Analysing high-throughput sequencing data in Python with HTSeq 2.0

Givanna H. Putri; Simon Anders; Paul Theodor Pyl; John E. Pimanda; Fabio Zanini

doi:10.1093/bioinformatics/btac166

Analysing high-throughput sequencing data in Python with HTSeq 2.0

Givanna H. Putri, Simon Anders, Paul Theodor Pyl, John E. Pimanda, Fabio Zanini

Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review

Sammanfattning

HTSeq 2.0 provides a more extensive application programming interface including a new representation for sparse genomic data, enhancements for htseq-count to suit single-cell omics, a new script for data using cell and molecular barcodes, improved documentation, testing and deployment, bug fixes and Python 3 support.

Originalspråk	engelska
Sidor (från-till)	2943-2945
Antal sidor	3
Tidskrift	Bioinformatics
Volym	38
Nummer	10
DOI	https://doi.org/10.1093/bioinformatics/btac166
Status	Published - 2022 maj 15

Ämnesklassifikation (UKÄ)

Bioinformatik och systembiologi

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10.1093/bioinformatics/btac166Licens: CC BY

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title = "Analysing high-throughput sequencing data in Python with HTSeq 2.0",

abstract = "HTSeq 2.0 provides a more extensive application programming interface including a new representation for sparse genomic data, enhancements for htseq-count to suit single-cell omics, a new script for data using cell and molecular barcodes, improved documentation, testing and deployment, bug fixes and Python 3 support. ",

author = "Putri, {Givanna H.} and Simon Anders and Pyl, {Paul Theodor} and Pimanda, {John E.} and Fabio Zanini",

year = "2022",

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AB - HTSeq 2.0 provides a more extensive application programming interface including a new representation for sparse genomic data, enhancements for htseq-count to suit single-cell omics, a new script for data using cell and molecular barcodes, improved documentation, testing and deployment, bug fixes and Python 3 support.

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Analysing high-throughput sequencing data in Python with HTSeq 2.0

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Ämnesklassifikation (UKÄ)

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