.. currentmodule:: HTSeq
HTSeq is a Python package that provides infrastructure to process data from high-throughput sequencing assays.
- Please see the chapter :ref:`tour` first for an overview on the kind of analysis you can do with HTSeq and the design of the package, and then look at the reference documentation.
- While the main purpose of HTSeq is to allow you to write your own analysis scripts, customized to your needs, there are also a couple of stand-alone scripts for common tasks that can be used without any Python knowledge. See the Scripts section in the overview below for what is available.
- For downloads and installation instructions, see :ref:`install`.
HTSeq is currently developed by Fabio Zanini at University of New South Wales in Sydney (fabio dot zanini at unsw dot edu dot au) and Simon Anders (anders at embl dot de) at EMBL Heidelberg (Genome Biology Unit).
HTSeq is described in the following publication:
Simon Anders, Paul Theodor Pyl, Wolfgang HuberHTSeq --- A Python framework to work with high-throughput sequencing dataBioinformatics (2014), in print, online at doi:10.1093/bioinformatics/btu638
If you use HTSeq in research, please cite this paper in your publication.
-
Download links and installation instructions can be found here
-
The Tour shows you how to get started. It explains how to install HTSeq, and then demonstrates typical analysis steps with explicit examples. Read this first, and then see the Reference for details.
-
This chapter explains typical usage patterns for HTSeq by explaining in detail three different solutions to the same programming task.
-
This chapter explorer in detail the use case of counting the overlap of reads with annotation features and explains how to implement custom logic by writing on's own customized counting scripts
Reference documentation
The various classes of HTSeq are described here.
-
A brief overview over all classes.
-
In order to represent sequences and reads (i.e., sequences with base-call quality information), the classes :class:`Sequence` and :class:`SequenceWithQualities` are used. The classes :class:`FastaReader` and :class:`FastqReader` allow to parse FASTA and FASTQ files.
-
The classes :class:`GenomicInterval` and :class:`GenomicPosition` represent intervals and positions in a genome. The class :class:`GenomicArray` is an all-purpose container with easy access via a genomic interval or position, and :class:`GenomicArrayOfSets` is a special case useful to deal with genomic features (such as genes, exons, etc.)
-
To process the output from short read aligners in various formats (e.g., SAM), the classes described here are used, to represent output files and alignments, i.e., reads with their alignment information.
-
The classes :class:`GenomicFeature` and :class:`GFF_Reader` help to deal with genomic annotation data.
-
This page describes classes to parse VCF, Wiggle and BED files.
-
Scripts
The following scripts can be used without any Python knowledge.
-
Given a FASTQ or SAM file, this script produces a PDF file with plots depicting the base calls and base-call qualities by position in the read. This is useful to assess the technical quality of a sequencing run.
-
Given a SAM file with alignments and a GFF file with genomic features, this script counts how many reads map to each feature.
-
Appendices
HTSeq is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
The full text of the GNU General Public License, version 3, can be found here: http://www.gnu.org/licenses/gpl-3.0-standalone.html