Analysing high-throughput sequencing data in Python with HTSeq 2.0-Reference-Cited by-同舟云学术

Analysing high-throughput sequencing data in Python with HTSeq 2.0

Published:2022-03-21 Issue:10 Volume:38 Page:2943-2945
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Putri Givanna H¹²^ORCID,Anders Simon³^ORCID,Pyl Paul Theodor⁴^ORCID,Pimanda John E¹²⁵⁶^ORCID,Zanini Fabio¹²⁷^ORCID

Affiliation:

1. School of Clinical Medicine, University of New South Wales , Sydney, NSW 2033, Australia

2. Adult Cancer Program, Lowy Cancer Research Centre, University of New South Wales , Sydney, NSW 2033, Australia

3. Bioquant Center, University of Heidelberg , 69120 Heidelberg, Germany

4. Division of Surgery, Oncology and Pathology, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University , Lund, Sweden

5. Department of Pathology, School of Medical Sciences, University of New South Wales , Sydney, NSW 2052, Australia

6. Department of Haematology, The Prince of Wales Hospital , Sydney, NSW 2031, Australia

7. Cellular Genomics Futures Institute, University of New South Wales , Sydney, NSW 2033, Australia

Abstract

Abstract 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.

Funder

European Molecular Biology Organization Fellowship

National Health and Medical Research Council

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btac166/43192435/btac166.pdf

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