Debian Med Project
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Summary
Covid-19
This task exists only for tagging COVID-19 relevant cases

The Debian Med team intends to take part at the

 COVID-19 Biohackathon (April 5-11, 2020)
This task was created only for the purpose to list relevant packages.

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 Covid-19 packages

Official Debian packages with high relevance

abacas
chiude vuoti in allineamenti genomici da letture corte
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ABACAS (Algorithm Based Automatic Contiguation of Assembled Sequences, disposizione automatica in modo contiguo, basata su algoritmi, di sequenze assemblate) è pensato per organizzare in modo contiguo (allineare, ordinare, orientare), visualizzare e progettare primer rapidamente per chiudere vuoti su sequenze contigue assemblate con metodo shotgun basate su una sequenza di riferimento.

ABACAS usa MUMmer per trovare le posizioni di allineamenti e identificare sintenie di sequenze contigue assemblate rispetto al riferimento. L'output è quindi elaborato per generare una pseudomolecola prendendo in considerazione le sequenze contigue sovrapponibili e i vuoti. ABACAS genera un file di confronto che può essere usato per visualizzare sequenze contigue ordinate e orientate in ACT. Le sintenie sono rappresentate con barre rosse la cui densità di colore decresce con valori minori dell'uguaglianza percentuale tra i blocchi confrontabili. Le informazioni sulle sequenze contigue, come l'orientamento, l'uguaglianza percentuale, la copertura e la sovrapposizione con altre sequenze contigue possono anche essere visualizzate caricando il file di output con le caratteristiche in ACT.

The package is enhanced by the following packages: abacas-examples
Please cite: Samuel Assefa, Thomas M. Keane, Thomas D. Otto, Chris Newbold and Matthew Berriman: ABACAS: algorithm-based automatic contiguation of assembled sequences. (PubMed,eprint) Bioinformatics 25(15):1968-1969 (2009)
Topics: Probes and primers
abyss
assemblatore di sequenze, parallelo, de novo per letture corte
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ABySS è un assemblatore di sequenze, parallelo, de novo che è progettato per letture corte. Può essere usato per assemblare dati di sequenza di trascrittoma o genoma. La parallelizzazione è ottenuta usando MPI, OpenMP e pthread.

Please cite: Shaun D. Jackman, Benjamin P. Vandervalk, Hamid Mohamadi, Justin Chu, Sarah Yeo, S. Austin Hammond, Golnaz Jahesh, Hamza Khan, Lauren Coombe, Rene L. Warren and İnanç Birol: "ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter". (PubMed,eprint) Genome Research 27(5):768-777 (2017)
Registry entries: Bio.tools  SciCrunch  Bioconda 
Topics: Sequence assembly
allelecount
algoritmi per numeri di copie NGS
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Codice di supporto per algoritmi per numeri di copie NGS. Prende un file di posizioni e un file [cr|b]am e genera un conteggio di copertura di ciascun allele [ACGT] in tale posizione (con qualsiasi impostazione di filtro).

Il pacchetto alleleCount esiste principalmente per evitare duplicazione di codice tra alcuni altri progetti, specificamente AscatNGS e Battenberg.

Registry entries: Bioconda 
assembly-stats
ottiene statistiche sugli assemblaggi da file FASTA e FASTQ
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Ottiene statistiche da un elenco di file.

La rilevazione del formato FASTA o FASTQ di ciascun file è automatica in base al contenuto dei file, perciò i nomi e le estensioni dei file sono irrilevanti.

Il formato di output predefinito è intellegibile. Si può cambiare il formato di output e ignorare le sequenze più corte di una lunghezza specificata.

Registry entries: Bioconda 
augur
componenti della catena di elaborazione di dati per analisi in tempo reale di virus
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Il progetto nextstrain è un tentativo di creare strumenti di visualizzazione e catene di elaborazione di dati informatici flessibili per tenere traccia dell'evoluzione in corso di patogeni mano a mano che emergono i dati delle sequenze. Il progetto nextstrain deriva da nextflu, che era specifico per l'evoluzione dell'influenza.

nextstrain è composto da tre componenti:

  • fauna: database e script di IO per dati seriologici e di sequenze;
  • augur: catene di elaborazione di dati informatiche per fare inferenza da dati grezzi;
  • auspice: applicazione web per visualizzare le inferenze risultanti.
bamclipper
rimuove sequenze primer specifiche di geni da allineamenti SAM/BAM
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Rimuove sequenze primer specifiche di geni da allineamenti SAM/BAM di ampliconi di PCR con soft-clipping.

bamclipper.sh fa il soft-clip di primer specifici di geni da file di allineamenti BAM basati su coordinate genomiche di coppie di primer in formato BEDPE.

Please cite: Chun Hang Au, Dona N Ho, Ava Kwong, Tsun Leung Chan and Edmond S K Ma: BAMClipper: removing primers from alignments to minimize false-negative mutations in amplicon next-generation sequencing. (PubMed,eprint) Scientific Reports 7(1):1567 (2017)
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bamkit
strumenti per comuni manipolazioni di file BAM
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Questo pacchetto fornisce alcuni strumenti Python 3 per manipolazioni comuni di file BAM.

bbmap
BBTools genomic aligner and other tools for short sequences
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The BBTools are a collection of small programs to solve recurrent tasks for the creative handling of short biological RNA/DNA sequences. This suite may be best known for its mapper, which is also the name of the project on sourceforge, but several tools have been added over time. All tools are multi-threaded, implemented platform-independently in Java:

BBMap: Short read aligner for DNA and RNA-seq data. Capable of handling arbitrarily large genomes with millions of scaffolds. Handles Illumina, PacBio, 454, and other reads; very high sensitivity and tolerant of errors and numerous large indels.

BBNorm: Kmer-based error-correction and normalization tool.

Dedupe: Simplifies assemblies by removing duplicate or contained subsequences that share a target percent identity.

Reformat: Reformats reads between fasta/fastq/scarf/fasta+qual/sam, interleaved/paired, and ASCII-33/64, at over 500 MB/s.

BBDuk: Filters, trims, or masks reads with kmer matches to an artifact/contaminant file.

The package is enhanced by the following packages: multiqc
Please cite: Brian Bushnell, Jonathan Rood and Esther Singer: BBMerge – Accurate paired shotgun read merging via overlap. (PubMed,eprint) PLOS One (2017)
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bcalm
compattamento de Bruijn in poca memoria
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Uno strumento di bioinformatica per costruire il grafo compattato de Bruijn dai dati di sequenziamento.

Questa è la versione parallela del software BCALM che usa la libreria gatb-core.

Please cite: Rayan Chikhi, Antoine Limasset and Paul Medvedev: Compacting de Bruijn graphs from sequencing data quickly and in low memory.. (eprint) Bioinformatics 32(12):208 (2016)
bcftools
identificazione di varianti genomiche e manipolazione di file VCF/BCF
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upstream1.21
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BCFtools è un insieme di utilità che manipolano identificazioni di varianti nel formato Variant Call Format (VCF) e nella sua controparte binaria BCF. Tutti i comandi funzionano in modo trasparente sia con VCF sia con BCF, compressi con BGZF e non.

The package is enhanced by the following packages: multiqc
Please cite: Petr Danecek and Shane A. McCarthy: BCFtools/csq: Haplotype-aware variant consequences. (2016)
Registry entries: Bio.tools  SciCrunch  Bioconda 
bedtools
suite di utilità per confrontare tratti genomici
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Le utilità BEDTools permettono di affrontare compiti comuni negli studi genomici, come trovare i tratti che si sovrappongono e calcolare la copertura. Le utilità sono largamente basate su quattro formati di file di largo uso: BED, GFF/GTF, VCF e SAM/BAM. Usando BEDTools si possono sviluppare pipe sofisticate che rispondono a domande di ricerca complesse mediante l'uso di svariati BEDTools in un flusso comune.

L'utilità groupBy è distribuita nel pacchetto filo.

Please cite: Aaron R. Quinlan and Ira M. Hall: BEDTools: a flexible suite of utilities for comparing genomic features. (PubMed,eprint) Bioinformatics 26(6):841-842 (2010)
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biobambam2
strumenti per elaborare file di allineamento a livello iniziale
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Questo pacchetto contiene alcuni strumenti per elaborare file BAM e include:

  bamsormadup:       ordinamento parallelo e marcatura di duplicati;
  bamcollate2:       legge BAM e scrive BAM riordinati in modo che siano
                     allineati o accorpati in base al nome
                     dell'interrogazione;
  bammarkduplicates: legge BAM e scrive BAM con allineamenti duplicati
                     segnati usando il campo dei flag di BAM;
  bammaskflags:      legge BAM e scrive BAM mascherando (rimuovendo) bit
                     dalla colonna dei flag;
  bamrecompress:     legge BAM e scrive BAM con un'impostazione di
                     compressione definita; questo strumento è in grado di
                     usare il multi-threading;
  bamsort:           legge BAM e scrive BAM riordinati per coordinate o per
                     nome dell'interrogazione;
  bamtofastq:        legge BAM e scrive FastQ; l'output può essere confrontato
                     * meno con il nome dell'interrogazione.
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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bowtie2
allineatore di letture brevi ultraveloce ed efficiente
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Uno strumento ultraveloce che usa la memoria in maniera efficiente per allineare letture di sequenziamento a sequenze lunghe di riferimento. È particolarmente adatto per allineare letture da circa 50 fino a centinaia o migliaia di caratteri e particolarmente adatto per allineare a genomi relativamente lunghi (es. mammiferi).

Bowtie 2 indicizza il genoma con un indice FM per mantenere bassa l'impronta di memoria: per il genoma umano, la sua impronta di memoria è tipicamente intorno a 3,2 GB. Bowtie 2 gestisce le modalità di allineamento con gap, locali e a estremità accoppiate.

The package is enhanced by the following packages: bowtie2-examples multiqc
Please cite: Ben Langmead and Steven L Salzberg: Fast gapped-read alignment with Bowtie 2. (PubMed) Nature Methods 9:357–359 (2012)
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Topics: Genomics
busco
insiemi per benchmark di ortologhi universali a singola copia
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Valutazione della completezza dell'assemblaggio di genomi e delle annotazioni con BUSCO (Benchmarking Universal Single-Copy Orthologs).

  • Selezione automatica di linee ottenute da https://www.orthodb.org/
  • Scaricamento automatico di tutti gli insiemi di dati e i file necessari per effettuare una elaborazione
  • Uso di prodigal per genomi non eucariotici.
The package is enhanced by the following packages: multiqc
Please cite: Mathieu Seppey, Mosè Manni and Evgeny M. Zdobnov: BUSCO: Assessing Genome Assembly and Annotation Completeness. (PubMed) Methods Mol Biol. 1962:227-245 (2019)
Registry entries: Bio.tools  Bioconda 
bustools
programma per manipolare file BUS per insiemi di dati RNA-Seq di cellule singole
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Questo pacchetto contiene il programma BUStools; può essere usato per correggere gli errori nei codici a barre, collassare UMI, produrre conteggi di geni o matrici di conteggio di compatibilità di trascritti.

Please cite: Páll Melsted, A. Sina Booeshaghi, Fan Gao, Eduardo Beltrame, Lambda Lu, Kristján Eldjárn Hjorleifsson, Jase Gehring and Lior Pachter: Modular and efficient pre-processing of single-cell RNA-seq.. BioRxiv :673285 (2019)
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bwa
Burrows-Wheeler Aligner
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BWA è un pacchetto software per mappare sequenze con bassa divergenza rispetto a vasti genomi di riferimento, come il genoma umano. Consiste di tre algoritmi: BWA-backtrack, BWA-SW e BWA-MEM. Il primo è progettato per sequenze Illumina fino a 100pb, mentre gli altri due per sequenze più lunghe da 70pb a 1Mpb. BWA-MEM e BWA-SH condividono funzionalità simili, come gestione per lunghe sequenze e allineamenti spezzati, ma BWA-MEM, che è il più recente, è generalmente quello raccomandato per interrogazioni di alta qualità perché più veloce e più accurato. BWA-MEM ha prestazioni migliori anche rispetto a BWA-backtrack per sequenze Illumina 70-100pb.

Please cite: Heng Li and Richard Durbin: Fast and accurate short read alignment with Burrows-Wheeler transform. (PubMed,eprint) Bioinformatics 25(14):1754-1760 (2009)
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cat-bat
taxonomic classification of contigs and metagenome-assembled genomes (MAGs)
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Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies. The core algorithm of both programs involves gene calling, mapping of predicted ORFs against the nr protein database, and voting-based classification of the entire contig / MAG based on classification of the individual ORFs. CAT and BAT can be run from intermediate steps if files are formatted appropriately.

Please cite: F. A. Bastiaan von Meijenfeldt, Ksenia Arkhipova, Diego D. Cambuy, Felipe H. Coutinho and Bas E. Dutilh: Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. (PubMed,eprint) Genome Biology 20(1):217 (2019)
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centrifuge
sistema rapido ed efficiente nell'uso della memoria per classificare sequenze di DNA
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Centrifuge è un sistema molto rapido e che usa la memoria in maniera efficiente per la classificazione di sequenze di DNA da campioni microbici, con migliore sensibilità e accuratezza paragonabile rispetto ad altri sistemi all'avanguardia. Il sistema usa un innovativo schema di indicizzazione basato sulla trasformata di Burrows-Wheeler (BWT) e l'indice Ferragina-Manzini (FM), ottimizzati specificamente per il problema della classificazione metagenomica. Centrifuge richiede un indice relativamente piccolo (es. 4,3 GB per ~4100 genomi batterici) ma fornisce una velocità di classificazione molto alta, permettendogli di elaborare una tipica esecuzione di sequenziamento di DNA entro un'ora. Insieme, questi progressi permettono analisi tempestive e accurate di vasti insiemi di dati metagenomici su normali computer da tavolo.

Please cite: Daehwan Kim, Li Song, Florian P. Breitwieser and Steven L. Salzberg: Centrifuge: rapid and sensitive classification of metagenomic sequences. (PubMed,eprint) Genome Research 26(12):1721-1729 (2016)
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changeo
toolkit per assegnazione di repertori clonali (Python 3)
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Change-O è una raccolta di strumenti per elaborare l'output di strumenti di allineamento V(D)J, assegnando cluster clonali a sequenze di immunoglobuline (Ig) e ricostruendo sequenze di linee germinali.

Grandi miglioramenti nelle tecnologie di sequenziamento ad alte prestazioni permettono oggi la caratterizzazione su vasta scala di repertori di Ig, definiti come raccolte di proteine antigene-recettore trans-membrana situate sulla superficie delle cellule B e T. Change-O è una suite di utilità per facilitare l'analisi avanzata di sequenze di Ig e di TCR seguendo l'assegnazione di segmenti a linee germinali. Change-O gestisce l'output di IMGT/HighV-QUEST e IgBLAST, e fornisce un'ampia varietà di metodi di clustering per assegnare gruppi clonali a sequenze di Ig. Sono inclusi anche ordinamento dei record, raggruppamento e varie operazioni di manipolazione su database.

Questo pacchetto installa la libreria per Python 3.

Please cite: Namita T. Gupta, Jason A. Vander Heiden, Mohamed Uduman, Daniel Gadala-Maria, Gur Yaari and Steven H. Kleinstein: Link to publication (PubMed,eprint) Bioinformatics 31(20):3356-3358 (2015)
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chip-seq
tools performing common ChIP-Seq data analysis tasks
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The ChIP-Seq software provides a set of tools performing common genome- wide ChIP- seq analysis tasks, including positional correlation analysis, peak detection, and genome partitioning into signal-rich and signal-poor regions. These tools exist as stand-alone C programs and perform the following tasks:

 1. Positional correlation analysis and generation of an aggregation
    plot (AP) (chipcor),
 2. Extraction of specific genome annotation features around reference
    anchor points (chipextract),
 3. Read centering or shifting (chipcenter),
 4. Narrow peak caller using a fixed width peak size (chippeak),
 5. Broad peak caller used for large regions of enrichment (chippart),
 6. Feature selection tool based on a read count threshold (chipscore).

Because the ChIP-Seq tools are primarily optimized for speed, they use their own compact format for ChIP-seq data representation called SGA (Simplified Genome Annotation). SGA is a line-oriented, tab-delimited plain text format.

Please cite: Giovanna Ambrosini, René Dreos, Sunil Kumar and Philipp Bucher: The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data. (PubMed,eprint) BMC Genomics 17(1):938 (2016)
clonalframeml
efficiente inferenza di ricombinazione in genomi interi di batteri
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ClonalFrameML è un pacchetto software che esegue un'efficiente inferenza di ricombinazione in genomi di batteri. ClonalFrameML è stato creato da Xavier Didelot e Daniel Wilson. ClonalFrameML può essere applicato a qualsiasi tipo di dati di sequenze allineate, ma è specialmente orientato all'analisi di sequenze di genomi interi. È in grado di confrontare centinaia di genomi interi nell'arco di ore su un normale computer da ufficio. Ci sono tre output principali da un'esecuzione di ClonalFrameML: una filogenia con lunghezze dei rami corrette per tenere conto della ricombinazione, una stima dei parametri chiave del processo di ricombinazione e una mappa genomica di dove la ricombinazione è avvenuta per ogni ramo della filogenia.

ClonalFrameML è un'implementazione di massima verosimiglianza del software bayesiano ClonalFrame che è stato precedentemente descritto da Didelot e Falush (2007). Il modello di ricombinazione alla base di ClonalFrameML è esattamente lo stesso di ClonalFrame, ma questa nuova implementazione è molto più veloce, è in grado di affrontare insiemi di dati genomici molto più grandi e non soffre dei problemi di convergenza di MCMC.

Please cite: Xavier Didelot and Daniel J. Wilson: ClonalFrameML: Efficient Inference of Recombination in Whole Bacterial Genomes. (PubMed,eprint) PLoS Comput Biology 11(2):e1004041 (2015)
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cutadapt
pulisce sequenze biologiche provenienti da letture di sequenziamento ad alte prestazioni
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Cutadapt aiuta nei compiti di pulitura di sequenze biologiche trovando le sequenze adattatore o primer in maniera tollerante agli errori. Può anche modificare e filtrare in vari modi le letture. Le sequenze adattatore possono contenere metacaratteri IUPAC. Inoltre sono gestite letture con estremità accoppiate e anche dati sullo spazio dei colori. Se lo si desidera, si può anche solamente fare il demultiplex dei dati in input, senza rimuovere alcuna sequenza adattatore.

Questo pacchetto contiene l'interfaccia utente.

The package is enhanced by the following packages: multiqc
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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cwltool
implementazione di riferimento di Common Workflow Language
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Questa è l'implementazione di riferimento degli standard del Common Workflow Language.

Gli standard aperti CWL servono a descrivere flussi di lavoro e strumenti di analisi in un modo che li renda portabili e scalabili su una varietà di ambienti software e hardware, dalle workstation a cluster, cloud e ambienti HPC (High Performance Computing). CWL è progettato per soddisfare le esigenze di scienze con grande uso di dati, come la bioinformatica. le immagini medicali, l'astronomia, la fisica e la chimica.

L'implementazione di riferimento di CWL (cwltool) è pensata per essere ricca di funzionalità e per fornire una validazione completa di file CWL, oltre a fornire altri strumenti correlati al lavoro con descrizioni CWL.

Please cite: Michael R. Crusoe, Sanne Abeln, Alexandru Iosup, Peter Amstutz, John Chilton, Nebojša Tijanić, Hervé Ménager, Stian Soiland-Reye, Bogdan Gavrilović, Carole Goble and The CWL Community: Methods included: standardizing computational reuse and portability with the Common Workflow Language. Communications of the ACM 65(6):54-63 (2022)
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dcmtk
programmi a riga di comando del toolkit OFFIS DICOM
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DCMTK è una raccolta di librerie e applicazioni per esaminare, costruire e convertire file immagine DICOM, maneggiare supporti offline, inviare e ricevere immagini tramite connessione di rete, così come esempi di server per archiviazione di immagini e worklist.

Questo pacchetto contiene le applicazioni di utilità di DCMTK.

Nota: questa versione è stata compilata con il supporto per libssl.

Please cite: Chung-Yueh Lien, Michael Onken, Marco Eichelberg, Tsair Kao and Andreas Hein: Open Source Tools for Standardized Privacy Protection of Medical Images. (eprint) Progress in Biomedical Optics and Imaging - Proceedings of SPIE 7967:79670M-79670M (2011)
delly
Structural variant discovery by read analysis
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Delly performs Structural variant discovery by integrated paired-end and split-read analysis. It discovers, genotypes and visualizes deletions, tandem duplications, inversions and translocations at single-nucleotide resolution in short-read massively parallel sequencing data. It uses paired-ends, split-reads and read-depth to sensitively and accurately delineate genomic rearrangements throughout the genome.

Please cite: Tobias Rausch, Thomas Zichner, Andreas Schlattl, Adrian M. Stuetz, Vladimir Benes and Jan O. Korbel: DELLY: structural variant discovery by integrated paired-end and split-read analysis.. Bioinformatics 28:i333-i339 (2012)
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dextractor
libreria dextractor di comandi di estrazione e compressione
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I comandi di dextractor permettono di recuperare esattamente e solamente le informazioni necessarie per assemblato e ricostruzione dai file sorgenti HDF5 prodotti dal sequenziatore PacBio RS II, o dai file sorgenti BAM prodotti dal sequenziatore PacBio Sequel.

Per ciascuno dei tre tipi di file estratti (fasta, quiva e arrow) la libreria contiene comandi per comprimere il tipo di file dato e per decomprimerlo, che è un processo reversibile che restituisce il file originale non compresso. I file .fasta compressi, con l'estensione .dexta, consumano 1/4 di byte per base. I file .quiva compressi, con l'estensione .dexqv, consumano 1,5 byte per base in media, e i file .arrow compressi, con l'estensione .dexar, consumano 1/4 di byte per base.

Per ulteriori informazioni, consultare la documentazione disponibile all'indirizzo https://github.com/thegenemyers/DEXTRACTOR.

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diamond-aligner
allineatore di sequenze locali accelerato compatibile con BLAST
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DIAMOND è un allineatore di sequenze per ricerche su proteine e DNA tradotto e funziona come sostituto perfetto per gli strumenti software NCBI BLAST. È adatto per ricerche proteina-proteina e per ricerche DNA-proteina su letture corte e sequenze più lunghe inclusi contig e assemblaggi, fornendo una velocizzazione di BLAST fino a 20.000 volte.

Please cite: Benjamin Buchfink, Chao Xie and Daniel H Huson: Fast and sensitive protein alignment using DIAMOND. (PubMed) Nature methods 12(1):59-60 (2015)
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discosnp
scopre Single Nucleotide Polymorphism da uno o più insiemi di letture grezze
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Il software discoSnp è progettato per scoprire SNP (Single Nucleotide Polymorphism - polimorfismo di nucleotide singolo) da uno o più insiemi di letture grezze ottenute tramite NGS (Next Generation Sequencers - sequenziatori di prossima generazione).

Notare che il numero di insiemi di letture in input non è vincolato, può essere uno, due o più. Notare anche che non sono necessari ulteriori dati come annotazioni o genoma di riferimento.

Il software è composto da due moduli. Il primo modulo, kissnp2, rileva gli SNP dagli insiemi di letture. Un secondo modulo, kissreads, potenzia i risultati di kissnp2 calcolando, per ogni insieme di letture e per ogni SNP trovato:

 1) la sua copertura di letture media,
 2) la qualità (phred) di letture che generano il polimorfismo.

Questo pacchetto è stato sorpassato da DiscoSnp++.

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drop-seq-tools
analisi di dati Drop-seq
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Questo software fornisce l'analisi computazionale di base per dati Drop-seq, che mostra come trasformare dati di sequenze grezze in una misura di espressione per ciascun gene in ciascuna cellula individuale.

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fasta3
tools for searching collections of biological sequences
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The FASTA programs find regions of local or global similarity between Protein or DNA sequences, either by searching Protein or DNA databases, or by identifying local duplications within a sequence. Other programs provide information on the statistical significance of an alignment. Like BLAST, FASTA can be used to infer functional and evolutionary relationships between sequences as well as help identify members of gene families.

  • Protein
  • Protein-protein FASTA
  • Protein-protein Smith-Waterman (ssearch)
  • Global Protein-protein (Needleman-Wunsch) (ggsearch)
  • Global/Local protein-protein (glsearch)
  • Protein-protein with unordered peptides (fasts)
  • Protein-protein with mixed peptide sequences (fastf)

  • Nucleotide

  • Nucleotide-Nucleotide (DNA/RNA fasta)
  • Ordered Nucleotides vs Nucleotide (fastm)
  • Un-ordered Nucleotides vs Nucleotide (fasts)

  • Translated

  • Translated DNA (with frameshifts, e.g. ESTs) vs Proteins (fastx/fasty)
  • Protein vs Translated DNA (with frameshifts) (tfastx/tfasty)
  • Peptides vs Translated DNA (tfasts)

  • Statistical Significance

  • Protein vs Protein shuffle (prss)
  • DNA vs DNA shuffle (prss)
  • Translated DNA vs Protein shuffle (prfx)

  • Local Duplications

  • Local Protein alignments (lalign)
  • Plot Protein alignment "dot-plot" (plalign)
  • Local DNA alignments (lalign)
  • Plot DNA alignment "dot-plot" (plalign)

This software is often used via a web service at the EBI with readily indexed reference databases at http://www.ebi.ac.uk/Tools/fasta/.

Please cite: William R. Pearson and D. J. Lipman: Improved tools for biological sequence comparison. (PubMed,eprint) Proc Natl Acad Sci U S A 85(8):2444-8 (1988)
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fastani
calcolo veloce senza allineamento dell'identità nucleotidica media di interi genomi
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La ANI (Average Nucleotide Identity) è definita come l'identità nucleotidica media di coppie di geni ortologhi condivisi tra due genomi microbici. FastANI gestisce il confronto a coppie di assemblati genomici completi oppure in bozza.

fastp
preprocessore di FASTQ ultraveloce tutto in uno
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Preprocessore FASTQ tutto in uno, fastp fornisce funzioni che includono profilazione della qualità, taglio di adattatore, filtraggio delle letture e correzione delle basi. Gestisce letture brevi a estremità singole e accoppiate e fornisce anche il supporto di base per dati di letture lunghe.

The package is enhanced by the following packages: multiqc
Please cite: Shifu Chen, Yanqing Zhou, Yaru Chen and Jia Gu: fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17):i884-i890 (2018)
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fastqc
controllo di qualità per elevati flussi di dati di sequenze
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FastQC mira a fornire un metodo rapido per effettuare alcuni controlli di qualità su elevati flussi attraverso pipe di dati di sequenze grezzi. Fornisce un insieme modulare di analisi che consentono di avere una veloce panoramica sui dati per individuare eventuali presenze di errori o problemi che bisogna tenere in considerazione prima di utilizzarli in analisi successive.

Le funzioni principali di FastQC sono:

  • importazione di dati da file BAM, SAM o FastQ (qualsiasi variante);
  • rapida panoramica per identificare in quali aree potrebbero esserci problemi;
  • grafici e tabelle riepilogative per una valutazione rapida dei dati;
  • esportazione dei risultati in un report permanente in formato HTML;
  • modalità offline che consente la generazione di report automatici senza eseguire l'applicazione interattiva.
The package is enhanced by the following packages: multiqc
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Topics: Sequencing
filtlong
strumento per filtrare la qualità di letture lunghe di sequenze di genoma
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Filtlong è uno strumento per filtrare letture lunghe in base alla qualità. Può prendere un insieme di letture lunghe e produrre un sottoinsieme più piccolo e migliore. Usa la lunghezza della lettura (più lunga è migliore) e l'identità della lettura (più alta è migliore) quando sceglie quali letture passano il filtro.

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flash
Fast Length Adjustment of SHort reads
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FLASH (Fast Length Adjustment of SHort reads) is a very fast and accurate software tool to merge paired-end reads from next-generation sequencing experiments. FLASH is designed to merge pairs of reads when the original DNA fragments are shorter than twice the length of reads. The resulting longer reads can significantly improve genome assemblies. They can also improve transcriptome assembly when FLASH is used to merge RNA-seq data.

The package is enhanced by the following packages: multiqc
Please cite: Tanja Magoč and Steven L Salzberg: FLASH: Fast Length Adjustment of Short Reads to Improve Genome Assemblies. (PubMed,eprint) Bioinformatics 27(21):2957-2963 (2011)
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flye
de novo assembler for single molecule sequencing reads using repeat graphs
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Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PacBio / ONT reads as input and outputs polished contigs. Flye also has a special mode for metagenome assembly.

Please cite: Mikhail Kolmogorov, Jeffrey Yuan, Yu Lin and Pavel A. Pevzner: Assembly of long, error-prone reads using repeat graphs. (PubMed) Nature Biotechnology 37(5):540–546 (2019)
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freebayes
scoperta e genotipizzazione bayesiana di polimorfismo basati su aplotipi
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FreeBayes è un rilevatore bayesiano di varianti genetiche progettato per trovare piccoli polimorfismi, specificamente SNP (single-nucleotide polymorphism), indel (inserzioni e delezioni), MNP (multi-nucleotide polymorphism) ed eventi complessi (eventi di sostituzione e inserzione compositi) più piccoli della lunghezza di un allineamento di sequenziamento di letture corte.

Please cite: Erik Garrison and Gabor Marth: Haplotype-based variant detection from short-read sequencing. (eprint) arXiv (2012)
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genometools
toolkit versatile per analisi genomiche
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GenomeTools contiene una raccolta di strumenti utili per analisi e presentazione di sequenze biologiche combinati in un unico binario.

Il toolkit contiene binari per gestione di sequenze e annotazioni, compressione di sequenze, generazione e accesso a strutture indice, visualizzazione di annotazioni e molto altro.

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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gffread
conversioni di formati GFF/GTF, filtraggio di regioni, estrazione di sequenze FASTA
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Gffread è una utilità per analizzare GFF/GTF che fornisce conversioni di formato, filtraggio di regioni, estrazione di sequenze FASTA e altro.

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ginkgocadx
software per immagini medicali e visualizzatore di DICOM completo
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Ginkgo CADx fornisce una soluzione completa per visualizzatore DICOM con capacità avanzate e gestione di estensioni.

  • Interfaccia facile e personalizzabile attraverso profili.
  • Visualizzazione completa di immagini DICOM.
  • Insieme completo di strumenti (misuratori, marcatori, testo, ...).
  • Gestione di più modalità (neurologica, radiologica, dermatologica, oftalmologica, ultrasuoni, endoscopia, ...).
  • Gestione della trasformazione in DICOM di JPEG, PNG, GIF e TIFF.
  • Gestione dell'integrazione EMH completa: flussi di lavoro standard HL7 e conformi con IHE.
  • Workstation PACS (C-FIND, C-MOVE, C-STORE, ...).
  • Estensibile tramite estensioni personalizzate:
  • composizione di mosaici di immagini retinali;
  • diagnostica automatica di analisi retinali;
  • diagnostica automatica di psoriasi.
gnumed-client
gestione della pratica medica - client
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Questo è GNUmed Electronic Medical Record; il suo scopo è di permettere ai dottori di mantenere un archivio medicalmente valido sulla salute dei propri pazienti. Attualmente non fornisce funzionalità di gestione del magazzino. Le funzionalità cliniche sono ben collaudate sul campo da veri dottori.

Benché il gruppo di lavoro di GNUmed abbia posto la più grande attenzione per assicurare che l'archivio medico sia sicuro in ogni momento, è comunque necessario assicurarsi di prendere le misure idonee per fare il backup dei dati medici in un posto sicuro ad intervalli appropriati. Inoltre non dimenticare di testare le procedure di ripristino!

Proteggete i vostri dati! GNUmed è fornito senza alcuna garanzia. Siete avvertiti.

Questo pacchetto contiene il client wxpython.

The package is enhanced by the following packages: entangle gnumed-doc
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gnumed-server
gestione della pratica medica - server
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Questo è GNUmed Electronic Medical Record; il suo scopo è di permettere a dottori di mantenere un archivio medicalmente valido sulla salute dei propri pazienti. Attualmente non fornisce funzionalità per la fatturazione e la gestione del magazzino. Le funzionalità cliniche fornite sono effettivamente ben testate sul campo da medici.

Benché il gruppo di lavoro di GNUmed abbia posto la più grande attenzione per assicurare che l'archivio medico sia sicuro in ogni momento, è comunque necessario assicurarsi di prendere le misure idonee per fare il backup dei dati medici in un posto sicuro ad intervalli appropriati. Inoltre non dimenticare di testare le procedure di ripristino!

Proteggete i vostri dati! GNUmed è fornito senza alcuna garanzia. Siete avvertiti.

Questo pacchetto contiene la componente col server PostgreSQL.

Nota bene: questo pacchetto attualmente NON crea il database GNUmed, ma installa semplicemente i file SQL necessari. Si legga il file README.Debian.

gromacs
simulatore di dinamica molecolare, con strumenti di costruzione e di analisi
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GROMACS è un pacchetto versatile per l'esecuzione di dinamiche molecolari, ovvero simulazioni delle equazioni newtoniane del moto per sistemi con un numero di particelle tra le centinaia e i milioni.

Progettato principalmente per biomolecole come proteine e lipidi con molte complesse interazioni di legame, GROMACS, essendo estremamente veloce nei calcoli per le interazioni di non-legame (che solitamente dominano le simulazioni) è anche utilizzato da molti gruppi per ricerche su sistemi non biologici, come ad esempio polimeri.

Il pacchetto contiene varianti sia per l'esecuzione su una macchina singola sia per l'uso dell'interfaccia MPI su più macchine.

Please cite: Berk Hess, Carsten Kutzner, David van der Spoel and Erik Lindahl: GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. (eprint) J. Chem. Theory Comput. 4(3):435-447 (2008)
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gubbins
analisi filogenetica di sequenze di genoma
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Gubbins gestisce l'analisi filogenetica rapida di grandi campioni di sequenze di genomi completi di batteri ricombinanti.

Gubbins (Genealogies Unbiased By recomBinations In Nucleotide Sequences) è un algoritmo che iterativamente identifica i loci contenenti elevate densità di sostituzioni di base mentre contemporaneamente costruisce una filogenia basata sulle presunte mutazioni puntuali al di fuori di tali regioni. Le simulazioni dimostrano che l'algoritmo genera ricostruzioni altamente accurate in modelli realistici di evoluzione batterica nel breve periodo e può essere eseguito in poche ore su allineamenti di centinaia di sequenze di genoma batterico.

Please cite: Nicholas J. Croucher, Andrew J. Page, Thomas R. Connor, Aidan J. Delaney, Jacqueline A. Keane, Stephen D. Bentley, Julian Parkhill and Simon R. Harris: Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. (PubMed,eprint) Nucleic Acids Research 43(3):e15 (2014)
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imagej
programma di elaborazione delle immagini pensato per immagini di microscopia
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Può visualizzare, modificare, analizzare, processare, salvare e stampare immagini a 8, 16 e 32 bit. Può leggere molti formati immagine inclusi TIFF, GIF, JPEG, BMP, DICOM, FITS e "raw". Gestisce "stack": una serie di immagini che condividono un'unica finestra.

Può calcolare statistiche su valori di area e pixel di selezioni definite dall'utente. Può misurare distanze ed angoli. Può creare istogrammi di densità e grafici con linee di contorno. Gestisce le funzioni standard di elaborazione delle immagini come manipolazione del contrasto, sharpening, sfumatura, rilevamento dei bordi e filtri mediani.

La calibrazione spaziale è disponibile per fornire misure dimensionali nel mondo reale in unità quali i millimetri. È anche disponibile la calibrazione della densità o della scala di grigi.

ImageJ è sviluppato da Wayne Rasband (wayne@codon.nih.gov) al Research Services Branch del National Institute of Mental Health di Bethesda nel Maryland, USA.

Please cite: Caroline A Schneider, Wayne S Rasband and Kevin W Eliceiri: NIH Image to ImageJ: 25 years of image analysis. (PubMed,eprint) Nature methods 9:671-675 (2012)
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ivar
funzioni largamente utili per sequenziamento di virus basato su ampliconi
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iVar è un pacchetto di calcolo che contiene funzioni largamente utili per sequenziamento di virus basato su ampliconi. Strumenti aggiuntivi per sequenziamento metagenomico stanno venendo attivamente incorporati in iVar. Sebbene ognuna di queste funzioni possa essere effettuata usando strumenti esistenti, iVar contiene un'intersezione di funzionalità da strumenti multipli che sono necessarie per identificare iSNV e sequenze di consenso da dati di sequenziamento virale su replicati multipli. iVar fornisce le seguenti funzioni:

 1. ritaglio di primer e basi di bassa qualità;
 2. identificazione di consenso;
 3. identificazione di varianti, sia iSNV sia inserzioni/delezioni e
 4. identificazione di corrispondenze errate a sequenze primer ed
    esclusione delle letture corrispondenti dai file di allineamento.
The package is enhanced by the following packages: multiqc
Please cite: Nathan D. Grubaugh, Karthik Gangavarapu, Joshua Quick, Nathaniel L. Matteson, Jaqueline Goes De Jesus, Bradley J. Main, Amanda L. Tan, Lauren M. Paul, Doug E. Brackney, Saran Grewal, Nikos Gurfield, Koen K. A. Van Rompay, Sharon Isern, Scott F. Michael, Lark L. Coffey, Nicholas J. Loman and Kristian G. Andersen: An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar. (PubMed,eprint) Genome Biology 20(1):8 (2019)
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kalign
allineamenti multipli di sequenza globali e progressivi
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Kalign è uno strumento a riga di comando per fare allineamenti multipli di sequenze biologiche. Usa l'algoritmo per corrispondenze di stringhe Muth-Manber per migliorare sia l'accuratezza che la velocità dell'allineamento. Usa un approccio globale, progressivo per l'allineamento, arricchito dall'uso di un algoritmo per corrispondenze approssimate di stringhe per calcolare le distanze tra le sequenze e incorporando corrispondenze locali nell'allineamento globale.

Please cite: Lassmann, Timo.: Kalign 3: multiple sequence alignment of large datasets. (eprint) Bioinformatics 36(6):1928-1929 (2020)
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kallisto
near-optimal RNA-Seq quantification
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Kallisto is a program for quantifying abundances of transcripts from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data, kallisto can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build. Pseudoalignment of reads preserves the key information needed for quantification, and kallisto is therefore not only fast, but also as accurate than existing quantification tools. In fact, because the pseudoalignment procedure is robust to errors in the reads, in many benchmarks kallisto significantly outperforms existing tools.

The package is enhanced by the following packages: multiqc
Please cite: Nicolas L Bray, Harold Pimentel, Páll Melsted and Lior Pachter: Near-optimal probabilistic RNA-seq quantification. (PubMed) Nature Biotechnology 34(5):525–527 (2016)
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kraken2
taxonomic classification system using exact k-mer matches
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Kraken 2 is the newest version of Kraken, a taxonomic classification system using exact k-mer matches to achieve high accuracy and fast classification speeds. This classifier matches each k-mer within a query sequence to the lowest common ancestor (LCA) of all genomes containing the given k-mer. The k-mer assignments inform the classification algorithm. [see: Kraken 1's Webpage for more details].

Kraken 2 provides significant improvements to Kraken 1, with faster database build times, smaller database sizes, and faster classification speeds. These improvements were achieved by the following updates to the Kraken classification program:

 1. Storage of Minimizers: Instead of storing/querying entire k-mers,
    Kraken 2 stores minimizers (l-mers) of each k-mer. The length of
    each l-mer must be ≤ the k-mer length. Each k-mer is treated by
    Kraken 2 as if its LCA is the same as its minimizer's LCA.
 2. Introduction of Spaced Seeds: Kraken 2 also uses spaced seeds to
    store and query minimizers to improve classification accuracy.
 3. Database Structure: While Kraken 1 saved an indexed and sorted list
    of k-mer/LCA pairs, Kraken 2 uses a compact hash table. This hash
    table is a probabilistic data structure that allows for faster
    queries and lower memory requirements. However, this data structure
    does have a <1% chance of returning the incorrect LCA or returning
    an LCA for a non-inserted minimizer. Users can compensate for this
    possibility by using Kraken's confidence scoring thresholds.
 4. Protein Databases: Kraken 2 allows for databases built from amino
    acid sequences. When queried, Kraken 2 performs a six-frame
    translated search of the query sequences against the database.
 5. 16S Databases: Kraken 2 also provides support for databases not
    based on NCBI's taxonomy. Currently, these include the 16S
    databases: Greengenes, SILVA, and RDP.
Please cite: Derrick E Wood and Steven L Salzberg: Kraken: ultrafast metagenomic sequence classification using exact alignments. (PubMed,eprint) Genome Biol. 15(3):R46 (2014)
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lastz
allineamento di coppie di sequenze di DNA
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LASTZ è un sostituto perfetto per BLASTZ ed è compatibile all'indietro con la sintassi della riga di comando di BLASTZ. Cioè supporta tutte le opzioni di BLASTZ, ma ne ha anche di aggiuntive e può produrre risultati di allineamento leggermente differenti.

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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)
libchipcard-dev
API per lettori di smart card
Maintainer: Micha Lenk
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libchipcard fornisce un'API per accedere a smart card. Alcuni esempi sono schede di memoria, oltre a carte HBCI (home banking) e le tedesche GeldKarte (contante elettronico) e KVK (assicurazione sanitaria).

Questo pacchetto contiene i file di sviluppo per libchipcard.

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.

libgdcm-tools
strumenti e utilità per Grassroots DiCoM
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Grassroots DiCoM è una libreria C++ per file medicali DICOM. È inglobata automaticamente in Python/C#/Java (tramite swig). Gestisce RAW, JPEG (con e senza perdita di dati), J2K, JPEG-LS, RLE e deflated.

Installare questo pacchetto per i programmi: gdcmanon, gdcmclean, gdcmconv, gdcmdiff, gdcmdump, gdcmpap3, gdcmgendir, gdcmimg, gdcminfo, gdcmpdf, gdcmraw, gdcmscanner, gdcmscu, gdcmtar, gdcmxml.

Please cite: David Rodríguez González, Trevor Carpenter, Jano I. van Hemert and Joanna Wardlaw: An open source toolkit for medical imaging de-identification. (PubMed,eprint) European Radiology 20(8):1896-1904 (2010)
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

libics-dev
Image Cytometry Standard file reading and writing (devel)
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This is the reference library for ICS (Image Cytometry Standard), an open standard for writing images of any dimensionality and data type to file, together with associated information regarding the recording equipment or recorded subject.

This package contains the libraries needed to build ICS applications.

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
libreria per elaborazione di dati di Next Generation Sequencing (NGS)
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Pacchetto ausiliare in Java adottato da una gamma di strumenti open source per l'analisi di repertori di cellule B e T.

libseqan3-dev
libreria C++ per l'analisi di sequenze biologiche v3 (sviluppo)
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SeqAn è una libreria di template C++ di algoritmi e strutture dati efficienti per l'analisi di sequenze, pensata principalmente per i dati biologici. Questa libreria applica un modello progettuale generico unico che garantisce alte prestazioni, generalità, estensibilità e integrazione con le altre librerie. SeqAn è facile da usare e semplifica lo sviluppo di nuovi strumenti software con minime perdite in termini di prestazioni.

Questo pacchetto contiene i file di sviluppo.

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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lighter
fast and memory-efficient sequencing error corrector
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Lighter is a fast, memory-efficient tool for correcting sequencing errors. Lighter avoids counting k-mers. Instead, it uses a pair of Bloom filters, one holding a sample of the input k-mers and the other holding k-mers likely to be correct. As long as the sampling fraction is adjusted in inverse proportion to the depth of sequencing, Bloom filter size can be held constant while maintaining near-constant accuracy. Lighter is parallelized, uses no secondary storage, and is both faster and more memory-efficient than competing approaches while achieving comparable accuracy.

lumpy-sv
infrastruttura probabilistica generica per scoperta di varianti strutturali
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LUMPY, un'innovativa infrastruttura per scoperta di SV che integra naturalmente segnali SV multipli unitamente attraverso campioni multipli. LUMPY produce sensibilità migliorata, specialmente quando il segnale SV è ridotto a causa della bassa copertura dei dati o della bassa frequenza di varianti di alleli nei campioni.

The package is enhanced by the following packages: lumpy-sv-examples
mecat2
ultra-fast and accurate de novo assembly tools for SMRT reads
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An improved version of MECAT. It is an ultra-fast and accurate mapping and error correcting de novo assembly tools for single molecula sequencing (SMRT) reads. MECAT2 consists of the following three modules:

 1. mecat2map: a fast and accurate alignment tool for SMRT reads.
 2. mecat2cns: correct noisy reads based on their pairwise overlaps.
 3. fsa: a string graph based assembly tool.
Please cite: Chuan-Le Xiao, Ying Chen, Shang-Qian Xie, Kai-Ning Chen, Yan Wang, Yue Han, Feng Luo and Zhi Xie: MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads. Nature Methods 14(11):1078 (2017)
megahit
ultra-fast and memory-efficient meta-genome assembler
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Megahit is an ultra-fast and memory-efficient NGS assembler. It is optimized for metagenomes, but also works well on generic single genome assembly (small or mammalian size) and single-cell assembly.

The software was praised in a Briefings in Bioinformatics 5/2020 review (DOI: 10.1093/bib/bbaa085).

Please cite: Dinghua Li, Chi-Man Liu, Ruibang Luo, Kunihiko Sadakane and Tak-Wah Lam: MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. (PubMed) 31:1674-1676 (2015)
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metabat
robust statistical framework for reconstructing genomes from metagenomic data
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MetaBAT integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency for accurate metagenome binning. MetaBAT outperforms alternative methods in accuracy and computational efficiency on both synthetic and real metagenome datasets. It automatically forms hundreds of high quality genome bins on a very large assembly consisting millions of contigs in a matter of hours on a single node.

Please cite: Dongwan D. Kang, Jeff Froula, Rob Egan and Zhong Wang: MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. (PubMed) PeerJ 3:e1165 (2015)
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minia
assemblatore di sequenze biologiche a letture corte
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Fino all'anno 2020 ciò che veniva chiamato sequenziamento di DNA di prossima generazione ("next-generation") produceva solo letture "corte", fino a ~600 paia basi in lunghezza, che poi dovevano essere unite come un puzzle, mediante sovrapposizioni casuali nella loro sequenza, fino ad un genoma completo. Questo è l'assemblamento di un genoma. Ci sono molti tranelli biologici in lunghi tratti di regioni a bassa complessità e variazioni nel numero di copie e altri tipi di ridondanze che lo rendono difficile.

Questo pacchetto contiene un assemblatore di sequenze di DNA a letture corte basato su un grafo di de Bruijn, capace di assemblare un genoma umano su un computer desktop in un giorno.

L'output di Minia è un insieme di contig, cioè tratti di sovrapposizioni lineari di letture corte privi di gap. Nel migliore caso possibile è un intero cromosoma.

Minia produce risultati con contiguità e accuratezza simili ad altri assemblatori di de Bruijn (es. Velvet).

Please cite: Rayan Chikhi and Guillaume Rizk: Space-Efficient and Exact de Bruijn Graph Representation Based on a Bloom Filter.. (PubMed,eprint) Algorithms for Molecular Biology 8(1):22 (2013)
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Topics: Sequence assembly
minimap2
allineatore a coppie versatile per sequenze genomiche e nucleotidiche con splice
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Minimap2 è un versatile programma per l'allineamento di sequenze che allinea sequenze di DNA o mRNA rispetto a un grande database di riferimento. Casi d'uso tipici includono: (1) mappatura di letture genomiche PacBio o Oxford Nanopore con il genoma umano; (2) ricerca di sovrapposizioni tra letture lunghe con tassi di errore fino a ~15%; (3) allineamento, tenendo conto degli splice, di letture PacBio Iso-Seq o Nanopore cDNA o Direct RNA rispetto a un genoma di riferimento; (4) allineamento di letture Illumina singole o accoppiate; (5) allineamento assemblaggio-verso-assemblaggio; (6) allineamento dell'intero genoma tra due specie strettamente imparentate con divergenza sotto a ~15%.

Per sequenze di letture di circa 10kb con rumore, minimap2 è decine di volte più veloce dei mappatori di letture lunghe più comuni, come BLASR, BWA-MEM, NGMLR e GMAP. È più accurato su letture lunghe simulate e produce allineamenti biologicamente significativi pronti per le analisi successive. Per letture corte Illumina >100bp, minimap2 è tre volte più veloce di BWA-MEM e Bowtie2, e altrettanto accurato su dati simulati. Valutazioni dettagliate sono disponibili nell'articolo o il preprint su minimap2.

Please cite: Heng Li: Minimap2: pairwise alignment for nucleotide sequences. (PubMed,eprint) Bioinformatics :2103-2110 (2018)
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mmb
modella la struttura e la dinamica di macromolecole
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MacroMoleculeBuilder, precedentemente conosciuto come RNABuilder, può essere usato per trasformazione, modellazione di omologie, ripiegamento (es. usando i contatti delle coppie di basi), ridisegno dei complessi, fit su mappe di densità a bassa risoluzione, predire riarrangiamenti locali in seguito a mutazione e molte altre applicazioni.

mmseqs2
raggruppamento in cluster e ricerca di proteine ultra-veloce e sensibile
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MMseqs2 (ricerca di sequenze Many-against-Many, molti-contro-molti) è una suite software per cercare e fare cluster di enormi insiemi di sequenze proteiche/nucleotidiche. MMseqs2 è software open source con licenza GPL implementato in C++ per Linux, MacOS e (come versione beta attraverso cygwin) Windows. Il software è progettato per essere eseguito su core e server multipli e dimostra una scalabilità molto buona. L'esecuzione di MMseqs2 può essere 10.000 volte più veloce di BLAST. A 100 volte la sua velocità ottiene quasi la stessa sensibilità. Può effettuare ricerche di profili con la stessa sensibilità di PSI-BLAST a una velocità più di 400 volte superiore.

Please cite: Martin Steinegger and Johannes Söding: Clustering huge protein sequence sets in linear time. Nature Communications 9(1) (2018)
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multiqc
output integration for RNA sequencing across tools and samples
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The sequencing of DNA or RNA with current high-throughput technologies involves an array of tools and these are applied over a range of samples. It is easy to loose oversight. And gathering the data and forwarding them in a readable manner to the individuals who took the samples is a challenge for a tool in itself. Well. Here it is. MultiQC aggregates the output of multiple tools into a single report.

Reports are generated by scanning given directories for recognised log files. These are parsed and a single HTML report is generated summarising the statistics for all logs found. MultiQC reports can describe multiple analysis steps and large numbers of samples within a single plot, and multiple analysis tools making it ideal for routine fast quality control.

Please cite: Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller: MultiQC: summarize analysis results for multiple tools and samples in a single report. (PubMed,eprint) Bioinformatics 31(19):3047-8 (2016)
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muscle
programma per allineamenti multipli di sequenze proteiche
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MUSCLE è un programma di allineamenti multipli di sequenze proteiche; il nome sta per MUltiple Sequence Comparison by Log-Expectation, confronto di sequenze multiple tramite log-expectation. Nei test fatti dagli autori, MUSCLE ha ottenuto i punteggi più alti tra tutti i programmi sottoposti a diversi benchmark per l'accuratezza degli allineamenti ed è anche uno dei programmi più veloci tra quelli disponibili.

Muscle v5 è una riscrittura con grandi modifiche di MUSCLE, basata su nuovi algoritmi.

Gli utenti dovrebbero considerare che gli argomenti della riga di comando sono cambiati rispetto alla versione 3.x di MUSCLE.

Maggiore accuratezza, scalabile a migliaia di sequenze

In confronto alle versioni precedenti, Muscle v5 è più accurato, è spesso più veloce e scalabile a insiemi di dati molto più grandi. Al momento della stesura di questo testo (fine 2021), Muscle v5 ha il più alto punteggio nei benchmark per allineamenti multipli, inclusi Balibase, Bralibase, Prefab e Balifam. Può allineare decine di migliaia di sequenze con alta accuratezza su un computer comune a basso costo (ad esempio una CPU Intel a 8 core con 32 GB di RAM). Su insiemi di dati più grandi, Muscle v5 è più accurato del 20-30% rispetto a MAFFT e Clustal-Omega.

Insiemi di allineamenti

Muscle v5 può generare insiemi di allineamenti alternativi ad alta accuratezza. Tutti i replicati hanno uguale accuratezza media nei test benchmark, incluso l'MSA fatto con i parametri predefiniti. Confrontando i risultati delle analisi successive (alberi, predizione di struttura, ...) su replicati diversi, si possono valutare gli effetti degli errori di allineamento sui propri studi.

Please cite: Robert C. Edgar: MUSCLE: multiple sequence alignment with high accuracy and high throughput. (PubMed,eprint) Nucleic Acids Research 32(5):1792-1797 (2004)
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Topics: Sequence analysis
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muscle3
programma per allineamenti multipli di sequenze proteiche
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MUSCLE è un programma di allineamenti multipli di sequenze proteiche; il nome sta per MUltiple Sequence Comparison by Log-Expectation, confronto di sequenze multiple tramite log-expectation. Nei test fatti dagli autori, MUSCLE ha ottenuto i punteggi più alti tra tutti i programmi sottoposti a diversi benchmark per l'accuratezza degli allineamenti ed è anche uno dei programmi più veloci tra quelli disponibili.

Questa è la versione 3 del programma MUSCLE. È un programma diverso da MUSCLE versione 5 che è pacchettizzato in Debian come muscle.

Please cite: Robert C. Edgar: MUSCLE: multiple sequence alignment with high accuracy and high throughput. (PubMed,eprint) Nucleic Acids Research 32(5):1792-1797 (2004)
Registry entries: Bio.tools  SciCrunch  Bioconda 
Topics: Sequence analysis
nanofilt
filtraggio e ritaglio di dati di sequenziamento con letture lunghe
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Filtraggio e ritaglio di dati di sequenziamento con letture lunghe. Filtraggio in base a qualità o lunghezza delle letture e ritaglio opzionale dopo aver passato i filtri. Legge dallo stdin e scrive sullo stdout. Opzionalmente legge direttamente da un file non compresso specificato nella riga di comando.

Pensato per essere usato:

 1. direttamente dopo l'estrazione di fastq
 2. prima della mappatura
 3. in un flusso tra l'estrazione e la mappatura
Please cite: Wouter De Coster, Svenn D'Hert, Darrin T. Schultz and Christine Van Broeckhoven: NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34 (2018)
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nanolyse
rimuove le letture del fago lambda da un file fastq
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NanoLyse è uno strumento per la rimozione rapida di DNA contaminante, che usa l'allineatore Minimap2 attraverso il collegamento Python mappy. Un'applicazione tipica è la rimozione del frammento di DNA di controllo del fago lambda fornito da ONT, per il quale è inclusa in questo pacchetto la sequenza di riferimento. Questo approccio, tuttavia, può portare a perdite non volute di letture da regioni altamente omologhe al genoma del fago lambda.

Please cite: Wouter De Coster, Svenn D’Hert, Darrin T Schultz, Marc Cruts and Christine Van Broeckhoven: NanoPack: visualizing and processing long-read sequencing data. (PubMed,eprint) Bioinformatics 34(15):2666-2669 (2018)
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nanook
analisi pre- e post-allineamento di dati di sequenziamento Nanopore
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NanoOK è un software flessibile, multi-riferimento per analisi pre- e post-allineamento di dati di sequenziamento Nanopore, profili di errore e qualità.

NanoOK (pronunciato ne-nuc) è uno strumento per estrazione, allineamento e analisi di letture Nanopore. NanoOK estrae letture come file FASTA o FASTQ, le allinea (con una scelta di strumenti di allineamento), poi genera un esaustivo rapporto multipagina in PDF contenente analisi di resa, accuratezza e qualità. Durante il processo genera file in testo semplice che possono essere utilizzati per ulteriori analisi, oltre a grafi utilizzabili per l'inclusione in presentazioni ed articoli.

The package is enhanced by the following packages: nanook-examples
Please cite: Richard M. Leggett, Darren Heavens, Mario Caccamo, Matthew D. Clark and Robert P. Davey: NanoOK: multi-reference alignment analysis of nanopore sequencing data, quality and error profiles. (PubMed,eprint) Bioinformatics 32(1):142-144 (2016)
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nanopolish
identificatore di sequenze di consenso per dati di sequenziamento a nanopori
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Nanopolish usa un modello di Markov nascosto segnale-livello per l'identificazione di sequenze di consenso di dati di sequenziamento a nanopori di genomi. Può eseguire l'analisi a livello di segnale di dati di sequenziamento Oxford Nanopore. Nanopolish può calcolare una sequenza di consenso migliorata per una bozza di assemblaggio genomico, rilevare modificazioni di basi, identificare SNP e indel in rapporto ad un genoma di riferimento e altro ancora.

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nanosv
structural variant caller for nanopore data
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NanoSV is a software package that can be used to identify structural genomic variations in long-read sequencing data, such as data produced by Oxford Nanopore Technologies’ MinION, GridION or PromethION instruments, or Pacific Biosciences RSII or Sequel sequencers. NanoSV has been extensively tested using Oxford Nanopore MinION sequencing data.

Please cite: Mircea Cretu Stancu, Markus J. van Roosmalen, Ivo Renkens, Marleen M. Nieboer, Sjors Middelkamp, Joep de Ligt, Giulia Pregno, Daniela Giachino, Giorgia Mandrile, Jose Espejo Valle-Inclan, Jerome Korzelius, Ewart de Bruijn, Edwin Cuppen, Michael E. Talkowski, Tobias Marschall, Jeroen de Ridder and Wigard P. Kloosterman: Mapping and phasing of structural variation in patient genomes using nanopore sequencing.. (eprint) Nature Communications 8:1326 (2017)
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ncbi-blast+
suite di prossima generazione degli strumenti di ricerca di sequenze BLAST
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BLAST (Basic Local Alignment Search Tool) è lo strumento per similarità di sequenze più largamente usato. Ci sono versioni di BLAST che confrontano interrogazioni su proteine con database di proteine, interrogazioni su nucleotidi con database di nucleotidi, così come versioni che traducono interrogazioni o database di nucleotidi in tutte le sei griglie di lettura e li confrontano con database o interrogazioni su proteine. PSI-BLAST produce una PSSM (position-specific-scoring-matrix, matrice di punteggio posizione-specifica) a partire da un'interrogazione su proteina, e poi usa tale PSSM per effettuare ulteriori ricerche. È anche possibile confrontare un'interrogazione per proteina o nucleotidi in un database di PSSM. NCBI gestisce una pagina web per BLAST su blast.ncbi.nlm.nih.gov così come un servizio in rete.

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ngmlr
CoNvex Gap-cost alignMents for Long Reads
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Ngmlr is a long-read mapper designed to sensitively align PacBilo or Oxford Nanopore to (large) reference genomes. It was designed to quickly and correctly align the reads, including those spanning (complex) structural variations. Ngmlr uses an SV aware k-mer search to find approximate mapping locations for a read and then a banded Smith- Waterman alignment algorithm to compute the final alignment. Ngmlr uses a convex gap cost model that penalizes gap extensions for longer gaps less than for shorter ones to compute precise alignments.

Please cite: Fritz J. Sedlazeck, Philipp Rescheneder, Moritz Smolka, Han Fang, Maria Nattestad, Arndt von Haeseler and Michael C. Schatz: Accurate detection of complex structural variations using single-molecule sequencing. Nature Methods 15:461–468 (2018)
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nthash
metodi per valutare tempo di esecuzione e test di uniformità per metodi per hash
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Contiene il binario nttest che ha opzioni per valutare tempo di esecuzione e uniformità per differenti metodi per calcolare hash.

Please cite: Hamid Mohamadi, Justin Chu, Benjamin P. Vandervalk and Inanc Birol: ntHash: recursive nucleotide hashing. (PubMed,eprint) Bioinformatics 32(22):3492-3494 (2016)
odil
libreria C++11 per lo standard DICOM (applicazione)
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Odil sfrutta i costrutti C++ per fornire un'API facile da usare per le diverse parti dello standard DICOM. In Odil sono inclusi: gestione di errori basata su eccezioni, accesso generico a elementi di insiemi di dati, rappresentazione standard JSON e XML di insiemi di dati, implementazione generica di messaggi, client e server per i vari protocolli DICOM.

Questo pacchetto contiene l'applicazione a riga di comando.

orthanc
server DICOM leggero e RESTful per immagini medicali
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Orthanc mira a fornire un server DICOM, semplice ma potente per le immagini medicali. Orthanc può trasformare qualsiasi computer con in esecuzione Windows o Linux in un archivio indipendente dal produttore (VNA) (in altre parole un sistema mini-PACS). La sua architettura è leggera e ciò significa che non è necessaria alcuna amministrazione di database complessa, né l'installazione di dipendenze di terze parti.

Ciò che rende unico Orthanc è il fatto che fornisce un'API RESTful. Grazie a questa fondamentale funzionalità, è possibile pilotare Orthanc da qualsiasi linguaggio per computer. Le etichette DICOM delle immagini medicali memorizzate possono essere scaricate in formato di file JSON. Inoltre Orthanc può generare al volo immagini PNG standard a partire da istanze DICOM.

Orthanc permette ai propri utenti di concentrarsi sul contenuto dei file DICOM, nascondendo la complessità del formato DICOM e del protocollo DICOM.

Please cite: Sébastien Jodogne: The Orthanc Ecosystem for Medical Imaging. (PubMed,eprint) Journal of Digital Imaging 31(3):341–352 (2018)
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orthanc-dicomweb
plugin per estendere Orthanc con la gestione di WADO e DICOMweb
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Orthanc DICOMweb è un plugin per Orthanc, un archivio per immagini mediche leggero, indipendente dal produttore e basato su REST. Estende il nucleo principale di Orthanc con la gestione degli standard WADO (ora noto come WADO-URI) e DICOMweb (QIDO-RS, STOW-RS, WADO-RS).

Please cite: Sebastien Jodogne: The Orthanc Ecosystem for Medical Imaging. J Digit Imaging (2018)
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orthanc-python
sviluppo di plugin per Orthanc usando il linguaggio di programmazione Python
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Questo plugin può essere usato per scrivere plugin Orthanc usando il linguaggio di programmazione Python, invece dei più complessi linguaggi di programmazione C/C++. Può essere usato per ottenere accesso ai moduli Python direttamente in Orthanc.

Questo plugin può essere di grande aiuto a chiunque desideri automatizzare il proprio flusso di lavoro con immagini, progettare/allenare nuovi algoritmi di apprendimento macchina o mettere in produzione sistemi AI direttamente in configurazioni cliniche.

Please cite: Sebastien Jodogne: The Orthanc Ecosystem for Medical Imaging. J Digit Imaging (2018)
orthanc-wsi
Whole-slide imaging support for Orthanc (digital pathology)
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Orthanc-WSI brings support of whole-slide imaging for digital pathology into Orthanc, the lightweight, RESTful Vendor Neutral Archive for medical imaging.

This package contains two command-line tools to convert whole-slide images to and from DICOM. Support for proprietary file formats is available through OpenSlide. The package also contains an Orthanc plugin to display such DICOM images by any standard Web browser. The implementation follows DICOM Supplement 145.

Please cite: Sebastien Jodogne: The Orthanc Ecosystem for Medical Imaging. J Digit Imaging (2018)
paleomix
pipelines and tools for the processing of ancient and modern HTS data
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The PALEOMIX pipelines are a set of pipelines and tools designed to aid the rapid processing of High-Throughput Sequencing (HTS) data: The BAM pipeline processes de-multiplexed reads from one or more samples, through sequence processing and alignment, to generate BAM alignment files useful in downstream analyses; the Phylogenetic pipeline carries out genotyping and phylogenetic inference on BAM alignment files, either produced using the BAM pipeline or generated elsewhere; and the Zonkey pipeline carries out a suite of analyses on low coverage equine alignments, in order to detect the presence of F1-hybrids in archaeological assemblages. In addition, PALEOMIX aids in metagenomic analysis of the extracts.

The pipelines have been designed with ancient DNA (aDNA) in mind, and includes several features especially useful for the analyses of ancient samples, but can all be for the processing of modern samples, in order to ensure consistent data processing.

Please cite: Mikkel Schubert, Luca Ermini, Clio Der Sarkissian, Hákon Jónsson, Aurélien Ginolhac, Robert Schaefer, Michael D Martin, Ruth Fernández, Martin Kircher, Molly McCue, Eske Willerslev and Ludovic Orlando: Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX. (PubMed) Nature Protocols 9(5):1056-82 (2014)
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parallel-fastq-dump
wrapper parallelo di fastq-dump
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fastq-dump di NCBI può essere a volte molto lento, anche se si hanno le risorse (rete, IO, CPU) per andare più veloci, anche se si è già scaricato il file sra. Questo strumento velocizza il processo dividendo il lavoro in thread multipli.

Ciò è possibile perché fastq-dump ha opzioni (-N e -X) per interrogare intervalli specifici del file sra; questo strumento funziona dividendo il lavoro nel numero richiesto di thread, eseguendo più fastq-dump in parallelo e concatenando i risultati insieme, come se si fosse semplicemente eseguita una normale chiamata a fastq-dump.

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parasail
allineatore basato su libparasail
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Questo pacchetto contiene un allineatore a riga di comando basato su libparasail. Parasail è una libreria C SIMD contenente implementazioni degli algoritmi Smith-Waterman, Needleman-Wunsch e vari algoritmi per allineamento di sequenze a coppie semi-globali.

picard-tools
strumenti a riga di comando per manipolare file SAM e BAM
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Il formato SAM (Sequence Alignment/Map, allineamento/mappa di sequenza) è un formato generico per memorizzare grandi allineamenti di sequenze nucleotidiche. In Picard Tools sono incluse le seguenti utilità per manipolare file SAM e BAM:

 AddCommentsToBam                  FifoBuffer
 AddOrReplaceReadGroups            FilterSamReads
 BaitDesigner                      FilterVcf
 BamIndexStats                     FixMateInformation
                                   GatherBamFiles
 BedToIntervalList                 GatherVcfs
 BuildBamIndex                     GenotypeConcordance
 CalculateHsMetrics                IlluminaBasecallsToFastq
 CalculateReadGroupChecksum        IlluminaBasecallsToSam
 CheckIlluminaDirectory            LiftOverIntervalList
 CheckTerminatorBlock              LiftoverVcf
 CleanSam                          MakeSitesOnlyVcf
 CollectAlignmentSummaryMetrics    MarkDuplicates
 CollectBaseDistributionByCycle    MarkDuplicatesWithMateCigar
 CollectGcBiasMetrics              MarkIlluminaAdapters
 CollectHiSeqXPfFailMetrics        MeanQualityByCycle
 CollectIlluminaBasecallingMetrics MergeBamAlignment
 CollectIlluminaLaneMetrics        MergeSamFiles
 CollectInsertSizeMetrics          MergeVcfs
 CollectJumpingLibraryMetrics      NormalizeFasta
 CollectMultipleMetrics            PositionBasedDownsampleSam
 CollectOxoGMetrics                QualityScoreDistribution
 CollectQualityYieldMetrics        RenameSampleInVcf
 CollectRawWgsMetrics              ReorderSam
 CollectRnaSeqMetrics              ReplaceSamHeader
 CollectRrbsMetrics                RevertOriginalBaseQualitiesAndAddMateCigar
 CollectSequencingArtifactMetrics  RevertSam
 CollectTargetedPcrMetrics         SamFormatConverter
 CollectVariantCallingMetrics      SamToFastq
 CollectWgsMetrics                 ScatterIntervalsByNs
 CompareMetrics                    SortSam
 CompareSAMs                       SortVcf
 ConvertSequencingArtifactToOxoG   SplitSamByLibrary
 CreateSequenceDictionary          SplitVcfs
 DownsampleSam                     UpdateVcfSequenceDictionary
 EstimateLibraryComplexity         ValidateSamFile
 ExtractIlluminaBarcodes           VcfFormatConverter
 ExtractSequences                  VcfToIntervalList
 FastqToSam                        ViewSam
The package is enhanced by the following packages: multiqc
Please cite: Broad Institute: Picard toolkit. Broad Institute, GitHub repository (2019)
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Topics: Sequencing; Document, record and content management
picopore
lossless compression of Nanopore files
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The Nanopore is a device to determine the sequences of single moleculres of DNA. No amplification. The output is gigantic and tools like this one help to reduce it.

Over time, other means have substitute the need for this one. Upstream has halted development. Some tutorials and pipelines of the Nanopore still refer to it, though.

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pigx-rnaseq
pipeline for checkpointed and distributed RNA-seq analyses
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This package provides a automated workflow for the automated analysis of RNA-seq experiments. A series of well-accecpted tools are connected in Python scripts and controlled via snakemake. This supports the parallel execution of these workflows and provides checkpointing, such that interrupted workflows can take up their work again.

Please cite: Ricardo Wurmus, Bora Uyar, Brendan Osberg, Vedran Franke, Alexander Gosdschan, Katarzyna Wreczycka, Jonathan Ronen and and Altuna Akalin: PiGx: Reproducible Genomics Analysis Pipelines with GNU Guix. (PubMed,eprint) GigaScience 7(12):giy123 (2018)
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pinfish
Collection of tools to annotate genomes using long read transcriptomics data
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The toolchain is composed of the following tools: 1. spliced_bam2gff - a tool for converting sorted BAM files containing spliced alignments into GFF2 format. Each read will be represented as a distinct transcript. This tool comes handy when visualizing spliced reads at particular loci and to provide input to the rest of the toolchain.

  1. cluster_gff - this tool takes a sorted GFF2 file as input and clusters together reads having similar exon/intron structure and creates a rough consensus of the clusters by taking the median of exon boundaries from all transcripts in the cluster.

  2. polish_clusters - this tool takes the cluster definitions generated by cluster_gff and for each cluster creates an error corrected read by mapping all reads on the read with the median length and polishing it using racon. The polished reads can be mapped to the genome using minimap2 or GMAP.

  3. collapse_partials - this tool takes GFFs generated by either cluster_gff or polish_clusters and filters out transcripts which are likely to be based on RNA degradation products from the 5' end. The tool clusters the input transcripts into "loci" by the 3' ends and discards transcripts which have a compatible transcripts in the loci with more exons.

plasmidid
mapping-based, assembly-assisted plasmid identification tool
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PlasmidID is a mapping-based, assembly-assisted plasmid identification tool that analyzes and gives graphic solution for plasmid identification.

PlasmidID is a computational pipeline that maps Illumina reads over plasmid database sequences. The k-mer filtered, most covered sequences are clustered by identity to avoid redundancy and the longest are used as scaffold for plasmid reconstruction. Reads are assembled and annotated by automatic and specific annotation. All information generated from mapping, assembly, annotation and local alignment analyses is gathered and accurately represented in a circular image which allow user to determine plasmidic composition in any bacterial sample.

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plink1.9
insieme di strumenti di analisi per associazione dell'intero genoma
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plink si aspetta in input i dati da chip SNP (polimorfismi a singolo nucleotide) di molti individui e la loro descrizione fenotipica di una malattia. Trova associazioni tra una variazione singola o una coppia di variazioni nel DNA con un fenotipo e può recuperare le annotazioni SNP da una fonte in linea.

Gli SNP possono essere analizzati individualmente o a coppie per valutare la loro associazione con fenotipi di malattie. È possibile ricercare congiuntamente variazioni nel numero di copie. Sono stati implementati svariati test statistici.

plink1.9 è un esteso aggiornamento di plink con nuovi algoritmi e nuovi metodi, più veloce e con meno consumo di memoria rispetto al primo plink.

Notare che l'eseguibile è stato rinominato in plink1.9 per una collisione di nomi. Ulteriori informazioni su questo possono essere lette in /usr/share/doc/plink1.9/README.Debian.

Please cite: Christopher C. Chang, Carson C. Chow, Laurent C.A.M. Tellier, Shashaank Vattikuti, Shaun M. Purcell and James J. Lee: Second-generation PLINK: rising to the challenge of larger and richer datasets. (eprint) GigaScience 4(1):7 (2015)
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plink2
insieme di strumenti di analisi per associazione dell'intero genoma
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plink si aspetta in input i dati da chip SNP (polimorfismi a singolo nucleotide) di molti individui e la loro descrizione fenotipica di una malattia. Trova associazioni tra una variazione singola o una coppia di variazioni nel DNA con un fenotipo e può recuperare le annotazioni SNP da una fonte in linea.

Gli SNP possono essere analizzati individualmente o a coppie per valutare la loro associazione con fenotipi di malattie. È possibile ricercare congiuntamente variazioni nel numero di copie. Sono stati implementati svariati test statistici.

plink2 è un aggiornamento esaustivo di plink e plink1.9 con nuovi algoritmi e nuovi metodi, più veloce e con meno consumo di memoria rispetto al primo plink.

Please cite: Christopher C. Chang, Carson C. Chow, Laurent C.A.M. Tellier, Shashaank Vattikuti, Shaun M. Purcell and James J. Lee: Second-generation PLINK: rising to the challenge of larger and richer datasets. (eprint) GigaScience 4(1):7 (2015)
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plip
strumento di profilazione dell'interazione proteina-ligando completamente automatizzato
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PLIP (Protein-Ligand Interaction Profiler) è uno strumento per analizzare e visualizzare interazioni proteina-ligando in file PDB.

Le sue funzionalità comprendono:

  • rilevamento di otto diversi tipi di interazioni non covalenti;
  • rilevamento automatico dei ligandi rilevanti in un file PDB;
  • scaricamento diretto di strutture PDB da server wwPDB se viene fornito un ID di PDB valido;
  • elaborazione di file PDB personalizzati contenenti complessi proteina- ligando (es. dal docking);
  • nessuna necessità per preparazione speciale di un file PDB, funziona così come è;
  • rapporti su interazioni a livello di atomi nei formati rST e XML per una facile analisi;
  • generazione di file di sessione di PyMOL (.pse) per ogni accoppiamento, che permette una facile preparazione di immagini per pubblicazioni e presentazioni;
  • rendering di immagini di anteprima per ciascun ligando e le sue interazioni con la proteina.
Please cite: Sebastian Salentin, Sven Schreiber, V. Joachim Haupt, Melissa F. Adasme and Michael Schroeder: PLIP: fully automated protein–ligand interaction profiler. (eprint) Nucleic Acids Research (W1) (2015)
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porechop
adapter trimmer for Oxford Nanopore reads
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Porechop is a tool for finding and removing adapters from Oxford Nanopore reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity. Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

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poretools
toolkit per dati Nanopore di sequenziamento di nucleotidi
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poretools è un toolkit flessibile per esplorare insiemi di dati generati da dispositivi Nanopore di sequenziamento da MinION con gli scopi di controllo di qualità e di analisi a valle. poretools opera direttamente sul formato di file nativo FAST5 (una variante dello standard HDF5) prodotto da ONT e fornisce una vasta gamma di utilità per convertire formati e strumenti per visualizzazione ed esplorazione dei dati.

Please cite: Nicholas Loman and Aaron Quinlan: Poretools: a toolkit for analyzing nanopore sequence data. (PubMed,eprint) Bioinformatics 30(23):3399-3401 (2014)
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pplacer
phylogenetic placement and downstream analysis
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Pplacer places reads on a phylogenetic tree. guppy (Grand Unified Phylogenetic Placement Yanalyzer) yanalyzes them. rppr is a helpful tool for working with reference packages.

Pplacer places query sequences on a fixed reference phylogenetic tree to maximize phylogenetic likelihood or posterior probability according to a reference alignment. Pplacer is designed to be fast, to give useful information about uncertainty, and to offer advanced visualization and downstream analysis.

Please cite: Frederick A Matsen, Robin B Kodner and E Virginia Armbrust: pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. (PubMed,eprint) BMC Bioinformatics 11:538 (2010)
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presto
toolkit per elaborare sequenze di cellule B e T
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pRESTO è un toolkit per elaborare letture grezze da sequenziamento ad alte prestazioni di repertori di cellule B e cellule T.

Gli enormi miglioramenti nelle tecnologie di sequenziamento ad alte prestazioni permettono ora la caratterizzazione su larga scala di repertori di linfociti, definiti come raccolte di proteine antigene-recettore trans-membrana posizionate sulla superficie di cellule B e cellule T. pRESTO (REpertoire Sequencing TOolkit) è composto da una suite di utilità per gestire tutti gli stadi dell'elaborazione di sequenze prima dell'assegnazione di segmenti a linee germinali. pRESTO è progettato per gestire letture singole o a terminali accoppiati. Include funzionalità per controllo di qualità, mascheramento dei primer, annotazione delle letture con codici a barre incorporati nelle sequenze, generazione di sequenze di consenso UMI (Unique Molecular Identifier), assemblaggio di letture a terminali accoppiati e identificazione di sequenze duplicate. Sono incluse anche numerose opzioni per operazioni di ordinamento, campionamento e conversione delle sequenze.

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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prinseq-lite
dati per PReprocessing and INformation of SEQuence (versione leggera)
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PRINSEQ aiuta a pre-elaborare i dati di sequenze genomiche o metagenomiche in formato FASTA o FASTQ. È uno strumento che genera statistiche riassuntive su sequenze e dati di qualità, e che è utilizzato per filtrare, riformattare e tagliare dati da sequenziamento di prossima generazione. È in particolare pensato per dati 454/Roche, ma può essere usato anche per altri tipi di dati di sequenze. La versione autonoma è principalmente progettata per la pre-elaborazione di dati e non genera statistiche riassuntive in forma grafica.

Please cite: Schmieder R and Edwards R: Quality control and preprocessing of metagenomic datasets. (PubMed,eprint) Bioinformatics 27(6):863-864 (2011)
prokka
annotazione rapida di genomi di procarioti
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Un tipico genoma con 4 Mbp può essere completamente annotato in meno di 10 minuti su un computer quad-core e scala bene fino a sistemi SMP con 32 core. Produce file GFF3, GBK e SQN che sono pronti per la modifica in Sequin e infine inviati a Genbank/DDJB/ENA.

The package is enhanced by the following packages: multiqc
Please cite: Torsten Seemann: Prokka: rapid prokaryotic genome annotation. (PubMed,eprint) Bioinformatics 30(14):2068-2069 (2014)
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proteinortho
rilevazione di (co-)ortologhi in analisi su vasta scala di proteine
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Proteinortho è uno strumento autonomo che è diretto verso grandi insiemi di dati e fa uso di tecniche di calcolo distribuito quando gira su hardware multi-core. Implementa una versione estesa dell'euristica per il miglior allineamento reciproco. Proteinortho è stato applicato per calcolare le proteine ortologhe nell'insieme completo di tutti i 717 genomi eubatterici disponibili su NCBI all'inizio del 2009. Gli autori sono riusciti ad identificare trenta proteine presenti nel 99% di tutti i proteomi batterici.

Please cite: Marcus Lechner, Sven Findeiß, Lydia Steiner, Manja Marz, Peter F Stadler and Sonja J Prohaska: Proteinortho: Detection of (Co-)orthologs in large-scale analysis. (PubMed,eprint) BMC Bioinformatics 12:124 (2011)
pybedtools-bin
script prodotti per pybedtools
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La suite di programmi BEDTools è ampiamente usata per la manipolazione di intervalli genomici, o "algebra del genoma". pybedtools fa da wrapper per BEDTools e lo estende, e offre manipolazioni a livello di tratti all'interno di Python.

Questo pacchetto fornisce script che sono eseguibili con la versione Python 3 di questo pacchetto.

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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pycoqc
computes metrics and generates Interactive QC plots
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PycoQC computes metrics and generates interactive QC plots for Oxford Nanopore technologies sequencing data

PycoQC relies on the sequencing_summary.txt file generated by Albacore and Guppy, but if needed it can also generates a summary file from basecalled fast5 files. The package supports 1D and 1D2 runs generated with Minion, Gridion and Promethion devices and basecalled with Albacore 1.2.1+ or Guppy 2.1.3+

The package is enhanced by the following packages: multiqc
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python3-biom-format
formato BIOM (Biological Observation Matrix) (Python 3)
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Il formato di file BIOM (Biological Observation Matrix, matrice di osservazioni biologiche) è progettato per essere un formato di uso generico per rappresentare campioni biologici con tabelle di contingenza delle osservazioni. BIOM è uno standard riconosciuto per l'Earth Microbiome Project ed è un progetto candidato del Genomics Standards Consortium.

Il formato BIOM è progettato per l'uso generico in vaste aree delle scienze -omiche comparative. Per esempio, in indagini su geni marker, l'uso principale di questo formato è quello di rappresentare tabelle OTU: in questo caso le osservazioni sono OTU e la matrice contiene i conteggi corrispondenti al numero di volte che ogni singola OTU è stata osservata in ciascun campione. Nel caso di dati di metagenomi, questo formato può essere usato per rappresentare tabelle metagenomiche: le osservazioni in questo caso potrebbero corrispondere ai sottosistemi SEED e la matrice conterrebbe i conteggi corrispondenti al numero di volte che ogni sottosistema è stato osservato in ciascun metagenoma. Analogamente, nel caso di dati genomici, questo formato può essere usato per rappresentare un insieme di genomi: le osservazioni in questo caso corrisponderebbero ancora una volta ai sottosistemi SEED e i conteggi corrisponderebbero al numero di volte che ogni sottosistema è stato osservato in ciascun genoma.

Questo pacchetto fornisce la libreria per il formato BIOM per l'interprete 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-biopython
libreria Python 3 per bioinformatica
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Il progetto Biopython è un'associazione internazionale di sviluppatori di strumenti Python disponibili liberamente per i calcoli biologici molecolari.

È uno sforzo collaborativo distribuito per sviluppare librerie e applicazioni Python 3 che si rivolgono verso i bisogni dei lavori in bioinformatica attuali e futuri. Il codice sorgente è reso disponibile sotto la licenza Biopython, che è estremamente liberale e compatibile con pressoché qualsiasi licenza esistente. Il progetto lavora accanto alla Open Bioinformatics Foundation, che generosamente fornisce lo spazio web e CVS per il progetto.

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-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
modulo Python 3 per il Center for Genomic Epidemiology
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Questo modulo Python 3 contiene classi e funzioni necessarie per eseguire i wrapper dei servizi e gli script delle catene di pipe sviluppati dal Center for Genomic Epidemiology.

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python3-cogent3
infrastruttura per biologia genomica
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PyCogent è una libreria software per la biologia genomica. È un'infrastruttura completamente integrata e accuratamente testata per:

  • controllare applicazioni di terze parti;
  • ideare flussi di lavoro; interrogare database;
  • condurre analisi probabilistiche innovative dell'evoluzione di sequenze biologiche;
  • generare grafici di qualità adatta per la pubblicazione. Si distingue per le molte funzionalità interne uniche (come un vero allineamento dei codoni) e l'aggiunta frequente di metodi completamente nuovi per l'analisi di dati genomici.
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-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-depinfo
ottiene e stampa dipendenze di pacchetti Python 3
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Questo è un pacchetto Python di utilità pensato per altri pacchetti di libreria. Fornisce una funzione che, quando chiamata con il nome di un pacchetto, stampa informazioni su piattaforma e dipendenze.

python3-drmaa
interfaccia per sistemi di gestione di risorse distribuite conformi a DRMAA
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Questa è un'implementazione Python di Distributed Resource Management (DRM) Application API (DRMAA). Fornisce tutte le funzionalità di alto livello necessarie per consegnare un compito a un sistema DRM (es. Sun Gridengine), incluse le operazioni comuni sui compiti, come terminazione o sospensione.

python3-etelemetry
leggero client Python 3 per comunicare con il server etelemetry
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Questo pacchetto Python 3 fornisce una leggera interfaccia client Python 3 per comunicare con il server etelemetry. Può essere usata per nipy o nipype.

python3-gffutils
lavorare con file GFF e GTF in un'infrastruttura a database flessibile
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Un pacchetto Python per lavorare con i file in formato GFF e GTF, comunemente utilizzati per annotazioni genomiche, e per manipolarli. I file vengono caricati in un database sqlite3, permettendo una manipolazione molto più complessa delle caratteristiche gerarchiche (es.: geni, trascritti ed esoni) di quella possibile con i soli metodi in testo semplice.

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python3-htseq
utilità per analizzare letture di sequenziamento ad alta efficienza del genoma per Python 3
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HTSeq può essere usato per eseguire numerosi compiti comuni di analisi quando si lavora con letture di sequenziamento ad alta efficienza del genoma:

  • ottenere riepiloghi statistici sui punteggi di qualità dell'identificazione delle basi per studiare la qualità dei dati;
  • calcolare un vettore di copertura ed esportarlo per la visualizzazione in un browser di genoma;
  • leggere dati di annotazione da un file GFF;
  • assegnare letture allineate da esperimenti RNA-Seq a esoni e geni.
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-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-nanomath
simple math function for other Oxford Nanopore processing scripts
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This Python3 module provides a few simple math and statistics functions for other scripts processing Oxford Nanopore sequencing data.

  • Calculate read N50 from a set of lengths get_N50(readlenghts)
  • Remove extreme length outliers from a dataset remove_length_outliers(dataframe, columname)
  • Calculate the average Phred quality of a read ave_qual(qualscores)
  • Write out the statistics report after calling readstats function write_stats(dataframe, outputname)
  • Compute a number of statistics, return a dictionary calc_read_stats(dataframe)
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-pairtools
infrastruttura per elaborare dati di sequenziamento da un esperimento Hi-C
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Semplice e veloce infrastruttura a riga di comando per elaborare dati di sequenziamento da un esperimento Hi-C.

Elabora allineamenti di sequenze a terminali appaiati ed effettua le seguenti operazioni:

  • rileva giunzioni di legatura (alias coppie Hi-C) in sequenze allineate, a terminali accoppiati, di molecole di DNA Hi-C;
  • ordina file .pairs per analisi successive;
  • rileva, applica tag e rimuove duplicati di PCR/ottici;
  • genera statistiche esaurienti su insiemi di dati Hi-C;
  • seleziona coppie Hi-C in base a criteri definiti in modo flessibile;
  • ripristina allineamenti .sam da coppie Hi-C.
The package is enhanced by the following packages: python3-pairtools-examples
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python3-pauvre
QC and genome browser plotting Oxford Nanopore and PacBio long reads
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Pauvre is a plotting package designed for nanopore and PacBio long reads.

This package currently hosts four scripts for plotting and/or printing stats.

 pauvre marginplot
    Takes a fastq file as input and outputs a marginal histogram with a
    heatmap.
 pauvre stats
    Takes a fastq file as input and prints out a table of stats, including
    how many basepairs/reads there are for a length/mean quality cutoff.
    This is also automagically called when using pauvre marginplot
 pauvre redwood
    Method of representing circular genomes. A redwood plot contains long
    reads as "rings" on the inside, a gene annotation "cambrium/phloem",
    and a RNAseq "bark". The input is .bam files for the long reads and
    RNAseq data, and a .gff file for the annotation.
 pauvre synteny
    Makes a synteny plot of circular genomes. Finds the most parsimonius
    rotation to display the synteny of all the input genomes with the
    fewest crossings-over. Input is one .gff file per circular genome
    and one directory of gene alignments.
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python3-pbcommand
interfaccia comune a riga di comando per moduli di analisi di Pacific Biosciences
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Per integrarsi con il motore di flusso di lavoro pbsmrtpipe, si deve essere in grado di generare un Tool Contract e di eseguire da un Resolved Tool Contract. Un Tool Contract contiene i metadati dell'eseguibile, come i tipi di file di input, di output e le opzioni. Ci sono due casi d'uso principali: il primo è fare il wrapping/chiamare funzioni Python che sono state definite in pacchetti Python 3 esterni, o script; il secondo è creare uno strumento CLI che gestisca l'emissione di Tool Contract, l'esecuzione dei Resolved Tool Contract e una CLI completa in stile argparse.

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python3-pbcore
libreria Python 3 per elaborare file di dati PacBio
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Il pacchetto pbcore fornisce moduli Python per elaborare file di dati Pacific Biosciences e creare applicazioni bioinformatiche PacBio. Questi moduli includono strumenti per leggere/scrivere formati di dati PacBio, file di dati d'esempio per test e debug, classi base e utilità per creare applicazioni bioinformatiche.

Questo pacchetto fa parte della suite SMRTAnalysis.

Questo è il modulo per Python 3.

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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-pychopper
identify, orient and trim full-length Nanopore cDNA reads
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Pychopper v2 is a Python module to identify, orient and trim full-length Nanopore cDNA reads. It is also able to rescue fused reads and provides the script 'pychopper.py'. The general approach of Pychopper v2 is the following:

  • Pychopper first identifies alignment hits of the primers across the length of the sequence. The default method for doing this is using nhmmscan with the pre-trained strand specific profile HMMs, included with the package. Alternatively, one can use the edlib backend, which uses a combination of global and local alignment to identify the primers within the read.
  • After identifying the primer hits by either of the backends, the reads are divided into segments defined by two consecutive primer hits. The score of a segment is its length if the configuration of the flanking primer hits is valid (such as SPP,-VNP for forward reads) or zero otherwise.
  • The segments are assigned to rescued reads using a dynamic programming algorithm maximizing the sum of used segment scores (hence the amount of rescued bases). A crucial observation about the algorithm is that if a segment is included as a rescued read, then the next segment must be excluded as one of the primer hits defining it was "used up" by the previous segment. This put constraints on the dynamic programming graph. The arrows in read define the optimal path for rescuing two fused reads with the a total score of l1 + l3.

A crucial parameter of Pychopper v2 is -q, which determines the stringency of primer alignment (E-value in the case of the pHMM backend). This can be explicitly specified by the user, however by default it is optimized on a random sample of input reads to produce the maximum number of classified reads.

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python3-pydicom
lettura e scrittura di file medicali DICOM (Python 3)
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pydicom è un modulo in Python puro per l'analisi di file DICOM. DICOM è uno standard (http://medical.nema.org) per comunicare le immagini medicali e le informazioni relative, come rapporti e oggetti di radioterapia.

pydicom rende facile la lettura di file DICOM in naturali strutture pythoniche per una facile manipolazione. Gli insiemi di dati modificati posso essere nuovamente scritti su file in formato DICOM.

Questo pacchetto installa il modulo per Python 3.

python3-pyfaidx
accesso casuale efficiente per sottosequenze FASTA in Python 3
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Samtools fornisce una funzione "faidx" (FAsta InDeX) che crea un piccolo file indice piatto ".fai" che permette un accesso casuale veloce a qualsiasi sottosequenza del file FASTA indicizzato, caricando solo una quantità minima del file in memoria. Questo modulo Python implementa classi Python pure per indicizzazione, recupero e modifica sul posto di file FASTA usando un indice compatibile con samtools. Il modulo pyfaidx è compatibile a livello di API con il modulo seqdb di pygr. Uno script "faidx" a riga di comando viene installato accanto al modulo pyfaidx e facilita manipolazioni complesse di file FASTA senza alcuna conoscenza di programmazione.

Questo pacchetto fornisce i moduli Python 3 per accedere a file FASTA.

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-pynn
simulator-independent specification of neuronal network models
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PyNN allows for coding a model once and run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). PyNN translates standard cell-model names and parameter names into simulator-specific names.

python3-pysam
interfaccia al formato SAM/BAM per allineamento e mappatura di sequenze (Python 3)
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Pysam è un modulo Python per leggere e manipolare Samfile. È un wrapper leggero per l'API C di samtools. Pysam include anche un'interfaccia per tabix.

Questo pacchetto installa il modulo per Python 3.

The package is enhanced by the following packages: python-pysam-tests
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python3-questplus
QUEST+ implementation in Python3
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QUEST+ is a Bayesian adaptive psychometric testing method that allows an arbitrary number of stimulus dimensions, psychometric function parameters, and trial outcomes.

This package provides an implementation in Python3.

python3-scitrack
libreria Python 3 per tenere traccia di dati scientifici
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Scitrack è una libreria mirata agli sviluppatori di applicazioni che scrivono software scientifico per gestire il tracciamento di calcoli scientifici. La libreria fornisce funzionalità elementari per gestire la registrazione di log. Le funzionalità primarie sono relative alla generazione di somme di controllo sui file di input e output e a facilitare la registrazione di log dell'ambiente di calcolo.

python3-screed
utilità per sequenze di letture di nucleotidi corte in Python 3
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Screed analizza file FASTA e FASTQ, genera database e permette di interrogarli. Da questi database possono essere recuperati valori come nome, descrizione e qualità della sequenza e la sequenza stessa.

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python3-seirsplus
Models of SEIRS epidemic dynamics with extensions
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This package implements generalized SEIRS infectious disease dynamics models with extensions that model the effect of factors including population structure, social distancing, testing, contact tracing, and quarantining detected cases.

Notably, this package includes stochastic implementations of these models on dynamic networks.

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
rappresentazione numerica delle differenze tra stringhe
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Un piccolo modulo Python che fornisce il calcolo della distanza di modifica (alias distanza di Levenshtein, cioè contare inserzioni, delezioni e sostituzioni) e della distanza di Hamming.

Il suo scopo principale è di velocizzare il calcolo della distanza di modifica, permettendo di specificare un numero massimo di differenze maxdiff (banding). Se viene fornito tale parametro, la distanza di modifica restituita è accurata solo fino a maxdiff. Cioè, se l'effettiva distanza di modifica è più grande di maxdiff, viene restituito un valore più grande di maxdiff, ma non necessariamente l'effettiva distanza di modifica.

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python3-toolz
??? missing short description for package python3-toolz :-(
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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-tornado
server web scalabile e non bloccante, e strumenti - pacchetto Python 3
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Tornado è un'infrastruttura web e una libreria di rete asincrona per Python, originariamente sviluppate da FriendFeed. Grazie all'uso di I/O di rete non bloccante, Tornado può scalare fino a decine di migliaia di connessioni aperte, e ciò lo rende ideale per polling lunghi, WebSocket e altre applicazioni che necessitano una connessione di lunga durata per ogni utente.

Questa è la versione per Python 3 del pacchetto.

python3-treetime
inference of time stamped phylogenies and ancestral reconstruction (Python 3)
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TreeTime provides routines for ancestral sequence reconstruction and the maximum likelihoo inference of molecular-clock phylogenies, i.e., a tree where all branches are scaled such that the locations of terminal nodes correspond to their sampling times and internal nodes are placed at the most likely time of divergence.

TreeTime aims at striking a compromise between sophisticated probabilistic models of evolution and fast heuristics. It implements GTR models of ancestral inference and branch length optimization, but takes the tree topology as given. To optimize the likelihood of time-scaled phylogenies, treetime uses an iterative approach that first infers ancestral sequences given the branch length of the tree, then optimizes the positions of unconstraine d nodes on the time axis, and then repeats this cycle. The only topology optimization are (optional) resolution of polytomies in a way that is most (approximately) consistent with the sampling time constraints on the tree. The package is designed to be used as a stand-alone tool or as a library used in larger phylogenetic analysis workflows.

Features

  • ancestral sequence reconstruction (marginal and joint maximum likelihood)
  • molecular clock tree inference (marginal and joint maximum likelihood)
  • inference of GTR models
  • rerooting to obtain best root-to-tip regression
  • auto-correlated relaxed molecular clock (with normal prior)

This package provides the Python 3 module.

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python3-vcf
analizzatore di Variant Call Format (VCF) per Python 3
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Variant Call Format (VCF) specifica il formato di un file di testo usato in bioinformatica per memorizzare variazioni di sequenze di geni. Il formato è stato sviluppato con l'avvento di progetti di sequenziamento di DNA e di genotipizzazione su larga scala, come il 1000 Genomes Project.

L'intento di questo modulo è di imitare il modulo "csv" nella stdlib di Python, in contrasto con formati più flessibili di serializzazione come JSON o YAML. "vcf" tenterà di analizzare il contenuto di ogni record sulla base dei tipi di dato specificati nelle righe di meta-informazioni, e precisamente le righe ##INFO e ##FORMAT. Se tali righe sono mancanti o incomplete, controllerà i tipi riservati menzionati nelle specifiche. In mancanza di ciò, restituirà semplicemente delle stringhe.

Questo pacchetto fornisce i moduli per Python 3.

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q2-cutadapt
plugin QIIME 2 per lavorare con adattatori in dati di sequenze
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QIIME 2 è un pacchetto per analisi del microbioma potente, estensibile e decentralizzato con attenzione a trasparenza di analisi e dati. QIIME2 permette ai ricercatori di iniziare un'analisi con dati di sequenze di DNA grezze e finire con figure di qualità di pubblicazione e risultati statistici. Caratteristiche principali:

  • tracciamento integrato e automatico della provenienza dei dati;
  • sistema di tipi semantici;
  • sistema a plugin per estendere le funzionalità di analisi del microbioma;
  • gestione per più tipi di interfacce utente (es. API, riga di comando, grafica).

QIIME 2 è un completo rifacimento e riscrittura della catena di analisi di microbioma QIIME 1. QIIME 2 risolve molte delle limitazioni di QIIME 1 mantenendo al contempo le funzionalità che fanno di QIIME 1 una catena di analisi potente e ampiamente utilizzata.

QIIME 2 attualmente gestisce una catena di elaborazione iniziale end-to-end per analisi del microbioma. Nuove funzionalità diventeranno regolarmente disponibili attraverso plugin per QIIME 2. Si può vedere un elenco dei plugin attualmente disponibili sulla pagina dei plugin disponibili di QIIME 2. La pagina dei plugin futuri elenca i plugin che sono in fase di sviluppo.

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-table
plugin QIIME 2 per gestione di operazioni su tabelle di caratteristiche
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QIIME 2 è un pacchetto per analisi del microbioma potente, estensibile e decentralizzato con attenzione a trasparenza di analisi e dati. QIIME2 permette ai ricercatori di iniziare un'analisi con dati di sequenze di DNA grezze e finire con figure di qualità di pubblicazione e risultati statistici. Caratteristiche principali:

  • tracciamento integrato e automatico della provenienza dei dati;
  • sistema di tipi semantici;
  • sistema a plugin per estendere le funzionalità di analisi del microbioma;
  • gestione per più tipi di interfacce utente (es. API, riga di comando, grafica).

QIIME 2 è un completo rifacimento e riscrittura della catena di analisi di microbioma QIIME 1. QIIME 2 risolve molte delle limitazioni di QIIME 1 mantenendo al contempo le funzionalità che fanno di QIIME 1 una catena di analisi potente e ampiamente utilizzata.

QIIME 2 attualmente gestisce una catena di elaborazione iniziale end-to-end per analisi del microbioma. Nuove funzionalità diventeranno regolarmente disponibili attraverso plugin per QIIME 2. Si può vedere un elenco dei plugin attualmente disponibili sulla pagina dei plugin disponibili di QIIME 2. La pagina dei plugin futuri elenca i plugin che sono in fase di sviluppo.

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
plugin QIIME 2 per filtraggio e taglio basato su PHRED
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QIIME 2 è un pacchetto per analisi del microbioma potente, estensibile e decentralizzato con attenzione a trasparenza di analisi e dati. QIIME2 permette ai ricercatori di iniziare un'analisi con dati di sequenze di DNA grezze e finire con figure di qualità di pubblicazione e risultati statistici. Caratteristiche principali:

  • tracciamento integrato e automatico della provenienza dei dati;
  • sistema di tipi semantici;
  • sistema a plugin per estendere le funzionalità di analisi del microbioma;
  • gestione per più tipi di interfacce utente (es. API, riga di comando, grafica).

QIIME 2 è un completo rifacimento e riscrittura della catena di analisi di microbioma QIIME 1. QIIME 2 risolve molte delle limitazioni di QIIME 1 mantenendo al contempo le funzionalità che fanno di QIIME 1 una catena di analisi potente e ampiamente utilizzata.

QIIME 2 attualmente gestisce una catena di elaborazione iniziale end-to-end per analisi del microbioma. Nuove funzionalità diventeranno regolarmente disponibili attraverso plugin per QIIME 2. Si può vedere un elenco dei plugin attualmente disponibili sulla pagina dei plugin disponibili di QIIME 2. La pagina dei plugin futuri elenca i plugin che sono in fase di sviluppo.

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)
qcat
demultiplexing di letture Oxford Nanopore da file FASTQ
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Qcat è uno strumento a riga di comando per fare il demultiplexing di letture Oxford Nanopore da file FASTQ. Accetta file FASTQ con basi identificate e divide le letture in file FASTQ separati secondo il loro codice a barre. Qcat rende gli algoritmi di demultiplexing usati in albacore/guppy e EPI2ME disponibili per essere usati localmente con file FASTQ. Attualmente qcat implementa l'algoritmo EPI2ME.

The package is enhanced by the following packages: qcat-examples
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quicktree
algoritmo di unione dei vicini per filogenie
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QuickTree è un'implementazione efficiente dell'algoritmo Neighbor-Joining (PMID: 3447015), capace di ricostruire filogenie da enormi allineamenti in un tempo inferiore all'età dell'universo.

QuickTree accetta come input sia una matrice di distanze, sia un allineamento di sequenze multiple. Il primo deve essere in formato PHYLIP. L'ultimo in formato Stockholm, che è il formato di allineamento nativo del database Pfam. Allineamenti in vari formati possono essere convertiti nel formato Stockholm con il programma sreformat, che fa parte del pacchetto HMMer (hmmer.org).

Gli alberi sono scritti su stdout nel formato Newick/New-Hampshire usato da PHYLIP e molti altri programmi.

r-bioc-htsfilter
GNU R filter replicated high-throughput transcriptome sequencing data
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This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions.

r-bioc-limma
modelli lineari per dati di microarray
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I microarray sono piastre microscopiche con filamenti corti di DNA attentamente disposti e/o superfici preparate chimicamente su cui altro DNA si lega in modo preferenziale. La quantità di DNA che si lega in diverse posizioni di questi chip, tipicamente determinata sulla base di un colorante fluorescente, deve essere interpretata. La tecnologia viene tipicamente usata con DNA derivato da RNA, cioè per determinare l'attività di un gene e/o delle sue varianti di splice. La tecnologia però è anche usata per determinare variazioni di sequenza in DNA genomico.

Questo pacchetto Bioconductor gestisce l'analisi di dati microarray di espressione genica, specialmente per l'uso di modelli lineari per l'analisi di esperimenti progettati e la valutazione dell'espressione differenziale. Il pacchetto include funzionalità di pre-elaborazione per array colorati con due colori. I metodi di espressione differenziale si applicano a tutte le piattaforme di array e trattano gli esperimenti Affymetrix, a singolo canale e a due canali in un modo unificato.

Please cite: Gordon K. Smyth: Limma: linear models for microarray data. (eprint) :397-420 (2005)
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r-bioc-mutationalpatterns
GNU R comprehensive genome-wide analysis of mutational processes
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This BioConductor package provides an extensive toolset for the characterization and visualization of a wide range of mutational patterns in base substitution catalogs.

r-bioc-pwmenrich
PWM enrichment analysis
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A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools.

r-bioc-rcpi
molecular informatics toolkit for compound-protein interaction
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Rcpi offers a molecular informatics toolkit with a comprehensive integration of bioinformatics and chemoinformatics tools for drug discovery.

Please cite: Dong-Sheng Cao, Nan Xiao, Qing-Song Xu and Alex F. Chen: Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions. Bioinformatics 31(2):279-281 (2015)
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r-bioc-rgsepd
GNU R gene set enrichment / projection displays
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R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group.

r-bioc-rsamtools
allineamento binario (BAM), variant call (BCF) o importazione di file tabix per GNU R
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Questo pacchetto fornisce un'interfaccia alle utilità "samtools", "bcftools" e "tabix" per la manipolazione di SAM (Sequence Alignment / Map), BCF (binary variant call) e file delimitati da tab indicizzati e compressi (tabix).

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r-bioc-tcgabiolinks
GNU R/Bioconductor package for integrative analysis with GDC data
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The aim of TCGAbiolinks is:

 1) facilitate the GDC open-access data retrieval,
 2) prepare the data using the appropriate pre-processing strategies,
 3) provide the means to carry out different standard analyses and
 4) to easily reproduce earlier research results.
In more detail, the package provides multiple methods for analysis (e.g.,

differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines.

r-cran-alakazam
analisi di linee clonali e diversità per immunoglobuline
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Alakazam fa parte dell'infrastruttura di analisi Immcantation per AIRR-seq (Adaptive Immune Receptor Repertoire sequencing) e fornisce un insieme di strumenti per investigare linee clonali, diversità, uso di geni e altre proprietà a livello di repertorio dei recettori di linfociti, con particolare attenzione al sequenziamento ad alte prestazioni di immunoglobuline (Ig).

Alakazam ha 5 scopi principali:

  • Fornire funzionalità di base per altri pacchetti R nell'infrastruttura Immcantation. Ciò include compiti comuni come I/O su file, manipolazione di base di sequenze di DNA e interazione con segmenti V(D)J e annotazioni di geni.
  • Fornire un'interfaccia R per interagire con l'output delle suite di strumenti pRESTO e Change-O.
  • Effettuare ricostruzioni di linee su popolazioni clonali di sequenze di Ig e analizzare la topologia degli alberi delle linee risultanti.
  • Effettuare analisi di abbondanza e diversità clonali su repertori linfocitari.
  • Effettuare analisi di proprietà fisico-chimiche delle sequenze dei recettori linfocitari.
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. (eprint) 31(20):3356–3358 (2017)
r-cran-covid19us
casi di COVID-19 negli Stati Uniti preparati per GNU R
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Questo pacchetto fornisce un wrapper GNU R per la "COVID Tracking Project API" (https://covidtracking.com/api/) che fornisce dati sui casi di COVID-19 negli Stati Uniti.

r-cran-diagnosismed
toolkit per analisi di accuratezza per test diagnostici medicali
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DiagnosisMed è un pacchetto GNU R per analizzare l'accuratezza dei dati da test diagnostici per valutare le condizioni di salute. È stato creato per essere usato da professionisti in campo medico. Questo pacchetto aiuta a stimare la sensibilità e la specificità da risultati di test categorici e continui incluse alcune valutazioni di risultati indeterminati, oppure confrontare diversi test categorici e stimare valori limite ragionevoli per i test e visualizzarli in un modo usato comunemente da professionisti in campo medico. Non è ancora disponibile un'interfaccia grafica.

r-cran-epi
analisi epidemiologica GNU R
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Funzioni per analisi demografiche ed epidemiologiche nel diagramma di Lexis, cioè registrazioni e dati di aggiornamento di un gruppo, inclusi dati censiti ad intervalli e la rappresentazione di dati multi-stato. Inoltre vi sono alcune funzioni utili per la tabulazione e il tracciamento di grafici. Contiene alcuni insiemi di dati epidemiologici.

Il pacchetto Epi è orientato principalmente all'epidemiologia "classica" per le malattie croniche. Il pacchetto si è sviluppato dal corso di Statistica applicata all'epidemiologia usando R (vedere http://www.pubhealth.ku.dk/~bxc/SPE).

Una breve introduzione a R per l'epidemiologia è disponibile su http://staff.pubhealth.ku.dk/%7Ebxc/Epi/R-intro.pdf Notare che le pagine 38-120 di questo documento sono semplicemente le pagine di manuale del pacchetto Epi.

Epi non è l'unico pacchetto R per l'analisi epidemiologica, il pacchetto epitools, anch'esso disponibile in Debian, è un pacchetto più indirizzato all'epidemiologia delle malattie infettive.

Epi è usato dal Dipartimento di Biostatistica dell'Università di Copenaghen.

Please cite: Martyn Plummer and Bendix Carstensen: Lexis: An R Class for Epidemiological Studies with Long-Term Follow-Up. Journal of Statistical Software 38(5):1-12 (2011)
r-cran-epibasix
funzioni epidemiologiche elementari per GNU R
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Funzioni epidemiologiche elementari per un corso universitario di epidemiologia/biostatistica.

Questo pacchetto contiene strumenti elementari per l'analisi di problemi epidemiologici comuni, che vanno dalla stima della grandezza di un campione all'analisi di tabelle di contingenza 2x2 e misure di base di concordanza (kappa, sensibilità/specificità). Vengono inoltre prodotti, quando possibile, rapporti riassuntivi e stampe appropriati per facilitare l'interpretazione. Questo pacchetto è in fase di sviluppo, perciò qualsiasi commento o suggerimento è benvenuto. Il codice sorgente è largamente commentato per facilitare modifiche. L'utenza a cui è indirizzato include studenti universitari di vari corsi di statistica epidemiologica/biologica.

Epibasix è stato sviluppato in Canada.

r-cran-epicalc
calcolatore epidemiologico per GNU R
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Funzioni per rendere facile il calcolo epidemiologico in R.

Permette di elaborare insiemi di dati da Dbase (.dbf), Stata (.dta), SPSS (.sav), EpiInfo (.rec) e valori separati da virgole (.csv) o anche gruppi di dati in R per eseguire diversi calcoli epidemiologici.

r-cran-epiestim
GNU R estimate time varying reproduction numbers from rpidemic curves
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Tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence as described in Cori et al. (2013) and Wallinga and Teunis (2004) .

r-cran-epir
funzioni GNU R per analisi di dati epidemiologici
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Un pacchetto per analizzare dati epidemiologici. Contiene funzioni per regolare direttamente e indirettamente misure della frequenza di malattie, quantificare misure di associazioni sulla base di strati singoli o multipli di dati numerici presentati in una tabella di contingenza e calcolare intervalli di confidenza sul rischio di incidenza e le stime delle percentuali di incidenza. Sono fornite funzioni varie per l'uso in meta-analisi, interpretazione di test diagnostici e calcoli della dimensione del campione.

r-cran-epitools
strumenti GNU R per epidemiologia per dati e grafici
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Strumenti GNU R per epidemiologi e analisti di dati. Epitools fornisce strumenti numerici e soluzioni software che sono state usate e testate in applicazioni epidemiologiche nel mondo reale.

Molti problemi pratici nell'analisi dei dati sulla salute pubblica richiedono programmazione o software speciale e gli studiosi, in diversi luoghi, possono fare sforzi di programmazione duplicati. Spesso analisi semplici, come la costruzione di intervalli di confidenza, non sono calcolate e perciò complicano un'inferenza statistica corretta per aree geografiche limitate. Ci sono molti esempi di strumenti numerici semplici e utili che migliorerebbero il lavoro degli epidemiologi nei dipartimenti di salute pubblica e che, però, non sono disponibili immediatamente per i problemi reali che si trovano di fronte. La disponibilità di questi strumenti incoraggerà un più vasto uso dei metodi appropriati e promuoverà politiche di salute pubblica basate su prove.

r-cran-hms
ora del giorno bella per GNU R
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Questo pacchetto per GNU R implementa una classe S3, basata sulla classe "difftime", per memorizzare e formattare valori per ora del giorno.

r-cran-incidence
calcolo, gestione, disegno e modellazione in GNU R dell'incidenza di eventi con data
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Fornisce funzioni e classi per calcolare, gestire e visualizzare l'incidenza da eventi con data per un intervallo di tempo definito. Le date possono essere fornite in vari formati standard. La classe "incidence" è usata per memorizzare l'incidenza calcolata e può essere facilmente manipolata, divisa in sottoinsiemi e disegnata. In aggiunta può essere fatto il fit di modelli log-lineari su oggetti "incidence" usando "fit". Questo pacchetto fa parte dell'insieme di strumenti RECON (http://www.repidemicsconsortium.org/) per l'analisi di epidemie.

r-cran-kernelheaping
GNU R kernel density estimation for heaped and rounded data
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In self-reported or anonymised data the user often encounters heaped data, i.e. data which are rounded (to a possibly different degree of coarseness). While this is mostly a minor problem in parametric density estimation the bias can be very large for non-parametric methods such as kernel density estimation. This package implements a partly Bayesian algorithm treating the true unknown values as additional parameters and estimates the rounding parameters to give a corrected kernel density estimate. It supports various standard bandwidth selection methods. Varying rounding probabilities (depending on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (). Additionally, bivariate non- parametric density estimation for rounded data, Gross, M. et al. (2016) (), as well as data aggregated on areas is supported.

r-cran-lexrankr
extractive summarization of text with the LexRank algorithm
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An R implementation of the LexRank algorithm implementing stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. The technique on the problem of Text Summarization (TS) is tested. Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence.

Please cite: Güneş Erkan and Dragomir R. Radev: LexRank: Graph-based Lexical Centrality as Salience in Text Summarization. (eprint) Journal of Artific Intelligence Research 22:457-479 (2004)
r-cran-mediana
simulazione di studi clinici
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Fornisce un'infrastruttura generica per simulazione di studi clinici, basata sull'approccio CSE (Clinical Scenario Evaluation). Il pacchetto gestisce una vasta classe di modelli di dati (inclusi studi clinici con punti finali continui, binari, di tipo sopravvivenza e di tipo contatore, oltre a risultati multivariati che sono basati su combinazioni dei diversi punti finali), strategie di analisi e criteri di valutazione comunemente usati.

r-cran-msm
modelli multistato di Markov e di Markov nascosti in tempo continuo per GNU R
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Funzioni per il fitting di modelli generici multi-stato di Markov e di Markov nascosti in tempo continuo a dati longitudinali. Sia le probabilità di transizione tra gli stati markoviani sia il processo di output del modello di Markov nascosto possono essere modellati in termini di covariate. Sono gestiti svariati schemi di osservazione, inclusi processi osservati ad intervalli arbitrari, processi osservati completamente e stati censurati.

Please cite: Christopher H. Jackson: Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software 38(8):1-29 (2011)
r-cran-qtl
pacchetto GNU R per analisi di linkage dei marcatori genetici
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R/qtl è un ambiente estensibile e interattivo per mappare loci per tratti quantitativi (QTL) in incroci sperimentali. È implementato come un pacchetto aggiuntivo per il linguaggio/software statistico R che è liberamente disponibile e largamente usato (vedere http://www.r-project.org).

Lo sviluppo di questo software come componente aggiuntivo per R permette di sfruttare le funzioni matematiche e statistiche di base e le potenti capacità di creazione di grafici che sono fornite con R. Inoltre l'utente trae beneficio dall'integrazione perfetta del software di mappatura QTL in un programma di analisi statistica generica. Lo scopo è di rendere i complessi metodi di mappatura QTL disponibili ad una vasta utenza e di permettere agli utenti di concentrarsi sulla creazione dei modelli piuttosto che sul calcolo.

Un componente chiave dei metodi di calcolo per la mappatura QTL è la tecnologia dei modelli di Markov nascosti (HMM) per gestire dati genotipici mancanti. Sono stati implementati i principali algoritmi HMM con gestione della presenza di errori nel genotipo, per reincroci, interincroci e incroci a quattro vie con fase nota.

La versione attuale di R/qtl include funzionalità per stimare mappe genetiche, identificare errori nel genotipo ed effettuare scansioni del genoma per QTL singoli e scansioni del genoma bidimensionali per due QTL, usando mappature di intervalli (con l'algoritmo EM), regressione di Haley-Knott e imputazione multipla. Tutto questo può essere fatto in presenza di covariate (come sesso, età o trattamento). È anche possibile adattare modelli QTL di ordine più alto usando l'imputazione multipla.

Please cite: Karl W. Broman, Hao Wu, Saunak Sen and Gary A. Churchill: R/qtl: QTL mapping in experimental crosses. (PubMed,eprint) Bioinformatics 19:889-890 (2003)
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r-cran-seroincidence
strumento GNU R per calcolare la sieroincidenza
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I livelli di anticorpi misurati in campioni di popolazione trasversali possono essere tradotti in stime della frequenza con cui avvengono sieroconversioni (nuove infezioni). Per poter interpretare i livelli anticorpali trasversali misurati, devono essere conosciuti i parametri di predizione del decadimento degli anticorpi. In rapporti precedentemente pubblicati (Simonsen et al. 2009 e Versteegh et al. 2005), queste informazioni sono state ottenute da studi longitudinali su soggetti che avevano infezioni da Salmonella o Campylobacter confermate colturalmente. È stato utilizzato un modello bayesiano di calcolo all'indietro per convertire le misure anticorpali in una stima del tempo trascorso dall'infezione. Ciò può essere utilizzato per stimare la sieroincidenza nel campione trasversale della popolazione. Per entrambe le misure longitudinale e trasversale delle concentrazioni anticorpali, è stato usato un metodo ELISA indiretto. I modelli sono validi solo per soggetti con più di 18 anni. Le stime della sieroincidenza sono adatte per monitorare l'effetto di programmi di controllo quando sono disponibili per l'analisi campioni di siero trasversali rappresentativi. Questi forniscono informazioni più accurate sulla pressione dell'infezione nella popolazione umana in più nazioni.

Please cite: PFM Teunis, JCH van Eijkeren, CW Ang, YTHP van Duynhoven, JB Simonsen, MA Strid and W van Pelt: Biomarker dynamics: estimating infection rates from serological data. (PubMed) Statistics in Medicine 31(20):2240–2248 (2012)
r-cran-sf
Simple Features for R
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Support for simple features, a standardized way to encode spatial vector data. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations.

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-sjplot
GNU R data visualization for statistics in social science
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Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.

r-cran-spp
GNU R ChIP-seq processing pipeline
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R package for anlaysis of ChIP-seq and other functional sequencing data

  • Assess overall DNA-binding signals in the data and select appropriate quality of tag alignment.
  • Discard or restrict positions with abnormally high number of tags.
  • Calculate genome-wide profiles of smoothed tag density and save them in WIG files for viewing in other browsers.
  • Calculate genome-wide profiles providing conservative statistical estimates of fold enrichment ratios along the genome. These can be exported for browser viewing, or thresholded to determine regions of significant enrichment/depletion.
  • Determine statistically significant point binding positions
  • Assess whether the set of point binding positions detected at a current sequencing depth meets saturation criteria, and if does not, estimate what sequencing depth would be required to do so.
Please cite: Peter V Kharchenko, Michael Y Tolstorukov and Peter J Park: Design and analysis of ChIP-seq experiments for DNA-binding proteins. (PubMed) Nature biotechnology 26(12):1351–1359 (2008)
r-cran-stringi
funzionalità per elaborazione di stringhe di caratteri per GNU R
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Permette l'elaborazione veloce, corretta, coerente e portabile, oltre che comoda, di testo o stringhe di caratteri in qualsiasi localizzazione e codifica nativa. Grazie all'uso della libreria ICU, il pacchetto fornisce agli utenti R funzioni indipendenti dalla piattaforma conosciute ai programmatori Java, Perl, Python, PHP e Ruby. Tra le funzionalità disponibili ci sono: ricerca di modelli (ad esempio attraverso espressioni regolari), generazione di stringhe casuali, collazione di stringhe, traslitterazione, concatenazione, formattazione e analisi di date e orari, ecc.

Please cite: Marek Gagolewski, Bartlomiej Tartanus, Oliver Keyes and Marcin Pawel Bujarski: R package stringi: Character string processing facilities. zenodo (2015)
r-cran-surveillance
pacchetto GNU R per la modellazione e il monitoraggio di fenomeni epidemici
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Metodi statistici per la modellazione e il monitoraggio di serie temporali di conteggi, proporzioni e dati categorici, così come per la modellazione di processi puntuali di fenomeni epidemici nel tempo continuo.

I metodi di monitoraggio si focalizzano sul rilevamento di aberrazioni in serie temporali di conteggi da dati di sorveglianza per la salute pubblica di malattie trasmissibili, ma potrebbero anche esserci applicazioni per l'environmetrica, l'ingegneria dell'affidabilità, l'econometria o le scienze sociali. Il pacchetto implementa molte comuni procedure di rilevamento di epidemie, come l'algoritmo di Farrington (migliorato) o il metodo GLR-CUSUM negativo binomiale di Höhle & Paul (2008) . È anche incluso un approccio CUSUM innovativo che combina modellazione logistica e logistica multinomiale. Il pacchetto contiene diversi insiemi di dati di casi reali, la capacità di simulare dati di epidemie e di visualizzare i risultati del monitoraggio in maniera temporale, spaziale o spazio-temporale. Una recente panoramica delle procedure di monitoraggio disponibili è fornita in Salmon et al. (2016) .

Per l'analisi retrospettiva di diffusioni epidemiche, il pacchetto fornisce tre infrastrutture di modellazione endemica-epidemica con strumenti per visualizzazione, inferenza di verosimiglianza e simulazione. hhh4() stima modelli per serie temporali di conteggi (multivariate) secondo Paul & Held (2011) e Meyer & Held (2014) . twinSIR() modella la cronologia di eventi SIR (suscettibili-infetti-rimossi) di una popolazione fissa, ad esempio epidemie tra fattorie o reti, come un processo puntuale multivariato come proposto da Höhle (2009) . twinstim() stima modelli di processi puntuali auto-eccitanti per un modello puntuale spazio-temporale di eventi infettivi, ad esempio dati di sorveglianza georeferenziati con marcatura temporale, come proposto da Meyer et al. (2012) . Una recente panoramica delle infrastrutture di modellazione spazio-tempo implementate per i fenomeni epidemici è fornita in Meyer et al. (2017) .

Please cite: Maëlle Salmon, Dirk Schumacher and Michael Höhle: Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance. Journal of Statistical Software 70(10):1-35 (2016)
r-cran-tigger
Infers new Immunoglobulin alleles from Rep-Seq Data
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Summary: Infers the V genotype of an individual from immunoglobulin (Ig) repertoire-sequencing (Rep-Seq) data, including detection of any novel alleles. This information is then used to correct existing V allele calls from among the sample sequences.

High-throughput sequencing of B cell immunoglobulin receptors is providing unprecedented insight into adaptive immunity. A key step in analyzing these data involves assignment of the germline V, D and J gene segment alleles that comprise each immunoglobulin sequence by matching them against a database of known V(D)J alleles. However, this process will fail for sequences that utilize previously undetected alleles, whose frequency in the population is unclear.

TIgGER is a computational method that significantly improves V(D)J allele assignments by first determining the complete set of gene segments carried by an individual (including novel alleles) from V(D)J-rearrange sequences. TIgGER can then infer a subject’s genotype from these sequences, and use this genotype to correct the initial V(D)J allele assignments.

The application of TIgGER continues to identify a surprisingly high frequency of novel alleles in humans, highlighting the critical need for this approach. TIgGER, however, can and has been used with data from other species.

Core Abilities:

  • Detecting novel alleles
  • Inferring a subject’s genotype
  • Correcting preliminary allele calls

Required Input

  • A table of sequences from a single individual, with columns containing the following:
  • V(D)J-rearranged nucleotide sequence (in IMGT-gapped format)
  • Preliminary V allele calls
  • Preliminary J allele calls
  • Length of the junction region
  • Germline Ig sequences in IMGT-gapped fasta format (e.g., as those downloaded from IMGT/GENE-DB)

The former can be created through the use of IMGT/HighV-QUEST and Change-O.

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. (eprint) 31(20):3356–3358 (2017)
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r-other-ascat
Allele-Specific Copy Number Analysis of Tumours
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ASCAT (allele-specific copy number analysis of tumors) is a allele- specific copy number analysis of the in vivo breast cancer genome. It can be used to accurately dissect the allele-specific copy number of solid tumors, simultaneously estimating and adjusting for both tumor ploidy and nonaberrant cell admixture.

Please cite: Peter Van Loo, Silje H Nordgard, Ole Christian Lingjærde, Hege G Russnes, Inga H Rye, Wei Sun, Victor J Weigman, Peter Marynen, Anders Zetterberg, Bjørn Naume, Charles M Perou, Anne-Lise Børresen-Dale and Vessela N Kristensen: Allele-specific Copy Number Analysis of Tumors. (PubMed) PNAS 107(39):16910-5 (2010)
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ragout
Reference-Assisted Genome Ordering UTility
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Ragout (Reference-Assisted Genome Ordering UTility) è uno strumento per creare scaffold a livello di cromosoma che usa riferimenti multipli. Dati dei frammenti iniziali di assemblamento (contig/scaffold) e uno o più riferimenti correlati (completi o bozze), produce un assemblato su scala di cromosoma (come insieme di scaffold).

L'approccio è basato sull'analisi di riarrangiamenti genomici (come inversioni o traslocazioni cromosomiche) tra i genomi di input e sulla ricostruzione della struttura più parsimoniosa del genoma obiettivo.

Ragout ora gestisce genomi sia piccoli sia grandi (di scala e complessità dei Mammiferi). L'assemblaggio di genomi altamente polimorfici è attualmente limitato.

Please cite: Mikhail Kolmogorov, Joel Armstrong, Brian J. Raney, Ian Streeter, Matthew Dunn, Fengtang Yang, Duncan Odom, Paul Flicek, Thomas M. Keane, David Thybert, Benedict Paten and Son Pham: Chromosome assembly of large and complex genomes using multiple references. (PubMed,eprint) Genome Research 28(11):1720-1732 (2018)
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readucks
Nanopore read de-multiplexer (read demux -> readux -> readucks, innit)
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This package is inspired by the demultiplexing options in porechop but without the adapter trimming options - it just demuxes. It uses the parasail library with its Python bindings to do pairwise alignment which provides a considerable speed up over the seqan library used by porechop due to its low-level use of vector processor instructions.

recan
disegno della distanza genetica per analisi di eventi di ricombinazione
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recan è un pacchetto Python che permette di costruire grafici della distanza genetica per esplorare e scoprire eventi di ricombinazione in genomi virali.

Questo metodo è stato precedentemente implementato in strumenti software per il desktop: RAT, Simplot e RDP4.

rna-star
allineatore universale e ultraveloce di sequenziamento di RNA
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Software STAR (Spliced Transcripts Alignment to a Reference) basato su un algoritmo di allineamento per RNA-seq mai descritto prima, che usa la ricerca di seed mappabili sequenziali massimi in array di suffissi non compressi seguita da raggruppamento in cluster dei seed e procedure di cucitura. STAR supera in prestazioni altri strumenti di allineamento di un fattore maggiore di 50 in velocità di mappatura, facendo l'allineamento con il genoma umano di 550 milioni di letture a terminali accoppiati 2 x 76 pb all'ora su un modesto server con 12 core, al contempo migliorando la sensibilità e la precisione dell'allineamento. In aggiunta a rilevazioni de novo senza bias di giunzioni canoniche, STAR può anche scoprire trascritti chimerici (fusione) e splice non canonici, ed è anche in grado di mappare sequenze di RNA a sequenza intera. Usando il sequenziamento Roche 454 di ampliconi PCR di trascrizione inversa, gli autori hanno validato sperimentalmente 1960 nuove giunzioni di splice intergeniche con un tasso di successo dell'80-90%, confermando l'elevata precisione della strategia di mappatura di STAR.

The package is enhanced by the following packages: multiqc
Please cite: Alexander Dobin, Carrie A. Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson and Thomas R. Gingeras: STAR: ultrafast universal RNA-seq aligner. (PubMed,eprint) Bioinformatics 29(1):15-21 (2012)
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Topics: Sequence analysis
rsem
RNA-Seq tramite Expectation-Maximization
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RSEM è un pacchetto software per stimare i livelli di espressione di isoforme e geni da dati RNA-Seq. Il pacchetto RSEM fornisce un'interfaccia facile da usare, gestisce i thread per il calcolo parallelo dell'algoritmo EM, dati di lettura a terminali accoppiati e singoli, punteggi di qualità, letture a lunghezza variabile e stima RSPD. Inoltre, fornisce stime per la media a posteriori e per l'intervallo di confidenza al 95% per i livelli di espressione. Per la visualizzazione, può generare file BAM e Wiggle sia in coordinate dei trascritti sia in coordinate genomiche. I file in coordinate genomiche possono essere visualizzati sia dal navigatore UCSC Genome sia dall'Integrative Genomics Viewer (IGV) del Broad Institute. I file in coordinate dei trascritti possono essere visualizzati da IGV. RSEM ha anche i propri script per generare grafici della profondità di lettura dei trascritti in formato PDF. La funzionalità che distingue RSEM è che i grafici della profondità di lettura possono essere sovrapposti, con la profondità di lettura attribuibile a letture uniche mostrata in nero e quella attribuibile a letture multiple mostrata in rosso. Inoltre, i modelli dedotti dai dati possono anche essere visualizzati. Ultimo, ma non meno importante, RSEM contiene un simulatore.

The package is enhanced by the following packages: multiqc
Please cite: Bo Li and Colin Dewey: RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. (PubMed,eprint) BMC Bioinformatics 12(1):323 (2011)
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ruby-bio
strumenti Ruby per la biologia molecolare computazionale
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Il progetto BioRuby mira ad implementare con il linguaggio Ruby un ambiente integrato per la bioinformatica. La filosofia di progettazione della libreria BioRuby è KISS (Keep It Simple, Stupid; mantieni le cose semplici, stupido) per massimizzare l'usabilità e l'efficienza come strumento quotidiano per i biologi. Il progetto è nato in Giappone ed è supportato dalla Università di Tokyo (Human Genome Center), dalla Università di Kyoto (Bioinformatics Center) e dalla Open Bio Foundation.

salmon
wicked-fast transcript quantification from RNA-seq data
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Salmon is a wicked-fast program to produce a highly-accurate, transcript-level quantification estimates from RNA-seq data. Salmon achieves is accuracy and speed via a number of different innovations, including the use of lightweight alignments (accurate but fast-to-compute proxies for traditional read alignments) and massively-parallel stochastic collapsed variational inference. The result is a versatile tool that fits nicely into many different pipelines. For example, you can choose to make use of the lightweight alignments by providing Salmon with raw sequencing reads, or, if it is more convenient, you can provide Salmon with regular alignments (e.g. computed with your favorite aligner), and it will use the same wicked-fast, state-of-the-art inference algorithm to estimate transcript-level abundances for your experiment.

The package is enhanced by the following packages: multiqc
Please cite: Rob Patro, Geet Duggal, Michael I Love, Rafael A Irizarry and Carl Kingsford: Salmon provides fast and bias-aware quantification of transcript expression. (eprint) Nature Methods 14(4):417-419 (2017)
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samblaster
marks duplicates, extracts discordant/split reads
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Current "next-generation" sequencing technologies cannot tell what exact sequence they will be reading. They take what is available. And if some sequences are read very often, then this needs some extra biomedical thinking. The genome could for instance be duplicated.

samblaster is a fast and flexible program for marking duplicates in read-id grouped paired-end SAM files. It can also optionally output discordant read pairs and/or split read mappings to separate SAM files, and/or unmapped/clipped reads to a separate FASTQ file. When marking duplicates, samblaster will require approximately 20MB of memory per 1M read pairs.

The package is enhanced by the following packages: multiqc
Please cite: Gregory G. Faust and Ira M. Hall: SAMBLASTER: fast duplicate marking and structural variant read extraction. (PubMed,eprint) Bioinformatics 30(17):2503-2505 (2014)
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samclip
filter SAM file for soft and hard clipped alignments
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Most short read aligners perform local alignment of reads to the reference genome. Examples includes bwa mem, minimap2, and bowtie2 (unless in --end-to-end mode). This means the ends of the read may not be part of the best alignment.

This can be caused by:

  • adapter sequences (aren't in the reference)
  • poor quality bases (mismatches only make the alignment score worse)
  • structural variation in your sample compared to the reference
  • reads overlapping the start and end of contigs (including circular genomes)

Read aligners output a SAM file. Column 6 in this format stores the CIGAR string. which describes which parts of the read aligned and which didn't. The unaligned ends of the read can be "soft" or "hard" clipped, denoted with S and H at each end of the CIGAR string. It is possible for both types to be present, but that is not common. Soft and hard don't mean anything biologically, they just refer to whether the full read sequence is in the SAM file or not.

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samtools
elaborazioni di allineamenti di sequenze nei formati SAM, BAM e CRAM
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Samtools è un insieme di utilità che manipolano allineamenti di sequenze di nucleotidi nel formato binario BAM. Importa ed esporta nei formati SAM (Sequence Alignment/Map - allineamento/mappa di sequenze) e CRAM ASCII, effettua ordinamenti, unioni e indicizzazioni e permette di rintracciare letture in qualsiasi regione in modo veloce. È stato progettato per funzionare su flussi di dati ed è in grado di aprire un file BAM o CRAM (non SAM) su un server FTP o HTTP remoto.

The package is enhanced by the following packages: libbio-samtools-perl multiqc
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)
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scrappie
basecaller for Nanopore sequencer
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The Nanopore is a device for DNA/RNA sequencing that does not require an amplification of the material. The polynucleotides are threaded through a pore and while these pass through, the change in the electrostatic potential allows one to identify ("call") the actual base that resides in the pore. Scrappie goes a step further and also attempts to describe modifications to the nucleic acid.

Please cite: Ryan R. Wick, Louise M. Judd and Kathryn E. Holt: Performance of neural network basecalling tools for Oxford Nanopore sequencing.. (eprint) Genome Biol. 20:129 (2019)
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sepp
phylogeny with ensembles of Hidden Markov Models
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The tool SEPP implementing these methods uses ensembles of Hidden Markov Models (HMMs) in different ways, each focusing on a different problem.

SEPP stands for "SATe-enabled Phylogenetic Placement", and addresses the problem of phylogenetic placement of short reads into reference alignments and trees.

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seqkit
toolkit multipiattaforma e ultraveloce per manipolazione di file FASTA/Q
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SeqKit descrive un toolkit completo, multipiattaforma e ultraveloce per elaborazione di FASTA/Q. SeqKit fornisce file binari eseguibili per tutti i principali sistemi operativi, inclusi Windows, Linux e Mac OS X, e può essere usato direttamente senza alcuna dipendenza o pre-configurazione. SeqKit dimostra prestazioni competitive in tempo di esecuzione e uso di memoria in confronto a strumenti simili. L'efficienza e l'usabilità di SeqKit permettono ai ricercatori di compiere rapidamente manipolazioni comuni su file FASTA/Q.

Please cite: Wei Shen, Shuai Le, Yan Li and Fuquan Hu: SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation. (PubMed,eprint) PlosOne 11(10):e0163962 (2016)
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seqmagick
interfaccia simile a imagemagick per Biopython SeqIO
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Seqmagick è una piccola utilità per esporre la conversione di formato di file in BioPython in un modo comodo.

Le funzionalità includono:

  • Modifica di sequenze:
  • rimozione di gap;
  • invertita e complementare invertita;
  • taglio dei residui ad un intervallo;
  • modifica di maiuscole/minuscole;
  • ordinamento per lunghezza o ID.
  • Visualizzazione di informazioni sui file di sequenza.
  • Sottoinsieme dei file di sequenza in base a:
  • posizione;
  • ID;
  • deduplicazione.
  • Filtraggio delle sequenze in base al punteggio di qualità.
  • Taglio degli allineamenti ad una regione di interesse definita dai primer forward e reverse.
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shapeit4
metodo veloce e accurato per stima di aplotipi (fasatura)
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Segmented HAPlotype Estimation and Imputation Tools versione 4 (SHAPEIT4). SHAPEIT4 è un metodo veloce e accurato per stima di aplotipi (conosciuto anche come fasatura) per dati di sequenziamento e array SNP.

The package is enhanced by the following packages: shapeit4-example
Please cite: Olivier Delaneau, Jean-Francois Zagury, Matthew R Robinson, Jonathan L Marchini and Emmanouil T Dermitzakis: Accurate, scalable and integrative haplotype estimation. (eprint) Nature Communications (2019)
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shiny-server
put Shiny web apps online
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Shiny Server lets you put shiny web applications and interactive documents online. Take your Shiny apps and share them with your organization or the world.

Shiny Server lets you go beyond static charts, and lets you manipulate the data. Users can sort, filter, or change assumptions in real-time. Shiny server empower your users to customize your analysis for their specific needs and extract more insight from the data.

shovill
assemblaggio di genomi di isolati batterici da letture Illumina a terminali accoppiati
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Shovill è una pipeline che usa SPAdes come suo nucleo centrale, ma modifica i passaggi prima e dopo il passo di assemblaggio primario, per ottenere risultati simili in un tempo minore. Shovill gestisce anche altri strumenti per assemblaggio, come SKESA, Velvet e Megahit, perciò si può sfruttare la pre- e post-elaborazione fornita da Shovill anche con essi.

smrtanalysis
suite software per sequenziamento in tempo reale di molecole singole
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SMRT® Analysis è una suite di software per bioinformatica potente e open source disponibile per l'analisi di dati di sequenziamento di DNA dalla tecnologia SMRT di Pacific Biosciences. Gli utenti possono scegliere tra una varietà di protocolli di analisi che utilizzano PacBio® e strumenti di terze parti. I protocolli di analisi includono assemblaggio de novo di genoma, mappatura cDNA, rilevazione delle modifiche delle basi del DNA e analisi di ampliconi lunghi per determinare sequenze di consenso con fase.

Questo è un metapacchetto che dipende dai componenti di SMRT Analysis.

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snakemake
sistema di gestione dei flussi di lavoro in Python
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I sistemi di compilazione come GNU Make sono usati frequentemente per creare flussi di lavoro complicati, ad esempio in bioinformatica. Questo progetto ha lo scopo di ridurre la complessità di creare flussi di lavoro fornendo un linguaggio specifico di dominio (DSL) pulito e moderno in stile Python, insieme a un ambiente di esecuzione veloce e confortevole.

Please cite: Johannes Köster and Sven Rahmann: Snakemake-a scalable bioinformatics workflow engine. Bioinformatics (2012)
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snpeff
genetic variant annotation and effect prediction toolbox - tool
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"We are all different!" Geneticists agree to this. Even twins, who are said to be identical are on a molecular level only "mostly" identical. And even within the exact same individual, healthy cells acquire mutations such that we are all genetic mosaics. Changes to individual cells may be induced by environmental factors, e.g. like UV light, or happen sporadically as mishaps during cellular divisions.

Because there are so many genetic differences, and most have just no particular meaning for the development of a phenotype, i.e. most have no effect, it would be nice to have heuristics implemented that direct the researcher towards single-nucleotide polymorphisms (SNPs) that are most likely to be relevant. This identifies the gene that causes or contributes to, e.g, an illness, and possibly also genes that are affected by that change. Such mechanistic understanding of a disease, particularly when multiple genes and multiple genetic variants are contributing to the then "polygenic" phenotype, is at the onset of drug development and increasingly also for selecting individualized therapies in the clinic.

SnpEff is a variant annotation and effect prediction tool. It annotates and predicts the effects of variants on genes (such as amino acid changes). The inputs are predicted variants (SNPs, insertions, deletions and MNPs). The input file is usually obtained as a result of a sequencing experiment, and it is usually in variant call format (VCF).

SnpEff analyzes the input variants. It annotates the variants and calculates the effects they produce on known genes (e.g. amino acid changes).

This package contains the command line tool.

The package is enhanced by the following packages: multiqc
Please cite: Pablo Cingolani, Adrian Platts, Le Lily Wang, Melissa Coon, Tung Nguyen, Luan Wang, Susan J. Land, Douglas M. Ruden and Xiangyi Lu: A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w^1118; iso-2; iso-3. (PubMed,eprint) Fly 6(2):80-92 (2012)
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snpsift
tool to annotate and manipulate genome variants - tool
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SnpSift is a toolbox that allows one to filter and manipulate annotated files. Once the genomic variants have been annotated, one needs to filter them out in order to find the "interesting / relevant variants". Given the large data files, this is not a trivial task (e.g. one cannot load all the variants into XLS spreadsheet). SnpSift helps to perform this VCF file manipulation and filtering required at this stage in data processing pipelines.

This package contains the command line tool.

spades
assemblatore di genomi per singola-cellula e insiemi di dati di isolati
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L'assemblatore di genomi SPAdes - St. Petersburg è pensato per assemblaggi di isolati standard e di MDA batterici da singola cellula. Funziona con letture Illumina o IonTorrent ed è in grado di fornire assemblati ibridi usando letture PacBio e Sanger. Si possono anche fornire contigui aggiuntivi che verranno usati come letture lunghe.

Questo pacchetto fornisce le seguenti catene di elaborazione aggiuntive:

  • metaSPAdes – catena per insiemi di dati metagenomici
  • plasmidSPAdes – catena per estrarre e assemblare plasmidi da insiemi di dati WGS
  • metaplasmidSPAdes – catena per estrarre e assemblare plasmidi da insiemi di dati metagenomici
  • rnaSPAdes – assemblatore de novo di trascrittomi da dati RNA-Seq
  • truSPAdes – modulo per assemblamento di barcode TruSeq
  • biosyntheticSPAdes – modulo per assemblamento di cluster di geni per biosintesi con letture a estremità accoppiate

SPAdes fornisce diversi binari autonomi con un'interfaccia a riga di comando relativamente semplice: conteggio di k-meri (spades-kmercounter), costruzione di grafi di assemblamento (spades-gbuilder) e allineatore da letture lunghe a grafo (spades-gmapper).

Please cite: Anton Bankevich, Sergey Nurk, Dmitry Antipov, Alexey A. Gurevich, Mikhail Dvorkin, Alexander S. Kulikov, Valery M. Lesin, Sergey I. Nikolenko, Son Pham, Andrey D. Prjibelski, Alexey V. Pyshkin, Alexander V. Sirotkin, Nikolay Vyahhi, Glenn Tesler, Max A. Alekseyev and Pavel A. Pevzner: SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. (PubMed,eprint) Journal of Computational Biology 19(5):455-477 (2012)
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spaln
splicing-aware transcript-alignment to genomic DNA
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Spaln (space-efficient spliced alignment) is a stand-alone program that maps and aligns a set of cDNA or protein sequences onto a whole genomic sequence in a single job. It also performs spliced or ordinary alignment after rapid similarity search against a protein sequence database, if a genomic segment or an amino acid sequence is given as a query.

spaln supports a combination of protein sequence database and a given genomic segment and performs rapid similarity searches and (semi-)global alignments of a set of protein sequence queries against a protein sequence database. Spaln adopts multi-phase heuristics that makes it possible to perform the job on a conventional personal computer.

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staden-io-lib-utils
programmi per manipolare file di sequenziamento del DNA
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io_lib dal pacchetto Staden è una libreria di codice per leggere e scrivere file allo scopo di fornire un'interfaccia per la lettura di file di tracciamento (e file esperimento) di scopo generale. È stata compilata e testata su diversi sistemi Unix, MacOS X e MS Windows.

Questo pacchetto contiene i programmi che sono distribuiti con io_lib di Staden per la manipolazione e la conversione di file di dati di sequenziamento, e in particolare di file per manipolare letture corte generate da sequenziatori di seconda e terza generazione e memorizzate in formato SRF.

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stringtie
assemble short RNAseq reads to transcripts
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The abundance of transcripts in a human tissue sample can be determined by RNA sequencing. The exact sequence sampled may be random, depending on the technology used. And it may be short, i.e. shorter than the transcript. At some point, many shorter reads need to be assembled to the model the complete transcripts.

StringTie knows how to assemble of RNA-Seq into potential transcripts without the need of a reference genome and provides a quantification also of the splice variants.

Please cite: Mihaela Pertea, Geo M. Pertea, Corina .M. Antonescu, Tsung-Cheng Chang, Joshua T. Mendell and Steven L. Salzberg: StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nature Biotechnology 33:290–295 (2015)
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sumaclust
raggruppamento veloce in cluster di sequenze genomiche
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Con lo sviluppo del sequenziamento di prossima generazione, sono necessari strumenti efficienti per gestire milioni di sequenze in un tempo ragionevole. Sumaclust è un programma sviluppato da LECA; mira a raggruppare sequenze in cluster in un modo che sia veloce e corretto al tempo stesso. Questo strumento è stato sviluppato per essere adattato ai tipi di dati generati dal DNA metabarcoding, cioè marcatori corti, interamente sequenziati. Sumaclust raggruppa le sequenze in cluster usando lo stesso algoritmo di clustering di UCLUST e CD-HIT. Questo algoritmo è utile principalmente per rilevare sequenze "erronee" create durante i protocolli di amplificazione e sequenziamento e che derivano da sequenze "vere".

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texlive-science
??? missing short description for package texlive-science :-(
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thesias
test di effetti di aplotipi in studi di associazione
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L'obiettivo del programma THESIAS (Testing Haplotype Effects In Association Studies) è di eseguire analisi di associazione basate su aplotipi in individui senza legami di parentela. Questo programma è basato sul modello di massima verosimiglianza descritto in Tregouet et al. 2002 (Hum Mol Genet 2002,11: 2015-2023) ed è collegato all'algoritmo SEM (Tregouet et al. Ann Hum Genet 2004,68: 165-177). THESIAS permette la stima simultanea delle frequenze degli aplotipi e dei loro effetti associati sul fenotipo di interesse. In questo nuovo rilascio di THESIAS, possono essere studiati i risultati quantitativi, qualitativi (analisi logistica e per coppie corrispondenti), categorici e di sopravvivenza. È anche fattibile l'analisi degli aplotipi legati al cromosoma X. Possono anche essere investigati gli effetti degli aplotipi aggiustati per covariate e le interazioni aplotipo x covariata.

Please cite: David-Alexandre Trégouët and Valérie Garelle: "A new JAVA interface implementation of THESIAS: testing haplotype effects in association studies". (eprint) Bioinformatics 23(8):1038-1039 (2007)
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tiddit
structural variant calling
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TIDDIT is a tool to used to identify chromosomal rearrangements using Mate Pair or Paired End sequencing data. TIDDIT identifies intra and inter- chromosomal translocations, deletions, tandem-duplications and inversions, using supplementary alignments as well as discordant pairs.

TIDDIT has two analysis modules. The sv mode, which is used to search for structural variants. And the cov mode that analyse the read depth of a bam file and generates a coverage report.

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tipp
tool for Taxonomic Identification and Phylogenetic Profiling
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TIPP is a modification of SEPP for classifying query sequences (i.e. reads) using phylogenetic placement.

TIPP inserts each read into a taxonomic tree and uses the insertion location to identify the taxonomic lineage of the read. The novel idea behind TIPP is that rather than using the single best alignment and placement for taxonomic identification, it uses a collection of alignments and placements and considers statistical support for each alignment and placement.

TIPP can also be used for abundance estimation by computing an abundance profile on the reads binned to marker genes in a reference dataset.

tnseq-transit
statistical calculations of essentiality of genes or genomic regions
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This is a software that can be used to analyze Tn-Seq datasets. It includes various statistical calculations of essentiality of genes or genomic regions (including conditional essentiality between 2 conditions). These methods were developed and tested as a collaboration between the Sassetti lab (UMass) and the Ioerger lab (Texas A&M)

TRANSIT is capable of analyzing TnSeq libraries constructed with Himar1 or Tn5 datasets.

TRANSIT assumes you have already done pre-processing of raw sequencing files (.fastq) and extracted read counts into a .wig formatted file. The .wig file should contain the counts at all sites where an insertion could take place (including sites with no reads). For Himar1 datasets this is all TA sites in the genome. For Tn5 datasets this would be all nucleotides in the genome.

Please cite: Michael A. DeJesus, Chaitra Ambadipudi, Richard Baker, Christopher Sassetti and Thomas R. Ioerger: TRANSIT - A Software Tool for Himar1 TnSeq Analysis. (PubMed,eprint) PLOS 11(10):e1004401 (2015)
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toil
motore per flusso di lavoro multipiattaforma
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Toil è un motore per flusso di lavoro in Python puro scalabile, efficiente, multipiattaforma e facile da usare. Funziona con svariati bilanciatori del carico di comune utilizzo, come Slurm o Sun Grid Engine. Toil è anche compatibile con CWL (Common Workflow Language) attraverso l'interfaccia "toil-cwl-runner", che questo pacchetto rende disponibile per mezzo del sistema delle alternative di Debian sotto l'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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tombo
identificazione di nucleotidi modificati da dati grezzi di sequenziamento Nanopore
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Tombo è una suite di strumenti principalmente per l'identificazione di nucleotidi modificati da dati di sequenziamento Nanopore. Tombo fornisce anche strumenti per l'analisi e la visualizzazione di segnali grezzi Nanopore.

Please cite: Marcus Stoiber, Joshua Quick, Rob Egan, Ji Eun Lee, Susan Celniker, Robert K. Neely, Nicholas Loman, Len A Pennacchio and James Brown: De novo Identification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing. (eprint) bioRxiv (2016)
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tophat-recondition
post-processore per letture di TopHat non mappate
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tophat-recondition è un post-processore per letture di TopHat non mappate (contenute in unmapped.bam), che le rende compatibili con strumenti a valle (es. la suite Picard, samtools, GATK) (TopHat difetto #17). Aggira anche i bug in TopHat:

  • l'indicatore di SAM "mate is unmapped" non è impostato in nessuna lettura nel file unmapped.bam (TopHat difetto #3)
  • il mapped mate di una lettura non mappata può essere assente da accepted_hits.bam, creando una non corrispondenza tra il file e gli indicatori della lettura non mappata (TopHat difetto #16)
Please cite: Christian Brueffer and Lao H. Saal: A post-processor for TopHat unmapped reads. Bioinformatics 17(1):199 (2016)
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trinculo
toolkit per eseguire associazioni genetiche per fenotipi multi-categoria
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Un toolkit efficiente per eseguire associazioni genetiche per fenotipi multi-categoria. Implementa associazione ordinale e multinomiale incorporando covariate, analisi condizionale, priori non empirici ed empirici e mappatura di dettaglio.

umap-learn
Uniform Manifold Approximation and Projection
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Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t- SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data:

 1. The data is uniformly distributed on a Riemannian manifold;
 2. The Riemannian metric is locally constant (or can be
    approximated as such);
 3. The manifold is locally connected.

From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure.

Please cite: Leland McInnes, John Healy and James Melville: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. (eprint) arXiv (2018)
umis
tools for processing UMI RNA-tag data
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Umis provides tools for estimating expression in RNA-Seq data which performs sequencing of end tags of transcript, and incorporate molecular tags to correct for amplification bias.

There are four steps in this process.

 1. Formatting reads
 2. Filtering noisy cellular barcodes
 3. Pseudo-mapping to cDNAs
 4. Counting molecular identifiers
Please cite: Valentine Svensson, Kedar Nath Natarajan, Lam-Ha Ly, Ricardo J Miragaia, Charlotte Labalette, Iain C Macaulay, Ana Cvejic and Sarah A Teichmann: Power analysis of single-cell RNA-sequencing experiments. (PubMed) Nature methods 14:381–387 (2017)
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uncalled
Utility for Nanopore Current Alignment to Large Expanses of DNA
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Streaming algorithm for mapping raw nanopore signal to DNA references

Enables real-time enrichment or depletion on Oxford Nanopore Technologies (ONT) MinION runs via ReadUntil.

Also supports standalone signal mapping of fast5 reads

Please cite: Sam Kovaka, Yunfan Fan, Bohan Ni, Winston Timp and Michael C. Schatz: Targeted nanopore sequencing by real-time mapping of raw electrical signal with UNCALLED. (eprint) Nature Biotechnology (2020)
unicycler
hybrid assembly pipeline for bacterial genomes
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Unicycler is an assembly pipeline for bacterial genomes. It can assemble Illumina-only read sets where it functions as a SPAdes-optimiser. It can also assembly long-read-only sets (PacBio or Nanopore) where it runs a miniasm+Racon pipeline. For the best possible assemblies, give it both Illumina reads and long reads, and it will conduct a hybrid assembly.

Please cite: Ryan R. Wick, Louise M. Judd, Claire L. Gorrie and Kathryn E. Holt: Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. (PubMed,eprint) PLOS Computational Biology 13(6):e1005595 (2017)
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vg
tools for working with genome variation graphs
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variation graph data structures, interchange formats, alignment, genotyping, and variant calling methods

Variation graphs provide a succinct encoding of the sequences of many genomes. A variation graph (in particular as implemented in vg) is composed of:

  • nodes, which are labeled by sequences and ids
  • edges, which connect two nodes via either of their respective ends
  • paths, describe genomes, sequence alignments, and annotations (such as gene models and transcripts) as walks through nodes connected by edges

This model is similar to a number of sequence graphs that have been used in assembly and multiple sequence alignment. Paths provide coordinate systems relative to genomes encoded in the graph, allowing stable mappings to be produced even if the structure of the graph is changed.

Please cite: Erik Garrison, Jouni Sirén, Adam M Novak, Glenn Hickey, Jordan M Eizenga, Eric T Dawson, William Jones, Shilpa Garg, Charles Markello, Michael F Lin, Benedict Paten and Richard Durbin: Variation graph toolkit improves read mapping by representing genetic variation in the reference. (PubMed) Nature Biotechnology 36(9):875–879 (2018)
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vsearch
strumento per elaborare sequenze metagenomiche
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Strumento versatile e multi-thread a 64 bit per elaborare sequenze metagenomiche, incluse ricerca, raggruppamento in cluster, rilevamento di chimere, dereplicazione, ordinamento, mascheramento e rimescolamento.

Lo scopo di questo progetto è di creare un'alternativa allo strumento USEARCH sviluppato da Robert C. Edgar (2010). Il nuovo strumento dovrebbe:

  • avere un design a 64 bit che gestisca database molto grandi e molto più di 4 GB di memoria;
  • essere accurato almeno quanto usearch o di più;
  • essere veloce almeno quanto usearch o di più.
The package is enhanced by the following packages: vsearch-examples
Please cite: Torbjørn Rognes, Tomáš Flouri, Ben Nichols, Christopher Quince and Frédéric Mahé: VSEARCH: a versatile open source tool for metagenomics. (eprint) PeerJ 4:e2584
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vt
toolset for short variant discovery in genetic sequence data
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vt is a variant tool set that discovers short variants from Next Generation Sequencing data.

Vt-normalize is a tool to normalize representation of genetic variants in the VCF. Variant normalization is formally defined as the consistent representation of genetic variants in an unambiguous and concise way. In vt a simple general algorithm to enforce this is implemented.

The package is enhanced by the following packages: vt-examples
Please cite: Adrian Tan, Gonçalo R. Abecasis and Hyun Min Kang: Unified representation of genetic variants. (PubMed,eprint) Bioinformatics 31(13):2202–2204 (2015)
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workrave
strumento per prevenzione delle lesioni da sforzo ripetuto
Maintainer: Francois Marier
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Workrave è un programma che assiste nel recupero e nella prevenzione di lesioni da sforzo ripetuto (LSR o RSI, Ripetitive Strain Injury). Il programma avverte spesso di fare micro-pause, intervalli di riposo e vincola l'utente al proprio limite giornaliero.

Include un'applet per il vassoio di sistema che funziona con GNOME e KDE e ha capacità di rete per monitorare l'attività dell'utente anche se passare ripetutamente a computer diversi fa parte del suo lavoro.

Workrave offre molte più opzioni di configurazione rispetto agli strumenti simili.

wtdbg2
assemblatore de novo di sequenze per letture lunghe con rumore
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Wtdbg2 è un assemblatore de novo di sequenze per letture lunghe con rumore prodotte da PacBio od Oxford Nanopore Technologies (ONT). Assembla le letture grezze senza correzione di errore e poi costruisce la sequenza di consenso dall'output di assemblaggio intermedio. Wtdbg2 è in grado di assemblare il genoma umano e persino il genoma di 32Gb dell'axolotl ad una velocità dieci volte più elevata di CANU e FALCON, pur producendo contig con accuratezza di basi comparabile.

Durante l'assemblaggio, wtdbg2 taglia le letture in segmenti di 1024 pb, unisce segmenti simili in un vertice e connette i vertici in base alla adiacenza dei segmenti nelle letture. Il grafico risultante è detto grafo di Bruijn fuzzy (FBG). È simile al grafo di De Bruijn, ma permette non corrispondenze/gap e mantiene i percorsi delle letture quando collassa i k-meri. L'uso di FBG distingue wtdbg2 dalla maggior parte degli assemblatori di letture lunghe.

The package is enhanced by the following packages: wtdbg2-examples
Please cite: Jue Ruan and Heng Li: Fast and accurate long-read assembly with wtdbg2. (PubMed,eprint) naturemethods 17(2):155-158 (2020)
yanagiba
filter low quality Oxford Nanopore reads basecalled with Albacore
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Yanagiba is used to filter short or low quality Oxford Nanopore reads which have been basecalled with Albacore. It takes fastq.gz and an Albacore summary file as input. If no Albacore summary file is provided attempt to calculate mean qscore from directly from fastq file using NanoMath. Note: Calculated quality scores appear to be lower for reads called with Metrichor, you may need to lower your minqual setting in this case.

yanosim
read simulator nanopore DRS datasets
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Yanosim has three options:

  1. yanosim model:

    Creates an model of mismatches, insertions and deletions based on an alignment of nanopore DRS reads to a reference. Reads should be aligned to a transcriptome i.e. without spliced alignment, using minimap2. They should have the cs tag. 2. yanosim quantify:

    Quantify the number of reads mapping to each transcript in a reference, so that the right number of reads can be simulated. 3. yanosim simulate:

    Given a model created using yanosim model, and per-transcript read counts created using yanosim simulate, simulate error-prone long-reads from the given fasta file.

Official Debian packages with lower relevance

libsimde-dev
Implementations of SIMD instructions for all systems
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SIMDe provides fast, portable implementations of SIMD intrinsics on hardware which doesn't natively support them, such as calling SSE functions on ARM. There is no performance penalty if the hardware supports the native implementation (e.g., SSE/AVX runs at full speed on x86, NEON on ARM, etc.).

This makes porting code to other architectures much easier in a few key ways:

First, instead of forcing you to rewrite everything for each architecture, SIMDe lets you get a port up and running almost effortlessly. You can then start working on switching the most performance-critical sections to native intrinsics, improving performance gradually. SIMDe lets (for example) SSE/AVX and NEON code exist side-by-side, in the same implementation.

Second, SIMDe makes it easier to write code targeting ISA extensions you don't have convenient access to. You can run NEON code on your x86 machine without an emulator. Obviously you'll eventually want to test on the actual hardware you're targeting, but for most development, SIMDe can provide a much easier path.

SIMDe takes a very different approach from most other SIMD abstraction layers in that it aims to expose the entire functionality of the underlying instruction set. Instead of limiting functionality to the lowest common denominator, SIMDe tries to minimize the amount of effort required to port while still allowing you the space to optimize as needed.

The current focus is on writing complete portable implementations, though a large number of functions already have accelerated implementations using one (or more) of the following:

    SIMD intrinsics from other ISA extensions (e.g., using NEON to implement
SSE).
    Compiler-specific vector extensions and built-ins such as
__builtin_shufflevector and __builtin_convertvector
    Compiler auto-vectorization hints, using:
       OpenMP 4 SIMD
       Cilk Plus
       GCC loop-specific pragmas
       clang pragma loop hint directives
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-mmtf
codifica binaria di strutture biologiche (Python 3)
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MMTF (macromolecular transmission format) è una codifica binaria di strutture biologiche.

Questo pacchetto installa la libreria per Python 3.

r-bioc-rsubread
Subread Sequence Alignment and Counting for R
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Alignment, quantification and analysis of second and third generation sequencing data. Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery.

Can be applied to all major sequencing techologies and to both short and long sequence reads.

Please cite: Yang Liao, Gordon K Smyth and Wei Shi: The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads,. (eprint) Nucleic Acids Research 47(8):e47 (2019)
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Debian packages in contrib or non-free

bcbio
toolkit for analysing high-throughput sequencing data
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This package installs the command line tools 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.

This package builds and having it in Debian unstable helps the Debian developers to synchronize their efforts. But unless a series of external dependencies are not installed manually, the functionality of bcbio in Debian is only a shadow of itself. Please use the official distribution of bcbio for the time being, which means "use conda". The TODO file in the Debian directory should give an overview on progress for Debian packaging.

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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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varscan
variant detection in next-generation sequencing data
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Variant detection in massively parallel sequencing. For one sample, calls SNPs, indels, and consensus genotypes. For tumor-normal pairs, further classifies each variant as Germline, Somatic, or LOH, and also detects somatic copy number changes.

Please cite: Daniel C. Koboldt, Qunyuan Zhang, David E. Larson, Dong Shen, Michael D. McLellan, Ling Lin, Christopher A. Miller, Elaine R. Mardis, Li Ding and Richard K. Wilson: VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing". (PubMed,eprint) Genome Res. 22(3):568-576 (2012)
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vienna-rna
RNA sequence analysis
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The Vienna RNA Package consists of a C code library and several stand-alone programs for the prediction and comparison of RNA secondary structures. It is developed and maintained by the group of Ivo Hofacker in Vienna.

RNA secondary structure prediction through energy minimization is the most used function in the package. It provides three kinds of dynamic programming algorithms for structure prediction:

  • the minimum free energy algorithm of (Zuker & Stiegler 1981) which yields a single optimal structure,
  • the partition function algorithm of (McCaskill 1990) which calculates base pair probabilities in the thermodynamic ensemble, and the suboptimal folding algorithm of (Wuchty et.al 1999) which generates all suboptimal structures within a given energy range of the optimal energy.

For secondary structure comparison, the package contains several measures of distance (dissimilarities) using either string alignment or tree-editing (Shapiro & Zhang 1990). Finally, is provided an algorithm to design sequences with a predefined structure (inverse folding). The RNAforester package is a tool for aligning RNA secondary structures and it's user interface integrates to those of the tools of the Vienna RNA package.

Please cite: Ronny Lorenz, Stephan H. Bernhart, Christian Höner zu Siederdissen, Hakim Tafer, Christoph Flamm, Peter F. Stadler and Ivo L. Hofacker: ViennaRNA Package 2.0. (eprint) Algorithms for Molecular Biology 6(1):26 (2011)
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Debian packages in experimental

libtensorflow-framework2
Computation using data flow graphs for scalable machine learning
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TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

This package ships shared object libtensorflow_framework.so.2.0

A shared object which includes registration mechanisms for ops and kernels. Does not include the implementations of any ops or kernels. Instead, the library which loads libtensorflow_framework.so (e.g. _pywrap_tensorflow_internal.so for Python, libtensorflow.so for the C API) is responsible for registering ops with libtensorflow_framework.so. In addition to this core set of ops, user libraries which are loaded (via TF_LoadLibrary/tf.load_op_library) register their ops and kernels with this shared object directly.

For example, from Python tf.load_op_library loads a custom op library (via dlopen() on Linux), the library finds libtensorflow_framework.so (no filesystem search takes place, since libtensorflow_framework.so has already been loaded by pywrap_tensorflow) and registers its ops and kernels via REGISTER_OP and REGISTER_KERNEL_BUILDER (which use symbols from libtensorflow_framework.so), and pywrap_tensorflow can then use these ops. Since other languages use the same libtensorflow_framework.so, op libraries are language agnostic.

Please cite: Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Rafal Jozefowicz, Yangqing Jia, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Mike Schuster, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu and and Xiaoqiang Zheng: TensorFlow: Large-scale machine learning on heterogeneous systems.. (2015)
Remark of Debian Med team: https://lists.debian.org/debian-devel/2019/10/msg00168.html

The framework should also generate python3-tensorflow as predepencency for streamlit

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

arvados
managing and analyzing biomedical big data
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Arvados is an open source platform for managing, processing, and sharing genomic and other large scientific and biomedical data. With Arvados, bioinformaticians run and scale compute-intensive workflows, developers create biomedical applications, and IT administrators manage large compute and storage resources.

auspice
web app for visualizing pathogen evolution
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Version: 2.10.0-1

Nextstrain is an open-source project to harness the scientific and public health potential of pathogen genome data. We provide a continually- updated view of publicly available data with powerful analytics and visualizations showing pathogen evolution and epidemic spread. Our goal is to aid epidemiological understanding and improve outbreak response.

Remark of Debian Med team: That's a nodejs package. Unfortunately lots of preconditions are missing

Comments welcome whether it makes sense to package about 20 nodejs packages. A list can be found as "debian/TODO" or as comments in debian/control in Salsa.

blat
BLAST-Like Alignment Tool
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Version: 35-1

BLAT on DNA is designed to quickly find sequences of 95% and greater similarity of length 25 bases or more. It may miss more divergent or shorter sequence alignments. It will find perfect sequence matches of 25 bases, and sometimes find them down to 20 bases. BLAT on proteins finds sequences of 80% and greater similarity of length 20 amino acids or more. In practice DNA BLAT works well on primates, and protein blat on land vertebrates.

BLAT is not BLAST. DNA BLAT works by keeping an index of the entire genome in memory. The index consists of all non-overlapping 11-mers except for those heavily involved in repeats. The index takes up a bit less than a gigabyte of RAM. The genome itself is not kept in memory, allowing BLAT to deliver high performance on a reasonably priced Linux box. The index is used to find areas of probable homology, which are then loaded into memory for a detailed alignment. Protein BLAT works in a similar manner, except with 4-mers rather than 11-mers. The protein index takes a little more than 2 gigabytes.

Please cite: W. Jim Kent: BLAT--the BLAST-like alignment tool. (PubMed,eprint) Genome Research 12(4):656-64 (2002)
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chime
COVID-19 Hospital Impact Model for Epidemics
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Version: 0.2.1-1

Penn Medicine - COVID-19 Hospital Impact Model for Epidemics

This tool was developed by the Predictive Healthcare team at Penn Medicine. For questions and comments please see our contact page. Code can be found on Github. Join our Slack channel if you would like to get involved!

The estimated number of currently infected individuals is 533. The 91 confirmed cases in the region imply a 17% rate of detection. This is based on current inputs for Hospitalizations (4), Hospitalization rate (5%), Region size (4119405), and Hospital market share (15%).

An initial doubling time of 6 days and a recovery time of 14.0 days imply an R_0 of 2.71.

Mitigation: A 0% reduction in social contact after the onset of the outbreak reduces the doubling time to 6.0 days, implying an effective R_t of 2.712.712.71.

Remark of Debian Med team: Needs streamlit (see below)
covpipe
pipeline to generate consensus sequences from NGS reads
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CovPipe is a pipeline to generate consensus sequences from NGS reads based on a reference sequence. The pipeline is tailored to be used for SARS-CoV-2 data, but may be used for other viruses.

Genomic variants of your NGS data in comparison to a reference will be determined. These variants will be included into the reference and form the consensus sequences. See below for further details on the determined set of consensus sequences.

ensembl-vep
Variant Effect Predictor predicting the functional effects of genomic variants
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The Ensembl Variant Effect Predictor predicts the functional effects of genomic variants. It has three components:

  • VEP (Variant Effect Predictor) predicts the functional effects of genomic variants.
  • Haplosaurus uses phased genotype data to predict whole-transcript haplotype sequences.
  • Variant Recoder translates between different variant encodings.
Please cite: William McLaren, Laurent Gil, Sarah E. Hunt, Harpreet Singh Riat, Graham R. S. Ritchie, Anja Thormann, Paul Flicek and Fiona Cunningham: The Ensembl Variant Effect Predictor. (PubMed,eprint) Genome Biology 17(1):122 (2016)
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fieldbioinformatics
pipeline with virus identification with Nanopore sequencer
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License: MIT
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Version: 1.1.3-1

This is the ARTIC bioinformatics pipeline for working with virus sequencing data, sequenced with nanopore. It implements a complete bioinformatics protocol to take the output from the Nanopore sequencer and determine consensus genome sequences. Includes basecalling, de-multiplexing, mapping, polishing and consensus generation.

An outbreak of SARS-CoV-2, Ebola, ... something unknown? This software is field-proven.

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flappie
flip-flop basecaller for Oxford Nanopore reads
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Version: 2.1.3+ds-1

Basecall Fast5 reads using flip-flop basecalling.

Features

  • Flip-flop basecalling for the MinION platform

  • R9.4.1 (Native or PCR libraries)

  • R10C (PCR libraries only)
  • Basecalling of 5mC in CpG context for R9.4.1, PromethION platform
graphmap2
highly sensitive and accurate mapper for long, error-prone reads
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GraphMap2 is a highly sensitive and accurate mapper for long, error- prone reads. The mapping algorithm is designed to analyse nanopore sequencing reads, which progressively refines candidate alignments to robustly handle potentially high-error rates and a fast graph traversal to align long reads with speed and high precision (>95%). Evaluation on MinION sequencing data sets against short- and long-read mappers indicates that GraphMap increases mapping sensitivity by 10–80% and maps

95% of bases. GraphMap alignments enabled single-nucleotide variant calling on the human genome with increased sensitivity (15%) over the next best mapper, precise detection of structural variants from length 100 bp to 4 kbp, and species and strain-specific identification of pathogens using MinION reads.

Please cite: Ivan Sović, Mile Šikić, Andreas Wilm, Shannon Nicole Fenlon, Swaine Chen and Niranjan Nagarajan: Fast and sensitive mapping of nanopore sequencing reads with GraphMap. (PubMed,eprint) Nature Communications 7(11307) (2016)
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manta
structural variant and indel caller for mapped sequencing data
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Manta calls structural variants (SVs) and indels from mapped paired-end sequencing reads. It is optimized for analysis of germline variation in small sets of individuals and somatic variation in tumor/normal sample pairs. Manta discovers, assembles and scores large-scale SVs, medium- sized indels and large insertions within a single efficient workflow. The method is designed for rapid analysis on standard compute hardware: NA12878 at 50x genomic coverage is analyzed in less than 20 minutes on a 20 core server, and most WGS tumor/normal analyses can be completed within 2 hours. Manta combines paired and split-read evidence during SV discovery and scoring to improve accuracy, but does not require split- reads or successful breakpoint assemblies to report a variant in cases where there is strong evidence otherwise. It provides scoring models for germline variants in small sets of diploid samples and somatic variants in matched tumor/normal sample pairs. There is experimental support for analysis of unmatched tumor samples as well. Manta accepts input read mappings from BAM or CRAM files and reports all SV and indel inferences in VCF 4.1 format.

Please cite: Xiaoyu Chen, Ole Schulz-Trieglaff, Richard Shaw, Bret Barnes, Felix Schlesinger, Morten Källberg, Anthony J. Cox, Semyon Kruglyak and Christopher T. Saunders: Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. (PubMed,eprint) Bioinformatics 32(8):1220-1222 (2015)
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medaka
sequence correction provided by ONT Research
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Medaka is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods, whilst being much faster.

Features

  • Requires only basecalled data. (.fasta or .fastq)
  • Improved accurary over graph-based methods (e.g. Racon).
  • 50X faster than Nanopolish (and can run on GPUs).
  • Methylation aggregation from Guppy .fast5 files.
  • Benchmarks are provided here.
  • Includes extras for implementing and training bespoke correction networks.
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nanoplot
plotting scripts for long read sequencing data
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NanoPlot provides plotting scripts for long read sequencing data.

These scripts perform data extraction from Oxford Nanopore sequencing data in the following formats:

  • fastq files (optionally compressed)
  • fastq files generated by albacore, guppy or MinKNOW containing additional information (optionally compressed)
  • sorted bam files
  • sequencing_summary.txt output table generated by albacore, guppy or MinKnow basecalling (optionally compressed)
  • fasta files (optionally compressed)
  • multiple files of the same type can be offered simultaneously
Please cite: Wouter De Coster, Svenn D'Hert, Darrin T Schultz, Marc Cruts and Christine Van Broeckhoven: NanoPack: visualizing and processing long-read sequencing data. (PubMed,eprint) Bioinformatics 34(15):2666-2669 (2018)
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ncbi-magicblast
RNA-seq mapping tool
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Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read- pairing is ignored.

Please cite: Grzegorz M. Boratyn, Jean Thierry-Mieg, Danielle Thierry-Mieg, Ben Busby and Thomas L. Madden: Magic-BLAST, an accurate RNA-seq aligner for long and short reads. (PubMed,eprint) BMC Bioinformatics 20(1):405 (2019)
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nextflow
DSL for data-driven computational pipelines
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Nextflow is a bioinformatics workflow manager that enables the development of portable and reproducible workflows. It supports deploying workflows on a variety of execution platforms including local, HPC schedulers, AWS Batch, Google Genomics Pipelines, and Kubernetes. Additionally, it provides support for manage your workflow dependencies through built-in support for Conda, Docker, Singularity, and Modules.

Please cite: Paolo Di Tommaso, Maria Chatzou, Evan W Floden, Pablo Prieto Barja, Emilio Palumbo and Cedric Notredame: Nextflow enables reproducible computational workflows. (PubMed,eprint) Nature Biotechnology 35(4):316-319 (2017)
nextstrain-ncov
Nextstrain build for novel coronavirus (nCoV)
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This is a Nextstrain build for novel coronavirus, alternately known as hCoV-19 or SARS-CoV-2, visible at https://nextstrain.org/ncov .

Remark of Debian Med team: needs auspice
nf-core-artic
nf-core ARTIC field bioinformatics viral genome pipeline
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RNA-seq workflow for nextflow, meant to form a a bioinformatics pipeline for working with virus sequencing data sequenced with nanopore. This is a reimplementation for the nextflow workflow suite of the ARTIC fieldbioinformatics protocol.

This package at the very moment is not much more than a technical exercise. Upstream tagged it as "under development" - and that is what it is here, too.

Please cite: Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso and Sven Nahnsen.: The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. (2020)
oncofuse
predicting oncogenic potential of gene fusions
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Oncofuse is a framework designed to estimate the oncogenic potential of de-novo discovered gene fusions. It uses several hallmark features and employs a bayesian classifier to provide the probability of a given gene fusion being a driver mutation.

Please cite: Mikhail Shugay, Iñigo Ortiz de Mendíbil, José L. Vizmanos and Francisco J. Novo: Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions. (PubMed,eprint) Bioinformatics 29(20):2539–2546 (2013)
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optitype
precision HLA typing from next-generation sequencing data
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OptiType is a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate 4-digit HLA genotyping predictions from NGS data by simultaneously selecting all major and minor HLA Class I alleles.

Please cite: András Szolek, Benjamin Schubert, Christopher Mohr, Marc Sturm, Magdalena Feldhahn and Oliver Kohlbacher: OptiType: precision HLA typing from next-generation sequencing data. (PubMed,eprint) Bioinformatics 30(23):3310–3316 (2014)
pangolin
Phylogenetic Assignment of Named Global Outbreak LINeages
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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.

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pomoxis
analysis components from Oxford Nanopore Research
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Pomoxis comprises a set of basic bioinformatic tools tailored to nanopore sequencing. Notably tools are included for generating and analysing draft assemblies. Many of these tools are used by the research data analysis group at Oxford Nanopore Technologies.

Features

  • Wraps third party tools with known good default parameters and methods of use.
  • Creates an isolated environment with all third-party tools.
  • Streamlines common short analysis chains.
  • Integrates into katuali for performing more complex analysis pipelines.
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python3-idseq-dag
Pipeline engine for IDseq (Python 3)
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Idseq_dag is the pipeline execution engine for idseq (see idseq.net). It is a pipelining system that implements a directed acyclic graph (DAG) where the nodes (steps) correspond to individual python classes. The graph is defined using JSON.

The pipeline would be executed locally with local machine resources. idseq-dag could be installed inside a docker container and run inside the container.

This package installs the library for Python 3.

python3-scanpy
Single-Cell Analysis in Python
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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)
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qualimap
evaluating next generation sequencing alignment data
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Qualimap 2 provides both a Graphical User Interface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.

Supported types of experiments include:

  • Whole-genome sequencing
  • Whole-exome sequencing
  • RNA-seq (speical mode available)
  • ChIP-seq

Qualimap examines sequencing alignment data in SAM/BAM files according to the features of the mapped reads and provides an overall view of the data that helps to the detect biases in the sequencing and/or mapping of the data and eases decision-making for further analysis.

Qualimap provides multi-sample comparison of alignment and counts data.

  • Fast analysis accross the reference of genome coverage and nucleotide distribution;
  • Easy to interpret summary of the main properties of the alignment data;
  • Analysis of the reads mapped inside/outside of the regions provided in GFF format;
  • Computation and analysis of read counts obtained from intersectition of read alignments with genomic features;
  • Analysis of the adequasy of the sequencing depth in RNA-seq experiments;
  • Multi-sample comparison of alignment and counts data;
  • Clustering of epigenomic profiles.
Please cite: Fernando García-Alcalde, Konstantin Okonechnikov, José Carbonell, Luis M. Cruz, Stefan Götz, Sonia Tarazona, Joaquín Dopazo, Thomas F. Meyer and Ana Conesa: Qualimap: evaluating next-generation sequencing alignment data. (PubMed,eprint) Bioinformatics 28(20):2678-2679 (2012)
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quast
Quality Assessment Tool for Genome Assemblies
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QUAST evaluates genome assemblies. For metagenomes, please see MetaQUAST project. It works both with and without a given reference genome. The tool accepts multiple assemblies, thus it allows for comparisons.

Please cite: Alla Mikheenko, Andrey Prjibelski, Vladislav Saveliev, Dmitry Antipov and Alexey Gurevich: Versatile genome assembly evaluation with QUAST-LG. Bioinformatics 34(13):i142-i150 (2018)
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r-cran-covid19
GNU R Coronavirus COVID-19 data acquisition and visualization
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This GNU R package provides pre-processed, ready-to-use, tidy format datasets of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. The latest data are downloaded in real-time, processed and merged with demographic indicators from several trusted sources. The package implements advanced data visualization across the space and time dimensions by means of animated mapping. Besides worldwide data, the package includes granular data for Italy, Switzerland and the Diamond Princess.

r-other-fastbaps
A fast genetic clustering algorithm that approximates a Dirichlet Process Mixture model
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Takes a multiple sequence alignment as input and clusters according to the 'no-admixture' model. It combines ideas from the Bayesian Hierarchical Clustering algorithm of Heller et al. and hierBAPS to produce a rapid and accurate clustering algorithm.

Please cite: Gerry Tonkin-Hill, John A Lees, Stephen D Bentley, Simon D W Frost and Jukka Corander: Fast hierarchical Bayesian analysis of population structure. (PubMed,eprint) Nucleic Acids Research 47(11):5539–5549 (2019)
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rosa
Removal of Spurious Antisense in biological RNA sequences
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In stranded RNA-Seq experiments it is possible to detect and measure antisense transcription, important since antisense transcripts impact gene transcription in several different ways. Stranded RNA-Seq determines the strand from which an RNA fragment originates, and so can be used to identify where antisense transcription may be implicated in gene regulation.

However, spurious antisense reads are often present in experiments, and can manifest at levels greater than 1% of sense transcript levels. This is enough to disrupt analyses by causing false antisense counts to dominate the set of genes with high antisense transcription levels.

The RoSA (Removal of Spurious Antisense) tool detects the presence of high levels of spurious antisense transcripts, by:

  • analysing ERCC spike-in data to find the ratio of antisense:sense transcripts in the spike-ins; or
  • using antisense and sense counts around splice sites to provide a set of gene-specific estimates; or
  • both.

Once RoSA has an estimate of the spurious antisense, expressed as a ratio of antisense:sense counts, RoSA will calculate a correction to the antisense counts based on the ratio. Where a gene-specific estimate is available for a gene, it will be used in preference to the global estimate obtained from either spike-ins or spliced reads.

This package provides the library for the statistics suite R.

sailfish
RNA-seq expression estimation
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RNA-seq is a technology to read at least parts of individual RNA sequences of a tissue sample. After assigning these reads to genes that are likely responsible to have coded for them (mapping), this gives an insight (estimate) about how much these genes have been active (expressed) in that sample. The trickier bits in that process to address is the similarity of genes and the genes being capable to variably but deterministically skip parts of their sequence to be read (introns). A single variantly spliced gene may then yield different sequences (isoforms) and the RNA-seq evaluation better informs about this. It may be relevant for a disease.

Sailfish is particularly good (efficient) in this process. It tricks the complexity by introducing an intermediate level of artificial very short reads to which the alternative splicing is of no concern. That can then be addressed by "telephone-book"-like hashing techniques that are easy and lightning fast. The final presentation is then found to be competitive with established mappers like eXpress and Cufflinks.

Please cite: Rob Patro, Stephen M Mount and Carl Kingsford: Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. (PubMed) Nature Biotechnology 32(5):462-464 (2014)
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seqwish
alignment to variation graph inducer
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Seqwish implements a lossless conversion from pairwise alignments between sequences to a variation graph encoding the sequences and their alignments. As input we typically take all-versus-all alignments, but the exact structure of the alignment set may be defined in an application specific way. This algorithm uses a series of disk-backed sorts and passes over the alignment and sequence inputs to allow the graph to be constructed from very large inputs that are commonly encountered when working with large numbers of noisy input sequences. Memory usage during construction and traversal is limited by the use of sorted disk-backed arrays and succinct rank/select dictionaries to record a queryable version of the graph.

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signalalign
HMM-HDP models for MinION signal alignments
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MinION signal-level alignment and methylation detection using hidden Markov Models with hierarchical Dirichlet process kmer learning.

Nanopore sequencing is based on the principal of isolating a nanopore in a membrane separating buffered salt solutions, then applying a voltage across the membrane and monitoring the ionic current through the nanopore. The Oxford Nanopore Technologies (ONT) MinION sequences DNA by recording the ionic current as DNA strands are enzymatically guided through the nanopore. SignalAlign will align the ionic current from the MinION to a reference sequence using a trainable hidden Markov model (HMM). The emissions model for the HMM can either be the table of parametric normal distributions provided by ONT or a hierarchical Dirichlet process (HDP) mixture of normal distributions. The HDP models enable mapping of methylated bases to your reference sequence.

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streamlit
fast way to build custom ML tools
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Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file. No need to mess with HTTP requests, HTML, JavaScript, etc. All you need is your favorite editor and a browser.

Remark of Debian Med team: Help is urgently needed - no idea how to package this :-(

This is a machine learning framework which is required by chime. Needs python3-tensorflow

strelka
strelka2 germline and somatic small variant caller
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Strelka2 is a fast and accurate small variant caller optimized for analysis of germline variation in small cohorts and somatic variation in tumor/normal sample pairs. The germline caller employs an efficient tiered haplotype model to improve accuracy and provide read-backed phasing, adaptively selecting between assembly and a faster alignment- based haplotyping approach at each variant locus. The germline caller also analyzes input sequencing data using a mixture-model indel error estimation method to improve robustness to indel noise. The somatic calling model improves on the original Strelka method for liquid and late- stage tumor analysis by accounting for possible tumor cell contamination in the normal sample. A final empirical variant re-scoring step using random forest models trained on various call quality features has been added to both callers to further improve precision.

Please cite: Sangtae Kim, Konrad Scheffler, Aaron L. Halpern, Mitchell A. Bekritsky, Eunho Noh, Morten Källberg, Xiaoyu Chen, Yeonbin Kim, Doruk Beyter, Peter Krusche and Christopher T. Saunders: Strelka2: fast and accurate calling of germline and somatic variants. (PubMed) Nature Methods 15(8):591–594 (2018)
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ufasta
utility to manipulate fasta files
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Description of ufasta subcommands:

  • one: remove the new lines in the data section. Hence, all the sequences are written on one line. In some sense, it is the opposite of the format subcommand.
  • format: reformat the data sections. The data is written in lines of the same length, it can changes the content in upper/lower case.
  • sizes: print the amount of sequence in each section
  • head: like UNIX head. Display the first 10 sequences
  • tail: like UNIX tail. Display the last 10 sequences
  • rc: reverse complement every sequence
  • n50, stats: display stats about the sequences: N50, E size, total size, etc.
  • extract: extract a sequence whose header match given names
  • hsort, sort: sort file based on header content
  • dsort: sort the data sections
  • hgreap: output sequences whose header match the regular expression
  • dgresp: output sequences whose sequence match the regular expression
  • split: split a fasta file into many files
vadr
classification and annotation of viral sequences
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VADR (Viral Annotation DefineR) is a suite of tools for classifying and analyzing sequences homologous to a set of reference models of viral genomes or gene families. It has been mainly tested for analysis of Norovirus and Dengue virus sequences in preparation for submission to the GenBank database and finds its application also for the ongoing pandemics.

Please cite: Alejandro A Schäffer, Eneida L Hatcher, Linda Yankie, Lara Shonkwiler, J Rodney Brister, Ilene Karsch-Mizrachi and Eric P Nawrocki: VADR: validation and annotation of virus sequence submissions to GenBank. (eprint) bioRxiv (2020)
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*Popularitycontest results: number of people who use this package regularly (number of people who upgraded this package recently) out of 245986