These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: CpG_MPs: identification of CpG methylation patterns of genomic regions from high-throughput bisulfite sequencing data.
    Author: Su J, Yan H, Wei Y, Liu H, Liu H, Wang F, Lv J, Wu Q, Zhang Y.
    Journal: Nucleic Acids Res; 2013 Jan 07; 41(1):e4. PubMed ID: 22941633.
    Abstract:
    High-throughput bisulfite sequencing is widely used to measure cytosine methylation at single-base resolution in eukaryotes. It permits systems-level analysis of genomic methylation patterns associated with gene expression and chromatin structure. However, methods for large-scale identification of methylation patterns from bisulfite sequencing are lacking. We developed a comprehensive tool, CpG_MPs, for identification and analysis of the methylation patterns of genomic regions from bisulfite sequencing data. CpG_MPs first normalizes bisulfite sequencing reads into methylation level of CpGs. Then it identifies unmethylated and methylated regions using the methylation status of neighboring CpGs by hotspot extension algorithm without knowledge of pre-defined regions. Furthermore, the conservatively and differentially methylated regions across paired or multiple samples (cells or tissues) are identified by combining a combinatorial algorithm with Shannon entropy. CpG_MPs identified large amounts of genomic regions with different methylation patterns across five human bisulfite sequencing data during cellular differentiation. Different sequence features and significantly cell-specific methylation patterns were observed. These potentially functional regions form candidate regions for functional analysis of DNA methylation during cellular differentiation. CpG_MPs is the first user-friendly tool for identifying methylation patterns of genomic regions from bisulfite sequencing data, permitting further investigation of the biological functions of genome-scale methylation patterns.
    [Abstract] [Full Text] [Related] [New Search]