Bayesian genotyper for structural variants

SVTyper performs breakpoint genotyping of structural variants (SVs) using whole genome sequencing data. Users must supply a VCF file of sites to genotype (which may be generated by LUMPY) as well as a BAM/CRAM file of Illumina paired-end reads aligned with BWA-MEM. SVTyper assesses discordant and concordant reads from paired-end and split-read alignments to infer genotypes at each site. Algorithm details and benchmarking are described in Chiang et al., 2015.

Requirements:

• Python 2.7.x

pip install git+https://github.com/hall-lab/svtyper.git 

svtyper depends on pysam (version 0.15.0 or newer), numpy, and scipy; svtyper-sso additionally depends on cytoolz. If the dependencies aren't already available on your system, pip will attempt to download and install them.

svtyper is the original implementation of the genotyping algorithm, and works with multiple samples. svtyper-sso is an alternative implementation of svtyper that is optimized for genotyping a single sample. svtyper-sso is a parallelized implementation of svtyper that takes advantage of multiple CPU cores via the multiprocessing module. svtyper-sso can offer a 2x or more speedup (depending on how many CPU cores used) in genotyping a single sample. NOTE: svtyper-sso is not yet stable. There are minor logging differences between the two and svtyper-sso may exit with an error prematurely when processing CRAM files.

svtyper \ -i sv.vcf \ -B sample.bam \ -l sample.bam.json \ > sv.gt.vcf

import svtyper.classic as svt input_vcf = "/path/to/input.vcf" input_bam = "/path/to/input.bam" library_info = "/path/to/library_info.json" output_vcf = "/path/to/output.vcf" with open(input_vcf, "r") as inf, open(output_vcf, "w") as outf: svt.sv_genotype(bam_string=input_bam, vcf_in=inf, vcf_out=outf, min_aligned=20, split_weight=1, disc_weight=1, num_samp=1000000, lib_info_path=library_info, debug=False, alignment_outpath=None, ref_fasta=None, sum_quals=False, max_reads=None) # Results will be inside the /path/to/output.vcf file

svtyper-sso \ --core 2 # number of cpu cores to use \ --batch_size 1000 # number of SVs to process in a single batch (default: 1000) \ --max_reads 1000 # skip genotyping if SV contains valid reads greater than this threshold (default: 1000) \ -i sv.vcf \ -B sample.bam \ -l sample.bam.json \ > sv.gt.vcf

import svtyper.singlesample as sso input_vcf = "/path/to/input.vcf" input_bam = "/path/to/input.bam" library_info = "/path/to/library_info.json" output_vcf = "/path/to/output.vcf" with open(input_vcf, "r") as inf, open(output_vcf, "w") as outf: sso.sso_genotype(bam_string=input_bam, vcf_in=inf, vcf_out=outf, min_aligned=20, split_weight=1, disc_weight=1, num_samp=1000000, lib_info_path=library_info, debug=False, alignment_outpath=None, ref_fasta=None, sum_quals=False, max_reads=1000, cores=2, batch_size=1000) # Results will be inside the /path/to/output.vcf file

Requirements:

• Python 2.7 or newer
• GNU Make
• virtualenv (or conda for anaconda or miniconda users)

git clone https://github.com/hall-lab/svtyper.git cd svtyper virtualenv myvenv source myvenv/bin/activate pip install -e . <add, edit, or delete code> make test # when you're finished with development git push <remote-name> <branch> deactivate cd .. && rm -rf svtyper 

git clone https://github.com/hall-lab/svtyper.git cd svtyper conda create --channel bioconda --name mycenv pysam numpy scipy cytoolz # type 'y' when prompted with "proceed ([y]/n)?" source activate mycenv pip install -e . <add, edit, or delete code> make test # when you're finished with development git push <remote-name> <branch> source deactivate cd .. && rm -rf svtyper conda remove --name mycenv --all 

Many common issues are related to abnormal insert size distributions in the BAM file. SVTyper provides methods to assess and visualize the characteristics of sequencing libraries.

Running SVTyper with the -l flag creates a JSON file with essential metrics on a BAM file. SVTyper will sample the first N reads for the file (1 million by default) to parse the libraries, read groups, and insert size histograms. This can be done in the absence of a VCF file.

svtyper \ -B my.bam \ -l my.bam.json 

The lib_stats.R script produces insert size histograms from the JSON file

scripts/lib_stats.R my.bam.json my.bam.json.pdf 

C Chiang, R M Layer, G G Faust, M R Lindberg, D B Rose, E P Garrison, G T Marth, A R Quinlan, and I M Hall. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat Meth 12, 966–968 (2015). doi:10.1038/nmeth.3505.

http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3505.html