========================================================================
License: GPLv3
I have succesfully built on Unix with GCC-4.8, 4.9 and 5.1
git clone --recursive https://github.com/walaj/svlib.git
cd svlib
./configure
make
Convert a BND formatted SV VCF into a BEDPE. Output format will be:
chr1 pos1 pos1 chr2 pos2 pos2 id 0 str1 str2 info genotypes
Examples:
## convert a snowman vcf and sort with bedtools
svlib vcftobedpe a.snowman.sv.vcf | sortBed -i stdin
Simulate rearrangements and indels
svlib sim -G $REFHG19 -s 42 -R 1000 -X 10000 -A mysim
Test the ability of an aligner to realign an SV contig
svlib realigntest -G $REFHG19 -b some.bam > sim.fasta
## align sim.fasta with some aligner (eg BWA) to something like sim.bam
svlib realigntest -G $REFHG19 -E sim.bam > results
Call SVs and indels from a qname sorted BAM of long sequence alignments
CORES=10
TUM=tumor.shortreads.bam ## eg standard Illumina reads for scoring/genotyping
NORM=normal.shortreads.bam ## should be coordinate sorted
svlib seqtovcf qsorted.contigs.bam -p $CORES -t $TUM -n $NORM -a output_id -G $REFHG19
seqtovcf
can optionally use exclusion lists to avoid processing variants that
fall in noise regions (e.g. centromeres), or to avoid re-running the same variants
supported by different long-sequences.
## first pass to find contigs which support a unique variant (without reads for speed)
## -W flag will write a *.extracted.contigs.bam, so you can see which ones were chosen
## in the case that multiple contigs support the same variant, svlib only uses the highest quality contig
svlib seqtovcf qsorted.contigs.bam -a first_pass -G $REFHG19 -W
gunzip -c first_pass.bps.txt.gz | cut -f20 | sort | uniq > contigs.to.process
## second pass (with reads for genotyping)
svlib seqtovcf qsorted.contigs.bam -L contigs.to.process -B excluded_regions.bed -a second_pass -t $TUM -n $NORM
svlib is developed and maintained by Jeremiah Wala ([email protected]) -- Rameen Berkoukhim's lab -- Dana Farber Cancer Institute, Boston, MA
This project was developed in collaboration with the Cancer Genome Analysis team at the Broad Institute. Particular thanks to:
- Cheng-Zhong Zhang (Matthew Meyerson Lab)
- Marcin Imielinski