This is is the coverage pipeline from the Sequana project
| Overview: | Parallelised version of sequana_coverage for large eukaryotes genome. |
|---|---|
| Input: | A set of BAM or BED files. BED file must have 3 or 4 columns. First column is the chromosome/contig name, second column stored positions and third the coverage. Fourth optional columns contains a filtered coverage (not used in the analysis but shown in the HTML reports) |
| Output: | a set of HTML reports for each chromosomes and a multiqc report |
| Status: | production |
| Citation: | Dimitri Desvillechabrol, Christiane Bouchier, Sean Kennedy, Thomas Cokelaer Sequana coverage: detection and characterization of genomic variations using running median and mixture models GigaScience, Volume 7, Issue 12, December 2018, giy110, https://doi.org/10.1093/gigascience/giy110 and Cokelaer et al, (2017), ‘Sequana’: a Set of Snakemake NGS pipelines, Journal of Open Source Software, 2(16), 352, JOSS DOI https://doi:10.21105/joss.00352 |
sequana_multicov is based on Python3, just install the package as follows:
pip install sequana_multicov --upgrade
sequana_multicov --help sequana_multicov --input-directory DATAPATH
By default, this looks for BED file. WARNING. This are BED3 meaning a 3-columns tabulated file like this one:
chr1 1 10 chr1 2 11 ... chr1 N1 10 chr2 1 20 chr2 2 21 ... chr2 N2 20
where the first column stored the chromosome name, the second is the position and the third is the coverage itself. See sequana_coverage documentation for details. If you have BAM files as input, we will do the conversion for you. In such case, use this option:
--input-pattern "*.bam"
The sequana_coverage script creates a directory with the pipeline and its configuration file. You will then need to execute the pipeline:
cd coverage sh coverage.sh # for a local run
This launch a snakemake pipeline. If you are familiar with snakemake, you can retrieve the pipeline itself and its configuration files and then execute the pipeline yourself with specific parameters:
snakemake -s multicov.rules -c config.yaml --cores 4 --stats stats.txt
Or use sequanix interface as follows:
sequanix -w analysis -i . -p coverage
Go to the second panel, in Input data and then in Input directory. There, you must modify the pattern (empty field by default meaning search for fastq files) and set the field to either:
*.bed
or:
*.bam
You are ready to go. Save the project and press Run. Once done, open the HTML report.
This pipelines requires the following executable(s):
- sequana_coverage from Sequana, which should be installed automatically.
- multiqc
This pipeline runs coverage in parallel on the input BAM files (or BED file).
The coverage tool takes as input a BAM or a BED file. The BED file must have 3 or 4 columns as explained in the standalone application (sequana_coverage) documentation. In short, the first column is the chromosome name, the second column is the position (sorted) and the third column is the coverage (an optional fourth column would contain a coverage signal, which could be high quality coverage for instance).
If you have only BAM files, you can convert them using bioconvert tool or the command:
samtools depth -aa input.bam > output.bed
If you have a CRAM file:
samtools view -@ 4 -T reference.fa -b -o out.bam in.cram
For very large BAM/BED files, we recommend to split the BED file by chromosomes. For instance for the chromosome chr1, type:
# samtools index in.bam samtools depth -aa input.bam -r chr1 in.bam > chr1.bed
The standalone or Snakemake application can also take as input your BAM file and will convert it automatically into a BED file.
Here is the latest documented configuration file to be used with the pipeline. Each rule used in the pipeline may have a section in the configuration file.
| Version | Description |
|---|---|
| 1.1.0 |
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| 1.0.0 |
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| 0.9.1 |
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| 0.9.0 |
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