mapping/htseq_count

Description

Quantify gene expression for subsequent testing for differential expression.

This script takes one or more alignment files in SAM/BAM format and a feature file in GFF format and calculates for each feature the number of reads mapping to it.

See http://htseq.readthedocs.io/en/master/count.html for details.

Type

python_script

License

MIT

Links

Repository

Contributors

Launch

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
output: "$id.$key.output.tsv"  
output_sam: "$id.$key.output_sam._*.BAM"  
order: [ "name" ]  
stranded: [ "yes" ]  
minimum_alignment_quality: [ 10 ]  
mode: [ "union" ]  
non_unique: [ "none" ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v3.0.1 \  
  -main-script target/nextflow/mapping/htseq_count/main.nf \  
  -params-file params.yaml

Input

Name	Type & Properties	Description
--input	file required multiple	Path to the SAM/BAM files containing the mapped reads.
--reference	file required	Path to the GTF file containing the features.

Name

Type & Properties

--input

file

required

multiple

--reference

file

required

Output

Name	Type & Properties	Description
--output	file required output	Filename to output the counts to.
--output_delimiter	string	Column delimiter in output.
--output_sam	file multiple output	Write out all SAM alignment records into SAM/BAM files (one per input file needed), annotating each line with its feature assignment (as an optional field with tag 'XF'). See the -p option to use BAM instead of SAM.
--output_sam_format	string	Format to use with the --output_sam argument.

Name

Type & Properties

--output

file

required

output

--output_delimiter

string

--output_sam

file

multiple

output

--output_sam_format

string

Arguments

Name	Type & Properties	Description
--order -r	string	Sorting order of <alignment_file>. Paired-end sequencing data must be sorted either by position or by read name, and the sorting order must be specified. Ignored for single-end data.
--stranded -s	string	Whether the data is from a strand-specific assay. 'reverse' means 'yes' with reversed strand interpretation.
--minimum_alignment_quality -a --minaqual	integer	Skip all reads with MAPQ alignment quality lower than the given minimum value. MAPQ is the 5th column of a SAM/BAM file and its usage depends on the software used to map the reads.
--type -t	string	Feature type (3rd column in GTF file) to be used, all features of other type are ignored (default, suitable for Ensembl GTF files: exon)
--id_attribute -i	string multiple	GTF attribute to be used as feature ID (default, suitable for Ensembl GTF files: gene_id). All feature of the right type (see -t option) within the same GTF attribute will be added together. The typical way of using this option is to count all exonic reads from each gene and add the exons but other uses are possible as well. You can call this option multiple times: in that case, the combination of all attributes separated by colons (:) will be used as a unique identifier, e.g. for exons you might use -i gene_id -i exon_number.
--additional_attributes	string multiple	Additional feature attributes (suitable for Ensembl GTF files: gene_name). Use multiple times for more than one additional attribute. These attributes are only used as annotations in the output, while the determination of how the counts are added together is done based on option -i.
--add_chromosome_info	boolean_true	Store information about the chromosome of each feature as an additional attribute (e.g. colunm in the TSV output file).
--mode -m	string	Mode to handle reads overlapping more than one feature.
--non_unique	string	Whether and how to score reads that are not uniquely aligned or ambiguously assigned to features.
--secondary_alignments	string	Whether to score secondary alignments (0x100 flag).
--supplementary_alignments	string	Whether to score supplementary alignments (0x800 flag).
--counts_output_sparse	boolean_true	Store the counts as a sparse matrix (mtx, h5ad, loom).

Name

Type & Properties

--order

-r

string

--stranded

-s

string

--minimum_alignment_quality

-a

--minaqual

integer

--type

-t

string

--id_attribute

-i

string

multiple

--additional_attributes

string

multiple

--add_chromosome_info

boolean_true

--mode

-m

string

--non_unique

string

--secondary_alignments

string

--supplementary_alignments

string

--counts_output_sparse

boolean_true