targqc 0.0.0

[](https://travis-ci.org/vladsaveliev/TargQC)

# Alignment target coverage analysis tool

## Input - BAM file(s) (or FastQ files). - BED file (optional).

## Output - summary.html – sample-level coverage statistics and plots. - summary.tsv – sample-level coverage, parsable version - regions.txt – region-level coverage statistics.

## Installation ` git clone --recursive https://github.com/vladsaveliev/TargQC cd TargQC virtualenv venv_targqc && source venv_targqc/bin/activate # optional, but recommended if you are not an admin pip install --upgrade pip setuptools pip install -r requirements.txt python setup.py install `

## Usage ` targqc *.bam --bed target.bed -g hg19 -o targqc_results ` The results will be written to targqc_results folder.

The BED file may be omitted. In this case statistics reported will be based of off the whole genome.

The accepted values for -g are hg19, hg38, or a full path to any indexed reference fasta file: ` targqc *.bam --bed target.bed -g /path/to/genomes/some_genome.fa -o targqc_results ` When running from BAMs, only the .fai index is used, and the fasta file itself can be non-existent.

Instead of the BAM files, input FastQ are also allowed. The reads will be aligned by BWA to the reference genome specified by –bwa-prefix (unless -g is already a fasta path bwa-indexed). ` targqc *.fastq --bed target.bed -g hg19 -o targqc_results --bwa-prefix /path/to/ref.bwa ` Option –downsample-to <N> (default value 5e5) specifies the number of read pairs will be randomly selected from each input set. This feature allows to quickly estimate approximate coverage quality before full alignment. To turn downsampling off and align all reads, set –downsample-to off.

## Parallel running ### Threads Run using 3 threads: ` targqc *.bam --bed target.bed -g hg19 -o targqc_results -t 3 ` ### Cluster Run using 3 jobs, using SGE scheduler, and queue “queue”: ` targqc *.bam --bed target.bed -g hg19 -o targqc_results -t 3 -s sge -q batch.q -r pename=smp ` If the number of samples is higher than the requested number of jobs, the processes within job will be additionally parallelized using threads, so the full number of occupied cores will equal the number of requested threads (-t)

Other supported schedulers: Platform LSF (“lsf”), Sun Grid Engine (“sge”), Torque (“torque”), SLURM (“slurm”) (see details at https://github.com/roryk/ipython-cluster-helper)