Bis-SNP

Bis-SNP calls single nucleotide polymorphisms and estimates cytosine methylation levels from bisulfite-treated high-throughput sequencing data to enable SNP-aware methylation analysis.

Key Features:

• Model-based SNP calling: A model-based SNP caller tailored for bisulfite-treated sequences that identifies variants within converted DNA.
• SNP identification accuracy: At an average genomic coverage of 30×, Bis-SNP correctly identified 96% of SNPs under default high-stringency settings.
• Methylation analysis integrated with SNP calls: Joint determination of genotypes and methylation levels to improve methylation quantification accuracy.
• Bayesian inference framework: Uses Bayesian inference to estimate methylation probabilities across cytosine contexts including CpG, CHH, CHG, and GCH.
• GATK map-reduce integration: Leverages the Genome Analysis Toolkit (GATK) map-reduce framework for scalable processing of large datasets.
• Compatibility with Illumina high-throughput data: Tailored for high-throughput sequencing applications such as Illumina platforms.

Scientific Applications:

• Epigenetic profiling: Quantification of DNA methylation across CpG, CHH, CHG, and GCH contexts in bisulfite sequencing data.
• Allele-specific methylation and imprinting studies: Enables detection of allele-specific epigenetic events and imprinting by combining SNP and methylation calls.
• Variant discovery in bisulfite data: SNP discovery within bisulfite-treated sequencing datasets for downstream genetic and epigenetic analyses.
• High-throughput sequencing analyses: Applicable to large-scale bisulfite sequencing projects processed with GATK map-reduce workflows.

Methodology:

Bis-SNP applies a model-based SNP caller and Bayesian inference within the Genome Analysis Toolkit (GATK) map-reduce framework to jointly estimate genotypes and cytosine methylation probabilities across CpG, CHH, CHG and GCH contexts.