Introduction

BisSNP is a package based on the Genome Analysis Toolkit (GATK) map-reduce framework for genotyping and accurate DNA methylation calling in bisulfite treated massively parallel sequencing (Bisulfite-seq, NOMe-seq, RRBS and any other bisulfite treated sequencing) with Illumina directional library protocol. It contains the following key features:

• Call and summarize methylation of any cytosine context provided (CpG, CHH, CHG, GCH et.al.);
• Work for single end and paired-end data;
• Accurtae variant detection. Enable base quality recalibration and indel calling in bisulfite sequencing;
• Based on Java map-reduce framework, allow multi-thread computing. Cross-platform;
• Allow multiple output format, detailed VCF files, CpG haplotype reads file for mono-allelic methylation analysis, simplified bedGraph, wig and bed format for visualization in UCSC genome broswer and IGV browser.

BisSNP uses bayesian inference with locus specific methylation probabilities and bisulfite conversion rate of different cytosine context(not only CpG, CHH, CHG in Bisulfite-seq, but also GCH et.al. in other bisulfite treated sequencing) to determine genotypes and methylation levels simultaneously. Specificity and sensitivity has been validate by Illumina IM SNP array. In default threshold (Phred scale score > 20), it could detect 92.21% heterozygous SNPs with 0.14% false positive rate (90.88% sensitivity in C/T SNPs with 0.16% false positive rate, 98.51% sensitivity in non C/T SNPs with 0.16% false positive rate). Cytosine calling is not only based on reference context, so it could detect non-reference cytosine context for usage in epigenome wide association study.

BisSNP is hosted by SourceForge.net. The project page is here. The executable jar file are available from the download page. You can check out the latest source codes with:

svn checkout svn://svn.code.sf.net/p/bissnp/code/trunk bissnp-code

Publication

• Liu Y, Siegmund KD, Laird PW, Berman BP. Bis-SNP: combined DNA methylation and SNP calling for Bisulfite-seq data. Genome Biology 2012 Jul 11;13(7):R61.