Analysing high-throughput sequencing data in Python with HTSeq 2.0

SUMMARY: 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...

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Autores principales: Putri, Givanna H, Anders, Simon, Pyl, Paul Theodor, Pimanda, John E, Zanini, Fabio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113351/
https://www.ncbi.nlm.nih.gov/pubmed/35561197
http://dx.doi.org/10.1093/bioinformatics/btac166
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author Putri, Givanna H
Anders, Simon
Pyl, Paul Theodor
Pimanda, John E
Zanini, Fabio
author_facet Putri, Givanna H
Anders, Simon
Pyl, Paul Theodor
Pimanda, John E
Zanini, Fabio
author_sort Putri, Givanna H
collection PubMed
description SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: HTSeq 2.0 is released as an open-source software under the GNU General Public License and is available from the Python Package Index at https://pypi.python.org/pypi/HTSeq. The source code is available on Github at https://github.com/htseq/htseq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-91133512022-05-18 Analysing high-throughput sequencing data in Python with HTSeq 2.0 Putri, Givanna H Anders, Simon Pyl, Paul Theodor Pimanda, John E Zanini, Fabio Bioinformatics Applications Notes SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: HTSeq 2.0 is released as an open-source software under the GNU General Public License and is available from the Python Package Index at https://pypi.python.org/pypi/HTSeq. The source code is available on Github at https://github.com/htseq/htseq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-03-21 /pmc/articles/PMC9113351/ /pubmed/35561197 http://dx.doi.org/10.1093/bioinformatics/btac166 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Putri, Givanna H
Anders, Simon
Pyl, Paul Theodor
Pimanda, John E
Zanini, Fabio
Analysing high-throughput sequencing data in Python with HTSeq 2.0
title Analysing high-throughput sequencing data in Python with HTSeq 2.0
title_full Analysing high-throughput sequencing data in Python with HTSeq 2.0
title_fullStr Analysing high-throughput sequencing data in Python with HTSeq 2.0
title_full_unstemmed Analysing high-throughput sequencing data in Python with HTSeq 2.0
title_short Analysing high-throughput sequencing data in Python with HTSeq 2.0
title_sort analysing high-throughput sequencing data in python with htseq 2.0
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113351/
https://www.ncbi.nlm.nih.gov/pubmed/35561197
http://dx.doi.org/10.1093/bioinformatics/btac166
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