Cited by 21,464Genomics and Phylogenetic StudiesSingle-cell and spatial transcriptomicsGene expression and cancer classification

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HTSeq—a Python framework to work with high-throughput sequencing data

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Anders, Simon, Pyl, Paul Theodor, Huber, Wolfgang·2015·Bioinformatics

DOI 10.1093/bioinformatics/btu638PMC PMC4287950

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Abstract

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq . Contact: sanders@fs.tum.de

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Fig. 1. (a) The SAM_Alignment class as an example of an HTSeq data record: subsets of the content are bundled in object-valued fields, using classes (here SequenceWithQualities and GenomicInterval) that are also used in other data records to provide a common view on diverse data types. (b) The cigar field in a SAM_alignment object presents the detailed structure of a read alignment as a list of CigarOperation. This allows for convenient downstream processing of complicated alignment structures, such as the one given by the cigar string on top and illustrated in the middle. Five CigarOperation objects, with slots for the columns of the table (bottom) provide the data from the cigar string, along with the inferred coordinates of the affected regions in read (‘query’) and referenceCC BY · PubMed Central

Fig. 2. Using the class GenomicArrayOfSets to represent overlapping annotation metadata. The indicated features are assigned to the array, which then represents them internally as steps, each step having as value a set whose elements are references to the features overlapping the stepCC BY · PubMed Central

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Authors: Anders, S., Pyl, P. T., & Huber, W.
Affiliations: European Molecular Biology Laboratory (DE)
Corresponding author: Anders, Simon
ORCID: Anders, Simon
Pyl, Paul Theodor
Huber, Wolfgang
Venue: Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2014-09-25
Volume: 31
Issue: 2
Pages: 166-169
ISSN: 1367-4803 (ISSN-L)
1367-4811
DOI: 10.1093/bioinformatics/btu638
PMC: PMC4287950
OpenAlex: W2134526812
Cited by: 21,464
References: 18
Citations by year: 2026: 1267 · 2025: 1823 · 2024: 2188 · 2023: 2215 · 2022: 2453 · 2021: 2749 · 2020: 2323 · 2019: 1943 · 2018: 1658 · 2017: 1272 · 2016: 901 · 2015: 516 · 2014: 134
FWCI: 383.82
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor, 770 d)
Locations: Bioinformatics (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: European Commission (305626)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords: HTSeq, high-throughput sequencing data, Python library, RNA-seq, read counting, differential expression analysis, variant calls, gene models
MeSH terms: Gene Expression Regulation, Humans, Software, Genome, Human, Genomics, High-Throughput Nucleotide Sequencing

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