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HTSeq is a Python package that provides infrastructure to process data from high-throughput sequencing assays. HTSeq includes parsers for common file formats for a variety of types of input data and is suitable as a general platform for a diverse range of tasks. A core component of HTSeq is a container class that simplifies working with data associated with genomic coordinates, i.e. values attributed to genomic positions (e.g. read coverage) or to genomic intervals (e.g. genomic features such as exons or genes). Two stand-alone applications developed with HTSeq are distributed with the package, namely htseq-qa for read quality assessment and htseq-count for preprocessing RNA-Seq alignments for differential expression calling

HTSeq is described in the following publication:

Simon Anders, Paul Theodor Pyl, Wolfgang Huber
HTSeq — A Python framework to work with high-throughput sequencing data
Bioinformatics (2014), in print, online at doi:10.1093/bioinformatics/btu638

Mandatory arguments

  • Input SAM/BAM files: The alignment_files contains the aligned reads in the SAM or BAM format.

Make sure to use a splicing-aware aligner such as TopHat. HTSeq-count makes full use of the information in the CIGAR field.

  • Annotation file: The annotation file can be either gff_file or gtf_file and contains the features in the GFF format or GTF format


  • Input file type: Format of the input data. Possible values are sam (for text SAM files) and bam (for binary BAM files). Default is sam.

  • Sorting order of Alignment (For paired-end data): For paired-end data, the alignment has to be sorted either by read name or by alignment position. If your data is not sorted, use the samtools sort function of samtools to sort it. Use this option, with name or pos to indicate how the input data has been sorted. The default is name. If name is indicated, htseq-count expects all the alignments for the reads of a given read pair to appear in adjacent records in the input data. For pos, this is not expected; rather, read alignments whose mate alignment have not yet been seen are kept in a buffer in memory until the mate is found. While, strictly speaking, the latter will also work with unsorted data, sorting ensures that most alignment mates appear close to each other in the data and hence the buffer is much less likely to overflow. If you have paired-end data, pay attention to the -r option described below.

  • Strand-specificity: Whether the data is from a strand-specific assay (default: yes). For stranded=no, a read is considered overlapping with a feature regardless of whether it is mapped to the same or the opposite strand as the feature. For stranded=yes and single-end reads, the read has to be mapped to the same strand as the feature. For paired-end reads, the first read has to be on the same strand and the second read on the opposite strand. For stranded=reverse, these rules are reversed. Important: The default for strandedness is yes. If your RNA-Seq data has not been made with a strand-specific protocol, this causes half of the reads to be lost. Hence, make sure to set the option --stranded=no unless you have strand-specific data!

  • Minimum quality: Skip all reads with alignment quality lower than the given minimum value (default: 10 — Note: the default used to be 0 until version 0.5.4.)

  • Feature type: Feature type (3rd column in GFF file) to be used, all features of other type are ignored (default, suitable for RNA-Seq analysis using an Ensembl GTF file: exon)

  • Feature attribute or feature id: GFF attribute to be used as feature ID. Several GFF lines with the same feature ID will be considered as parts of the same feature. The feature ID is used to identity the counts in the output table. The default, suitable for RNA-Seq analysis using an Ensembl GTF file, is gene_id.

  • Mode: Mode to handle reads overlapping more than one feature. Possible values for  the option are union, intersection-strict and intersection-nonempty (default: union)

Output Folder 

The script outputs a table with counts for each feature, followed by the special counters, which count reads that were not counted for any feature for various reasons. The names of the special counters all start with a double underscore, to facilitate filtering. (Note: The double unscore was absent up to version 0.5.4). 

Test Run

All files are located in the Community Data directory of the CyVerse Discovery Environment at the following path:

Community Data > iplantcollaborative > example_data > htseq_count > 0.6.1 (/iplant/home/shared/iplantcollaborative/example_data/htseq_count/0.6.1)

Mandatory arguments: 

Use  testfile.sam and hy5_rep1_transcripts.gtf for inputs


Leave all the values as they are.


In the case of test run, the output folder contains two files - FeatureCounts.txt and htseq_analysis.sam that contains the count files

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