targqc 1.5.2

Genome capture target coverage evaluation tool

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

# TargQC - target capture coverage QC 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 ### From bioconda ` conda install -c bioconda targqc `

### From PyPI ` pip install targqc `

### From source ` 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)

# BED file annotation

The bed_annotation package provides tools for annotation of BED file regions with gene symbols, based on Ensembl data.

### Usage ` annotate_bed.py INPUT.bed -g hg19 -o OUTPUT.bed `

Script checks each region against the Ensembl genomic features database, and writes a BED file in a standardized format with a gene symbol, strand and exon rank in 4-6th columns:

INPUT.bed: ` chr1 69090 70008 chr1 367658 368597 `

OUTPUT.bed: ` chr1 69090 70008 OR4F5 1 + chr1 367658 368597 OR4F29 1 + `

#### Transcripts order

The piority for choosing transcripts for annotation is the following: - Overlap % with transcript - Overlap % with CDS - Overlap % with exons - Biotype (protein_coding > others > *RNA > *_decay > sense_* > antisense > translated_* > transcribed_*) - TSL (1 > NA > others > 2 > 3 > 4 > 5) - Presence of a HUGO gene symbol - Is cancer canonical - Transcript size

#### Extended annotation

Use –extended option to report extra columns with details on features, biotype, overlapping transcripts and overlap sizes: ` annotate_bed.py INPUT.bed -g hg19 -o OUTPUT.bed --extended `

OUTPUT.bed: ` ## Tx_overlap_%: part of region overlapping with transcripts ## Exon_overlaps_%: part of region overlapping with exons ## CDS_overlaps_%: part of region overlapping with protein coding regions #Chrom Start End Gene Exon Strand Feature Biotype Ensembl_ID TSL HUGO Tx_overlap_% Exon_overlaps_% CDS_overlaps_% chr1 69090 70008 OR4F5 1 + capture protein_coding ENST00000335137 NA OR4F5 100.0 100.0 99.7 chr1 367658 368597 OR4F29 1 + capture protein_coding ENST00000426406 NA OR4F29 100.0 100.0 99.7 `

#### Ambuguous annotations

Regions may overlap mltiple genes. The –ambiguities controls how the script resolves such ambiguities - –ambiguities all – report all reliable overlaps (in order in the “priority” section, see above) - –ambiguities all_ask – stop execution and ask user which annotation to pick - –ambiguities best_all (default) – find the best overlap, and if there are several equally good, report all (in terms of the “priority” above) - –ambiguities best_ask – find the best overlap, and if there are several equally good, ask user - –ambiguities best_one – find the best overlap, and if there are several equally good, report any of them

Note that the first 4 options might output multiple lines per region, e.g.: ` annotate_bed.py INPUT.bed -g hg19 -o OUTPUT.bed --extended --ambiguities best_all ` OUTPUT.bed: ` ## Tx_overlap_%: part of region overlapping with transcripts ## Exon_overlaps_%: part of region overlapping with exons ## CDS_overlaps_%: part of region overlapping with protein coding regions #Chrom Start End Gene Exon Strand Feature Biotype Ensembl_ID TSL HUGO Tx_overlap_% Exon_overlaps_% CDS_overlaps_% chr1 69090 70008 OR4F5 1 + capture protein_coding ENST00000335137 NA OR4F5 100.0 100.0 100.0 chr1 367658 368597 OR4F29 1 + capture protein_coding ENST00000426406 NA OR4F29 100.0 100.0 100.0 chr1 367658 368597 OR4F29 1 + capture protein_coding ENST00000412321 NA OR4F29 100.0 100.0 100.0 `

# Venn diagrams for BED files Build a web-page with size-proportional Venn diagrams for an unlimited set of BED files: ` bed_venn.py *.bed -o res_dir `