonctions présentes dans le paquet « snow ».

r-cran-kaos
Encoding of Sequences Based on Frequency Matrix Chaos
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Sequences encoding by using the chaos game representation. Löchel et al. (2019) .

Please cite: Hannah F. Löchel, Dominic Eger, Theodor Sperlea and Dominik Heider: Deep learning on chaos game representation for proteins. Bioinformatics (2019)
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r-cran-metap
Meta-Analysis of Significance Values
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The canonical way to perform meta-analysis involves using effect sizes. When they are not available this package provides a number of methods for meta-analysis of significance values including the methods of Edgington, Fisher, Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results; and a routine for graphical display.

r-cran-minerva
Maximal Information-Based Nonparametric Exploration
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Wrapper for 'minepy' implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family). Detailed information of the ANSI C implementation of 'minepy' can be found at http://minepy.readthedocs.io/en/latest.

r-cran-natserv
interface de GNU R pour « NatureServe »
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Il s’agit d’une interface pour « NatureServe » (http://www.natureserve.org). Elle fournit des méthodes récupérer des données, des métadonnées d’images, rechercher des noms de taxonomies et créer des cartes.

r-cran-nmf
GNU R framework to perform non-negative matrix factorization
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This package implements a set of previously published algorithms and seeding methods, and provides a framework to test, develop and plug new/custom algorithms. Most of the built-in algorithms have been optimized, and the main interface function provides parallel computations on multicore machines.

Please cite: Renaud Gaujoux and Cathal Seoighe: A flexible R package for nonnegative matrix factorization. (PubMed,eprint) BMC Bioinformatics 11(1):367 (2010)
r-cran-optimalcutpoints
Computing Optimal Cutpoints in Diagnostic Tests
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Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.

Please cite: Mónica López-Ratón María Xosé Rodríguez-Álvarez, Carmen Cadarso Suárez and Francisco Gude Sampedro: OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests. Journal of Statistical Software 61(8):1-36 (2014)
r-cran-parmigene
information mutuelle en parrallèle pour établir des réseaux géniques
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Ce paquet fournit une estimation en parallèle de l’information mutuelle en se basant sur une évaluation de l’entropie des distances des k plus proches voisins et des algorithmes de reconstruction de réseaux de régulation génique.

r-cran-pcapp
Robust PCA by Projection Pursuit
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Provides functions for robust PCA by projection pursuit. The methods are described in Croux et al. (2006) , Croux et al. (2013) , Todorov and Filzmoser (2013) .

Please cite: V. Todorov and P. Filzmoser: Comparing Classical and Robust Sparse PCA. 190 (2013)
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r-cran-proc
Display and Analyze ROC Curves
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Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

r-cran-rann
Fast Nearest Neighbour Search Using L2 Metric
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Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is support for approximate as well as exact searches, fixed radius searches and 'bd' as well as 'kd' trees. The distance is computed using the L2 (Euclidean) metric. Please see package 'RANN.L1' for the same functionality using the L1 (Manhattan, taxicab) metric.

r-cran-rcpphnsw
R bindings for a Library for Approximate Nearest Neighbors
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'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This package provides a minimal R interface by relying on the 'Rcpp' package. See https://github.com/nmslib/hnswlib for more on 'hnswlib'. 'hnswlib' is released under Version 2.0 of the Apache License.

r-cran-robustrankaggreg
Methods for robust rank aggregation
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Methods for aggregating ranked lists, especially lists of genes. It implements the Robust Rank Aggregation (Kolde et. al in preparation) and some other simple algorithms for the task. RRA method uses a probabilistic model for aggregation that is robust to noise and also facilitates the calculation of significance probabilities for all the elements in the final ranking.

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r-cran-rocr
paquet de GNU R pour préparer et afficher des courbes ROC
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Les courbes de caractéristiques de performance (ROC), les courbes de sensibilité/spécificité, les graphes d’élévation (lift chart) et les graphiques de précision/rappel sont des exemples populaires de visualisations dans l’analyse de compromis (trade-off) pour des paires particulières de mesures de performance. ROCR est un outil flexible pour créer des courbes en 2D de performance paramétrées selon des seuils en combinant librement deux parmi 25 mesures de performance (de nouvelles mesures peuvent être ajoutées en utilisant une interface standard). Des moyennes de courbes de différentes exécutions de validation croisée ou de bootstrap peuvent être réalisées par différentes méthodes, et des écarts-types, des erreurs-types ou des boîtes à moustache peuvent être utilisés pour visualiser les variations entre les exécutions. Le paramétrage peut être visualisé en imprimant des valeurs de seuil aux positions correspondantes des courbes ou en colorant la courbe selon le seuil. Tous les composants d’un tracé de performance peuvent être ajustés rapidement en utilisant un mécanisme flexible de répartition de paramètres. Malgré sa flexibilité, ROCR est d’utilisation aisée, avec seulement trois commandes et des valeurs par défaut appropriées pour toutes les options de paramètres.

Caractéristiques de ROCR : courbe ROC, tracé de précision/rappel, graphe d’élévations, courbe des coûts, courbe personnalisée en choisissant librement une mesure de performance pour l’axe des X et une autre pour l’axe des Y, gestion des données issues de validation croisée ou bootstrap, moyenne de courbe (verticalement, horizontalement ou par seuil), graphique d’erreurs-types, boîte à moustache, courbe avec codage de couleurs selon le seuil, impression de valeurs de seuil sur la courbe, intégration poussée avec les fonctions de tracé de R (rendant aisée l’ajustement de tracé ou la combinaison de plusieurs tracés), personnalisation complète, facilité d’utilisation (seulement trois commandes).

Mesures de performance connues de ROCR : précision, taux d’erreurs, taux de vrais positifs, taux de faux positifs, taux de vrais négatifs, taux de faux négatifs, sensibilité, spécificité, rappel, valeur prédictive positive, valeur prédictive négative, précision, taux de chute, élément non retrouvé, coefficient de corrélation phi, coefficient de corrélation de Matthews, information mutuelle, test du khi deux d'indépendance, rapport des chances (odds ratio), valeur d’élévation (lift value), test F de précision/rappel, enveloppe convexe ROC, aire sous la courbe ROC, seuil de rentabilité pour les précision/rappel, erreur de calibration, espérance mathématique d’entropie-croisée, erreur moyenne quadratique, mesure SAR, espérance du coût, coût explicite.

Please cite: Tobias Sing, Oliver Sander, Niko Beerenwinkel and Thomas Lengauer: ROCR: visualizing classifier performance in R. (PubMed,eprint) Bioinformatics 21(20):3940-3941 (2005)
r-cran-rook
web server interface for R
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The package provides a set of routines for R to perform as a web server. This is used by a series of reverse dependencies to develop interactive interfaces to statistical analyses and reports.

r-cran-rsvd
Randomized Singular Value Decomposition
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Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) .

Please cite: N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton and J. Nathan Kutz: Randomized Matrix Decompositions Using R. Journal of Statistical Software 89(11):1-48 (2019)
r-cran-shazam
Immunoglobulin Somatic Hypermutation Analysis
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Provides a computational framework for Bayesian estimation of antigen-driven selection in immunoglobulin (Ig) sequences, providing an intuitive means of analyzing selection by quantifying the degree of selective pressure. Also provides tools to profile mutations in Ig sequences, build models of somatic hypermutation (SHM) in Ig sequences, and make model-dependent distance comparisons of Ig repertoires.

SHazaM is part of the Immcantation analysis framework for Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) and provides tools for advanced analysis of somatic hypermutation (SHM) in immunoglobulin (Ig) sequences. Shazam focuses on the following analysis topics:

  • Quantification of mutational load SHazaM includes methods for determine the rate of observed and expected mutations under various criteria. Mutational profiling criteria include rates under SHM targeting models, mutations specific to CDR and FWR regions, and physicochemical property dependent substitution rates.
  • Statistical models of SHM targeting patterns Models of SHM may be divided into two independent components: 1) a mutability model that defines where mutations occur and 2) a nucleotide substitution model that defines the resulting mutation. Collectively these two components define an SHM targeting model. SHazaM provides empirically derived SHM 5-mer context mutation models for both humans and mice, as well tools to build SHM targeting models from data.
  • Analysis of selection pressure using BASELINe The Bayesian Estimation of Antigen-driven Selection in Ig Sequences (BASELINe) method is a novel method for quantifying antigen-driven selection in high-throughput Ig sequence data. BASELINe uses SHM targeting models can be used to estimate the null distribution of expected mutation frequencies, and provide measures of selection pressure informed by known AID targeting biases.
  • Model-dependent distance calculations SHazaM provides methods to compute evolutionary distances between sequences or set of sequences based on SHM targeting models. This information is particularly useful in understanding and defining clonal relationships.
Please cite: Namita T. Gupta, Jason A. Vander Heiden, Mohamed Uduman, Daniel Gadala-Maria, Gur Yaari and Steven H. Kleinstein: Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data.. (PubMed,eprint) Bioinformatics 31(20):3356-3358 (2015)
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r-cran-sitmo
GNU R parallel pseudo random number generator 'sitmo' header files
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Provided within are two high quality and fast PPRNGs that may be used in an 'OpenMP' parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence. The objective of this library to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and 'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the header files inside of 'sitmo' instead of including a copy of each engine within their individual package. Lastly, the package contains example implementations using the 'sitmo' package and three accompanying vignette that provide additional information.

r-cran-venndiagram
Generate High-Resolution Venn and Euler Plots
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A set of functions to generate high-resolution Venn and Euler plots. Includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.

ruby-rgfa
parse, edit and write GFA format graphs in Ruby
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The Graphical Fragment Assembly (GFA) format is a proposed file format to describe the product of a genome sequence assembly process. rgfa implements the proposed specifications for the GFA format described under https://github.com/pmelsted/GFA-spec/blob/master/GFA-spec.md as closely as possible. The library allows one to create an RGFA object from a file in the GFA format or from scratch, to enumerate the graph elements (segments, links, containments, paths and header lines), to traverse the graph (by traversing all links outgoing from or incoming to a segment), to search for elements (e.g. which links connect two segments) and to manipulate the graph (e.g. to eliminate a link or a segment or to duplicate a segment distributing the read counts evenly on the copies).

Debian packages in contrib or non-free

python3-bcbio
library for analysing high-throughput sequencing data
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This package installs the Python 3 libraries of the bcbio-nextgen toolkit implementing best-practice pipelines for fully automated high throughput sequencing analysis.

A high-level configuration file specifies inputs and analysis parameters to drive a parallel pipeline that handles distributed execution, idempotent processing restarts and safe transactional steps. The project contributes a shared community resource that handles the data processing component of sequencing analysis, providing researchers with more time to focus on the downstream biology.

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python3-seqcluster
analysis of small RNA in NGS data
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Identifies small RNA sequences of all sorts in RNA sequencing data. This is especially helpful for the identification of RNA that is neither coding nor belonging to the already well-established group of miRNA, towards many tools feel constrained to.

This package provides the Python module. For executables see the package 'seqcluster'.

Please cite: Lorena Pantano, Marc R. Friedländer, Georgia Escaramís, Esther Lizano, Joan Pallarès-Albanell, Isidre Ferrer, Xavier Estivill and Eulàlia Martí: Specific small-RNA signatures in the amygdala at premotor and motor stages of Parkinson's disease revealed by deep sequencing analysis. (PubMed) Bioinformatics (2015)
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vdjtools
framework for post-analysis of B/T cell repertoires
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VDJtools is an open-source Java/Groovy-based framework designed to facilitate analysis of immune repertoire sequencing (RepSeq) data. VDJtools computes a wide set of statistics and is able to perform various forms of cross-sample analysis. Both comprehensive tabular output and publication-ready plots are provided.

The main aims of the VDJtools Project are:

  • To ensure consistency between post-analysis methods and results
  • To save the time of bioinformaticians analyzing RepSeq data
  • To create an API framework facilitating development of new RepSeq analysis applications
  • To provide a simple enough command line tool so it could be used by immunologists and biologists with little computational background
Please cite: M Shugay, D.V. Bagaev, M.A. Turchaninova, D.A. Bolotin, O.V. Britanova, E.V. Putintseva, M.V. Pogorelyy, V.I. Nazarov VI, I.V. Zvyagin, V.I. Kirgizova, K.I. Kirgizov, E.V. Skorobogatova and D.M. Chudakov: VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires. (PubMed,eprint) PLoS Comput Biol. 11(11):e1004503 (2015)

Packaging has started and developers might try the packaging code in VCS

libatomicqueue-dev
devel files for C++ atomic_queue library
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C++11 multiple-producer-multiple-consumer lockless queues based on circular buffer with std::atomic. The main design principle these queues follow is simplicity: the bare minimum of atomic operations, fixed size buffer, value semantics.

The circular buffer side-steps the memory reclamation problem inherent in linked-list based queues for the price of fixed buffer size. See Effective memory reclamation for lock-free data structures in C++ for more details.

These qualities are also limitations:

  • The maximum queue size must be set at compile time or construction time.
  • There are no OS-blocking push/pop functions.

Nevertheless, ultra-low-latency applications need just that and nothing more. The simplicity pays off, see the throughput and latency benchmarks.

Available containers are:

  • AtomicQueue - a fixed size ring-buffer for atomic elements.
  • OptimistAtomicQueue - a faster fixed size ring-buffer for atomic elements which busy-waits when empty or full.
  • AtomicQueue2 - a fixed size ring-buffer for non-atomic elements.
  • OptimistAtomicQueue2 - a faster fixed size ring-buffer for non-atomic elements which busy-waits when empty or full.

These containers have corresponding AtomicQueueB, OptimistAtomicQueueB, AtomicQueueB2, OptimistAtomicQueueB2 versions where the buffer size is specified as an argument to the constructor.

libfast-perl
FAST Analysis of Sequences Toolbox
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The FAST Analysis of Sequences Toolbox (FAST) is a set of Unix tools (for example fasgrep, fascut, fashead and fastr) for sequence bioinformatics modeled after the Unix textutils (such as grep, cut, head, tr, etc). FAST workflows are designed for "inline" (serial) processing of flatfile biological sequence record databases per-sequence, rather than per-line, through Unix command pipelines. The default data exchange format is multifasta (specifically, a restriction of BioPerl FastA format). FAST tools expose the power of Perl and BioPerl for sequence analysis to non-programmers in an easy-to-learn command-line paradigm.

You do not need to know Perl or BioPerl to use FAST.

libforester-java
Libraries for evolutionary biology and comparative genomics research
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Forester is a library of Java software for phylogenomics and evolutionary biology research. It can be used to read or write phylogenetic trees, export trees to graphics file,...

libnexml-java
Java API for NeXML
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Java NeXML libraries and tools

  • model: the DOM-based core java 5 NeXML reading/writing API, inside src/main/java as well as JUnit tests inside src/tets/java. The API consists of interfaces in the org.nexml.model package and implementations thereof in the org.nexml.model.impl package.
  • mesquite_module: NeXML import/export functionality for mesquite. This subfolder structure contains classess (inside src/main/java) that depend on the org.nexml.model.* architecture. In addition there are resource files: properties files that map between certain annotation namespaces and/or predicates as encountered in NeXML files, and the Java handler classes that are to be dynamically loaded to operate on them; and a default Tree Style Sheet (TSS) file for marking up tree visualizations.
  • validator: Xerces-J-based XML validator (written by Terri Liebowitz of the San Diego Supercomputing Center, with modifications by Mark Holder) and a ValidateNeXML class that does essentially the same thing, but more tailored to NeXML specifically.
  • transformer: class that transforms NeXML documents into CDAO documents using the xslt stylesheets found in $NEXML_ROOT/xslt.
Please cite: Daniel H. Huson and Celine Scornavacca: Dendroscope 3: An Interactive Tool for Rooted Phylogenetic Trees and Networks. (PubMed,eprint) Systematic Biology 61(6):1061–1067 (2012)
python3-compclust
explore and quantify relationships between clustering results
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CompClust is a python package written using the pyMLX and IPlot APIs. It provides software tools to explore and quantify relationships between clustering results. Its development has been largely built around needs of microarray data analysis but could be easily used in other domains.

Briefly pyMLX provides for efficient and convenient execution of many clustering algorithms using a extendable library of algorithms. It also provides many-to-many linkages between data features and annotations (such as cluster labels, gene names, gene ontology information, etc.) These linkages persist through varied data manipulations. IPlot provides an abstraction of the plotting process in which any arbitrary feature or derived feature of the data can be projected onto any feature of the plot, including the X,Y coordinates of points, marker symbol, marker size, maker/line color, etc. These plots are intrinsically linked to the dataset, the View and the Labeling classes found within pyMLX.

python3-consensuscore2
generate consensus sequences for PacBio data -- Python 3
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ConsensusCore2 embodies core C++ routines underlying the Arrow HMM algorithm for PacBio multi-sequence consensus. Arrow is the successor to the Quiver model---a CRF model that was embodied in the ConsensusCore C++ library. Compared to Quiver, the Arrow model is more statistically principled and easier and more robust to train.

This package installs the library for Python 3.

python3-galaxy-lib
Subset of Galaxy core code base designed to be used
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A small subset of the Galaxy project for reuse outside the core. The Galaxy software framework enables researchers without informatics expertise to perform computational analyses through the web. A user interacts with Galaxy through the web by uploading and analyzing the data. Galaxy interacts with underlying computational infrastructure (servers that run the analyses and disks that store the data) without exposing it to the user.

python3-misopy
Mixture of Isoforms model for RNA-Seq isoform quantitation (Python 3)
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MISO (Mixture of Isoforms) is a probabilistic framework that quantitates the expression level of alternatively spliced genes from RNA-Seq data, and identifies differentially regulated isoforms or exons across samples. By modeling the generative process by which reads are produced from isoforms in RNA-Seq, the MISO model uses Bayesian inference to compute the probability that a read originated from a particular isoform.

MISO uses the inferred assignment of reads to isoforms to quantitate the abundances of the underlying set of alternative mRNA isoforms. Confidence intervals over estimates can be obtained, which quantify the reliability of the estimates.

This is the Python 3 module.

Please cite: Yarden Katz, Eric T. Wang, Edoardo M. Airoldi and Christopher B. Burge: Analysis and design of RNA sequencing experiments for identifying isoform regulation. (PubMed) Nature Methods 7(12):1009–1015 (2010)
python3-scanpy
Single-Cell Analysis in Python
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VCS1.9.6-1all
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License: BSD-3-Clause
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Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.

Please cite: F. Alexander Wolf, Philipp Angerer and Fabian J. Theis: SCANPY: large-scale single-cell gene expression data analysis. (eprint) Genome Biology 19(15) (2018)
Registry entries: Bio.tools  SciCrunch  Bioconda 
q2-composition
QIIME2 plugin for Compositional statistics
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VCS2021.8.0+ds-1all
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License: BSD-3-clause
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-deblur
QIIME2 plugin to wrap the Deblur software for sequence quality control
Versions of package q2-deblur
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License: BSD-3-clause
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Version: 2023.9.0-1

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Amnon Amir, Daniel McDonald, Jose A. Navas-Molina, Evguenia Kopylova, James T. Morton, Zhenjiang Zech Xu, Eric P. Kightley, Luke R. Thompson, Embriette R. Hyde, Antonio Gonzalez and Rob Knight: Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. (PubMed,eprint) mSystems 2 (2017)
q2-diversity
QIIME2 plugin for core diversity analysis
Versions of package q2-diversity
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License: BSD-3-clause
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Version: 2021.8.0-1

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. Functionality is made available through QIIME 2 plugins.

This plugin provides the means to statistically assess the diversity of microbiota. This has direct clinical interest, since with whatever we eat or have antibiotics applied, the survival of different groups of bacteria/yeasts will be affected. From these relative abundances of strains that constribute the microbiome, most prominently, comparisons within a group of samples (or an individual) determines the alpha diversity and between (groups of) samples the beta diversity is inspected.

This package is key to most workflows in qiime.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-gneiss
QIIME2 plugin for Compositional Data Analysis and Visualization
Versions of package q2-gneiss
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VCS2020.11.1-1all
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License: BSD-3-clause
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Version: 2020.11.1-1

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-longitudinal
QIIME2 plugin for longitudinal studies and paired comparisons
Versions of package q2-longitudinal
ReleaseVersionArchitectures
VCS2023.9.1+ds-1all
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License: BSD-3-clause
Debian package not available
Git
Version: 2023.9.1+ds-1

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-vsearch
QIIME 2 plugin for clustering and dereplicating with vsearch
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VCS2023.9.0-1all
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License: BSD-3-clause
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Version: 2023.9.0-1

A QIIME 2 plugin that wraps the vsearch application, and provides methods for clustering and dereplicating features and sequences.

Please cite: Torbjørn Rognes, Tomáš Flouri, Ben Nichols, Christopher Quince and Frédéric Mahé: VSEARCH: a versatile open source tool for metagenomics. PeerJ 4 (2016)
r-bioc-bridgedbr
identifier mapping between biological databases
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License: MIT
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Version: 2.4.0+dfsg-1

Use BridgeDb functions and load identifier mapping databases in R.

r-cran-drinsight
drug repurposing on transcriptome sequencing data
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License: GPL-2
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Version: 0.1.1-1

The package's name is an acronym for "Drug Repurposing Integration and Systematic Investigation of Genomic High Throughput Data", which pretty much describes it: This is a connectivity mapping-based drug repurposing tool that identifies drugs that can potentially reverse query disease phenotype or have similar functions with query drugs.

Please cite: Jinyan Chan, Xuan Wang, Jacob A Turner, Nicole E Baldwin and Jinghua Gu: Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing. (PubMed,eprint) Bioinformatics 35(16):2818–2826 (2019)
r-other-apmswapp
GNU R Pre- and Postprocessing For Affinity Purification Mass Spectrometry
Versions of package r-other-apmswapp
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VCS1.0-1all
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License: <license>
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Version: 1.0-1

The reliable detection of protein-protein-interactions by affinity purification mass spectrometry (AP-MS) is a crucial stepping stone for the understanding of biological processes. The main challenge in a typical AP-MS experiment is to separate true interaction proteins from contaminants by contrasting counts of proteins binding to specific baits with counts of negative controls.

Please cite: Martina Fischer, Susann Zilkenat, Roman G. Gerlach, Samuel Wagner and Bernhard Y. Renard: Pre- and post-processing workflow for affinity purification mass spectrometry data. (PubMed) Journal of Proteome Research 13(5):2239-49 (2014)

No known packages available

bioclipse
platform for chemo- and bioinformatics based on Eclipse
License: Eclipse Public License (EPL) + exception
Debian package not available

The Bioclipse project is aimed at creating a Java-based, open source, visual platform for chemo- and bioinformatics based on the Eclipse Rich Client Platform (RCP). Bioclipse, as any RCP application, is based on a plugin architecture that inherits basic functionality and visual interfaces from Eclipse, such as help system, software updates, preferences, cross-platform deployment etc.

Bioclipse will provide functionality for chemo- and bioinformatics, and extension points that easily can be extended by plugins to provide added functionality. The first version of Bioclipse includes a CDK-plugin (bc_cdk) to provide a chemoinformatic backend, a Jmol-plugin (bc_jmol) for 3D-visualization and a general logging plugin. To stay updated on upcoming features, releases, new plugins etc, please register for the mailing list bioclipse-announce. The development is best followed on the Bioclipse Wiki where we document the progress and ideas of the development on a daily basis.

octace-bioinfo
Bioinformatics manipulation for Octave
License: GPL-2+
Debian package not available
 aa2int:
   Convert amino acid characters into integers.
 aminolookup:
   Convert between amino acid representations.
 cleave:
   Cleave a peptide SEQUENCE using the PATTERN at the POSITION relative to the pattern.
 int2aa
   Convert amino acid integers into characters.
 seqreverse
   Reverse a nucleotide sequence.
*Popularitycontest results: number of people who use this package regularly (number of people who upgraded this package recently) out of 236867