MethylExtract

MethylExtract performs high-resolution whole-genome DNA methylation profiling and simultaneous detection of sequence variation from bisulfite-treated sequencing reads.


Key Features:

  • High-Quality Methylation Mapping: Generates comprehensive whole genome methylation maps at single-cytosine resolution by aligning bisulfite-treated sequence reads to a reference genome and profiling methylation levels.
  • Simultaneous Detection of Sequence Variation: Identifies single nucleotide variants and genotypes from the same bisulfite-treated data using a VarScan-like approach for sensitive SNV and genotype calling.
  • Error Source Management: Accounts for sequencing errors, incomplete bisulfite conversion (bisulfite failure), clonal reads, and SNVs and includes a statistical assessment of bisulfite conversion efficiency.

Scientific Applications:

  • Genetic–epigenetic interaction studies: Enables analysis of the interplay between genetic variants and differential DNA methylation by providing concurrent methylation and SNV calls.
  • Cancer epigenetics: Supports profiling of tumor methylation patterns alongside somatic variant detection to study epigenetic alterations in cancer.
  • Developmental biology: Facilitates investigation of DNA methylation dynamics during development.
  • Complex trait analysis: Allows integrative analyses of methylation and genetic variation in studies of complex traits.

Methodology:

Aligns bisulfite-treated reads to a reference genome, profiles methylation levels at single cytosines, detects SNVs using VarScan-like algorithms, performs statistical assessment of bisulfite conversion efficiency, and has been benchmarked against artificial bisulfite-treated datasets and compared with Bis-SNP.

Topics

Details

Tool Type:
command-line tool
Operating Systems:
Linux, Mac
Added:
8/3/2017
Last Updated:
11/24/2024

Operations

Publications

Barturen G, Rueda A, Oliver JL, Hackenberg M. MethylExtract: High-Quality methylation maps and SNV calling from whole genome bisulfite sequencing data. F1000Research. 2014;2:217. doi:10.12688/f1000research.2-217.v2. PMID:24627790. PMCID:PMC3938178.

Documentation

Links