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HTSeq – A Python framework to work with high-throughput sequencing data

View ORCID ProfileSimon Anders, View ORCID ProfilePaul Theodor Pyl, View ORCID ProfileWolfgang Huber
doi: https://doi.org/10.1101/002824
Simon Anders
Genome Biology Unit, European Molecular Biology Laboratory, 69111 Heidelberg, Germany
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Paul Theodor Pyl
Genome Biology Unit, European Molecular Biology Laboratory, 69111 Heidelberg, Germany
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Wolfgang Huber
Genome Biology Unit, European Molecular Biology Laboratory, 69111 Heidelberg, Germany
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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 work flows, 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, 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: HTSeq is released as open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index https://pypi.python.org/pypi/HTSeq.

Contact: sanders{at}fs.tum.de

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 19, 2014.
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HTSeq – A Python framework to work with high-throughput sequencing data
Simon Anders, Paul Theodor Pyl, Wolfgang Huber
bioRxiv 002824; doi: https://doi.org/10.1101/002824
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HTSeq – A Python framework to work with high-throughput sequencing data
Simon Anders, Paul Theodor Pyl, Wolfgang Huber
bioRxiv 002824; doi: https://doi.org/10.1101/002824

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