Analysing high-throughput sequencing data in Python with HTSeq 2.0

• DOI: 10.1093/bioinformatics/btac166
• Publication Date: 2022-03-18T04:24:03Z
• Authors: Givanna H Putri; Simon Anders; Paul Theodor Pyl; John E Pimanda; Fabio Zanini
• Keywords: 0301 basic medicine; Genomics (q-bio.GN); 0303 health sciences; High-Throughput Nucleotide Sequencing; Documentation; Genomics; Quantitative Biology - Quantitative Methods; Applications Notes; 03 medical and health sciences; FOS: Biological sciences; Quantitative Biology - Genomics; Licensure; Software; Quantitative Methods (q-bio.QM)

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.