Source-linked AI summary
Analysing high-throughput sequencing data in Python with HTSeq 2.0
Givanna H Putri, Simon Anders, Paul Theodor Pyl, John E Pimanda, Fabio Zanini
TL;DR
High-throughput sequencing and single-cell omics analyses need flexible open-source tools beyond standardized commercial pipelines. HTSeq 2.0 expands its Python API and counting workflows, adds sparse genomic-data representations, and modernizes testing and deployment. The authors conclude that it supports bulk and single-cell RNA-Seq quantification and broader multi-omic analysis.
Problem
Open-source sequencing workflows need a general-purpose tool that supports diverse omics analyses, including increasingly common single-cell RNA sequencing.
Method
The paper develops HTSeq 2.0 by extending htseq-count, adding StretchVector for sparse genomic islands, and modernizing the Python package and development infrastructure.
Results
HTSeq 2.0 supports high-throughput sequencing analysis and gene-expression quantification for bulk and single-cell RNA-Seq experiments.
Takeaways & Limitations
The authors believe HTSeq 2.0 will be convenient for exploring and quantifying results across multiple omic modalities.
Abstract
from arXiv · showhide
Summary: HTSeq 2.0 provides a more extensive API including a new representation for sparse genomic data, enhancements in 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 Licence and 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. Contact: fabio.zanini@unsw.edu.au
3 Bioquant Center, University of Heidelberg, 69120 Heidelberg, Germany
The listed affiliation is the Division of Surgery, Oncology and Pathology within the Department of Clinical Sciences Lund at Lund University in Sweden, with Bioquant Center affiliation in Heidelberg, Germany.
- The affiliation names the Division of Surgery, Oncology and Pathology.
- The division belongs to the Department of Clinical Sciences Lund, Faculty of Medicine, Lund University.
- The listed institutional location is Lund, Sweden, alongside Bioquant Center in Heidelberg, Germany.
6 Department of Haematology, the Prince of Wales Hospital, Sydney, NSW, Australia
The listed affiliations include the Department of Pathology at the University of New South Wales and the Cellular Genomics Futures Institute at the same university in Sydney, Australia; correspondence is directed to the designated author.
- The Cellular Genomics Futures Institute is located at the University of New South Wales in Sydney, Australia.
- The paper marks a designated contact with a plus sign indicating whom correspondence should address.
Text
HTSeq 2.0 extends a Python toolkit for high-throughput sequencing with single-cell support, richer counting and sparse-data structures, while modernizing its software infrastructure.
- HTSeq 2.0 extends a general-purpose Python sequencing toolkit and htseq-count to diverse omics analyses, including scRNA-Seq.
- htseq-count processes multiple BAM files in one call, supports parallel quantification, and provides additional output formats for single-cell experiments.Genomic features are loaded once from the GTF file, and distinct BAM files can be allocated to separate cores.
- StretchVector represents sparse genomic islands with dense NumPy arrays and associated start-end coordinates, while StepVector represents piecewise-constant sparse data.
- The package was modernized for Python 3 with continuous integration, automated binary releases, unit tests, bug fixes, and HTSlib-based file-type autodetection.
- The authors conclude that HTSeq 2.0 supports bulk and single-cell RNA-Seq quantification and broader high-throughput sequencing analysis.
- The authors believe the improvements will make HTSeq 2.0 convenient for exploring and quantifying results across multiple omic modalities.
Figure
Figure 1 summarizes HTSeq 2.0 improvements to htseq-count and sparse genomic-data representations, contrasting conventional and more detailed counting outputs with two sparsity structures.
- htseq-count improvements: Panel A depicts multicore processing of cell-specific BAM files and new output formats for single-cell data.
- htseq-count improvements: Panel B shows a conventional gene-cell matrix that collapses exon-level reads into one gene count, whereas panel C retains exon quantification and gene membership.
- Sparse data representations: Panel D represents piecewise-constant sparse genomic data with StepVector, while panel E represents sparse genomic islands with StretchVector.