Provided by: python3-htseq_1.99.2-1build1_amd64 
NAME
htseq-count - Count the number of reads in a SAM alignment file that map to GFF features
Given a file with aligned sequencing reads and a list of genomic features, a common task
is to count how many reads map to each feature.
A feature is here an interval (i.e., a range of positions) on a chromosome or a union of
such intervals.
In the case of RNA-Seq, the features are typically genes, where each gene is considered
here as the union of all its exons. One may also consider each exon as a feature, e.g., in
order to check for alternative splicing. For comparative ChIP-Seq, the features might be
binding region from a pre-determined list.
Special care must be taken to decide how to deal with reads that overlap more than one
feature. The htseq-count script allows one to choose between three modes. Of course, if
none of these fits your needs, you can write your own script with HTSeq. See the chapter
tour for a step-by-step guide on how to do so. See also the FAQ at the end, if the
following explanation seems to technical.
The three overlap resolution modes of htseq-count work as follows. For each position i in
the read, a set S(i) is defined as the set of all features overlapping position i. Then,
consider the set S, which is (with i running through all position within the read or a
read pair)
• the union of all the sets S(i) for mode union. This mode is recommended for most use
cases.
• the intersection of all the sets S(i) for mode intersection-strict.
• the intersection of all non-empty sets S(i) for mode intersection-nonempty.
If S contains precisely one feature, the read (or read pair) is counted for this feature.
If it contains more than one feature, the read (or read pair) is counted as ambiguous (and
not counted for any features), and if S is empty, the read (or read pair) is counted as
no_feature.
The following figure illustrates the effect of these three modes: [image]
USAGE
After you have installed HTSeq (see install), you can run htseq-count from the command
line:
htseq-count [options] <alignment_file> <gff_file>
If the file htseq-qa is not in your path, you can, alternatively, call the script with
python -m HTSeq.scripts.count [options] <alignment_file> <gff_file>
The <alignment_file> contains the aligned reads in the SAM format. (Note that the SAMtools
contain Perl scripts to convert most alignment formats to SAM.) Make sure to use a
splicing-aware aligner such as TopHat. HTSeq-count makes full use of the information in
the CIGAR field.
To read from standard input, use - as <alignment_file>.
If you have paired-end data, pay attention to the -r option described below.
The <gff_file> contains the features in the GFF format.
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). The special counters are:
• __no_feature: reads (or read pairs) which could not be assigned to any feature (set S as
described above was empty).
• __ambiguous: reads (or read pairs) which could have been assigned to more than one
feature and hence were not counted for any of these (set S had more than one element).
• __too_low_aQual: reads (or read pairs) which were skipped due to the -a option, see
below
• __not_aligned: reads (or read pairs) in the SAM file without alignment
• __alignment_not_unique: reads (or read pairs) with more than one reported alignment.
These reads are recognized from the NH optional SAM field tag. (If the aligner does not
set this field, multiply aligned reads will be counted multiple times, unless they getv
filtered out by due to the -a option.)
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!
Options
-f <format>, --format=<format>
Format of the input data. Possible values are sam (for text SAM files) and bam (for
binary BAM files). Default is sam.
-r <order>, --order=<order>
For paired-end data, the alignment have 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 for <order> 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.
-s <yes/no/reverse>, --stranded=<yes/no/reverse>
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.
-a <minaqual>, --a=<minaqual>
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.)
-t <feature type>, --type=<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)
-i <id attribute>, --idattr=<id attribute>
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.
-m <mode>, --mode=<mode>
Mode to handle reads overlapping more than one feature. Possible values for <mode>
are union, intersection-strict and intersection-nonempty (default: union)
-o <samout>, --samout=<samout>
write out all SAM alignment records into an output SAM file called <samout>,
annotating each line with its assignment to a feature or a special counter (as an
optional field with tag ‘XF’)
-q, --quiet
suppress progress report and warnings
-h, --help
Show a usage summary and exit
Frequenctly asked questions
My shell reports “command not found” when I try to run “htseq-count”. How can I launch the
script?
The file “htseq-count” has to be in the system’s search path. By default, Python
places it in its script directory, which you have to add to your search path. A
maybe easier alternative is to write python -m HTSeq.scripts.count instead of
htseq-count, followed by the options and arguments, which will launch the
htseq-count script as well.
Why are multi-mapping reads and reads overlapping multiple features discarded rather than
counted for each feature?
The primary intended use case for htseq-count is differential expression analysis,
where one compares the expression of the same gene across samples and not the
expression of different genes within a sample. Now, consider two genes, which share
a stretch of common sequence such that for a read mapping to this stretch, the
aligner cannot decide which of the two genes the read originated from and hence
reports a multiple alignment. If we discard all such reads, we undercount the total
output of the genes, but the ratio of expression strength (the “fold change”)
between samples or experimental condition will still be correct, because we discard
the same fratcion of reads in all samples. On the other hand, if we counted these
reads for both genes, a subsequent diffential-expression analysis might find false
positives: Even if only one of the gene changes increases its expression in
reaction to treatment, the additional read caused by this would be counted for both
genes, giving the wrong appearance that both genes reacted to the treatment.
I have used a GTF file generated by the Table Browser function of the UCSC Genome Browser,
and most reads are counted as ambiguous. Why?
In these files, the gene_id attribute incorrectly contains the same value as the
transcript_id attribute and hence a different value for each transcript of the same
gene. Hence, if a read maps to an exon shared by several transcripts of the same
gene, this will appear to htseq-count as and overlap with several genes. Therefore,
these GTF files cannot be used as is. Either correct the incorrect gene_id
attributes with a suitable script, or use a GTF file from a different source.
Can I use htseq-count to count reads mapping to transcripts rather than genes?
In principle, you could instruct htseq-count to count for each of a gene’s
transcript individually, by specifying --idattr transcript_id. However, all reads
mapping to exons shared by several transcripts will then be considered ambiguous.
(See second question.) Counting them for each transcript that contains the exons
would be possible but makes little sense for typical use cases. (See first
question.) If you want to perform differential expression analysis on the level of
individual transcripts, maybe ahve a look at our paper on DEXSeq for a discussion
on why we prefer performing such analyses on the level of exons instead.
For paired-end data, does htseq-count count reads or read pairs?
Read pairs. The script is designed to count “units of evidence” for gene
expression. If both mates map to the same gene, this still only shows that one cDNA
fragment originated from that gene. Hence, it should be counted only once.
What happens if the two reads in a pair overlap two different features?
The same as if one read overlaps two features: The read or read pair is counted as
ambiguous.
What happened if the mate of an aligned read is not aligned?
For the default mode “union”, only the aligned read determines how the read pair is
counted. For the other modes, see their description.
Most of my RNA-Seq reads are counted as ``__no_feature``. What could have gone wrong?
Common causes include: - The --stranded option was set wrongly. Use a genome
browser (e.g., IGV) to check. - The GTF file uses coordinates from another
reference assembly as the SAM file. - The chromosome names differ between GTF and
SAM file (e.g., chr1 in one file and jsut 1 in the other).
Which overlap mode should I use?
When I wrote htseq-count, I was not sure which option is best and included three
possibilities. Now, several years later, I have seen very few cases where the
default union would not be appropriate and hence tend to recommend to just stick to
union.
I have a GTF file? How do I convert it to GFF?
No need to do that, because GTF is a tightening of the GFF format. Hence, all GTF
files are GFF files, too. By default, htseq-count expects a GTF file.
I have a GFF file, not a GTF file. How can I use it to count RNA-Seq reads?
The GTF format specifies, inter alia, that exons are marked by the word exon in the
third column and that the gene ID is given in an attribute named gene_id, and
htseq-count expects these words to be used by default. If you GFF file uses a word
other than exon in its third column to mark lines describing exons, notify
htseq-count using the --type option. If the name of the attribute containing the
gene ID for exon lines is not gene_id, use the --idattr. Often, its is, for
example, Parent, GeneID or ID. Make sure it is the gene ID and not the exon ID.
How can I count overlaps with features other than genes/exons?
If you have GFF file listing your features, use it together with the --type and
--idattr options. If your feature intervals need to be computed, you are probably
better off writing your own counting script (provided you have some knowledge of
Python). Follow the tutorial in the other pages of this documentation to see how to
use HTSeq for this.
How should I cite htseq-count in a publication?
Please cite HTSeq as follows: S Anders, T P Pyl, W Huber: HTSeq — A Python
framework to work with high-throughput sequencing data. bioRxiv 2014. doi:
10.1101/002824. (This is a preprint currently under review. We will replace this
with the reference to the final published version once available.)
AUTHOR
Simon Anders
COPYRIGHT
2017, Simon Anders