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

Bioinformatics. 2022 May 13;38(10):2943-2945. doi: 10.1093/bioinformatics/btac166.

Authors

Givanna H Putri^{1

2}, Simon Anders³, Paul Theodor Pyl⁴, John E Pimanda^{1

2

5

6}, Fabio Zanini^{1

2

7}

Affiliations

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

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Documentation
Genomics
High-Throughput Nucleotide Sequencing*
Licensure
Software*