DSBS
DSBS decodes genome-wide DNA methylation and single-nucleotide variants from bisulfite sequencing data to enable simultaneous analysis of the DNA methylome and genomic variation.
Key Features:
- Simultaneous identification: Identifies genome-wide SNVs and DNA methylation at single-base resolution from a single DSBS dataset using a hairpin adapter to link Watson and Crick strands followed by bisulfite conversion and paired-end sequencing.
- Strand-bias elimination: Sequencing of both bisulfite-converted Watson and Crick strands within a single paired-end read removes strand bias in methylation and variant calls.
- Error correction: Mutual correction between read1 and read2 enables estimation and reduction of amplification and sequencing errors.
- Comprehensive analysis pipeline: DSBS Analyzer processes DSBS data to accurately call SNVs and quantify DNA methylation levels genome-wide.
Scientific Applications:
- Genome-wide hemimethylation landscape: Enables comprehensive profiling of hemimethylation distribution across genomic regions in human cells, including promoters and CpG islands.
- Disease and biological process studies: Facilitates combined analysis of genetic and epigenetic variation for research into diseases and biological processes influenced by both SNVs and DNA methylation.
Methodology:
DSBS Analyzer computationally processes DSBS paired-end reads to call SNVs and quantify methylation; mutual correction between read1 and read2 is used to estimate and reduce amplification and sequencing errors; strand-bias elimination is achieved by leveraging paired-end sequencing of both bisulfite-converted Watson and Crick strands.
Topics
Details
- Tool Type:
- workflow
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- Python
- Added:
- 11/3/2021
- Last Updated:
- 11/24/2024
Operations
Publications
Liang J, Zhang K, Yang J, Li X, Li Q, Wang Y, Cai W, Teng H, Sun Z. A new approach to decode DNA methylome and genomic variants simultaneously from double strand bisulfite sequencing. Briefings in Bioinformatics. 2021;22(6). doi:10.1093/bib/bbab201. PMID:34058751. PMCID:PMC8575003.