The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
HTSeq—a Python framework to work with high-throughput sequencing data
Open accessPublished version of a preprint
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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“Age-matched male mice were used in this study as these have been widely used for the aging studies and shown to correspond more closely to the phenotype of human aging55,56 and were ten to twenty seven months of age as described in the corresponding figure legends.”
From TERT activation targets DNA methylation and multiple aging hallmarks (2024) · cites this paper as [56]
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“These methods allocate multi-mapping reads among transcript and output within-sample normalized values corrected for sequencing biases [35, 41, 43].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [35]
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“Raw read counts are assembled outside limma using tools such as featureCounts (29), HTSeq-counts (30) or RSEM (31).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [30]
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Metadata
- Authors
- Anders, S., Pyl, P. T., & Huber, W.
- Affiliations
- European Molecular Biology Laboratory (DE)
- Corresponding author
- Anders, Simon
- 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
- 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
- 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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