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Related Experiment Video

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Targeted DNA Methylation Analysis by Next-generation Sequencing
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Development of a method for identifying and functionally analyzing allele-specific DNA methylation based on BS-seq

Jiang Zhu1, Mu Su2, Yue Gu3

  • 1School of Life Science & Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin 150001, PR China.

Epigenomics
|November 9, 2019
PubMed
Summary

A new method, GeneASM, identifies allele-specific DNA methylation (ASM) genome-wide using bisulfite sequencing. This approach discovered 2194 ASM genes in human lymphocytes, offering insights into genomic imprinting and disease associations.

Keywords:
allele-specific DNA methylationgenomic imprintinghigh-throughput sequencingsingle-nucleotide polymorphism

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Area of Science:

  • Genomics
  • Epigenetics

Background:

  • Allele-specific DNA methylation (ASM) plays a crucial role in gene regulation.
  • Accurate identification of ASM is essential for understanding various biological processes and diseases.
  • Existing methods often require haplotype information, limiting their applicability.

Purpose of the Study:

  • To develop and validate a novel computational method for genome-wide ASM identification.
  • To identify ASM in human lymphocytes without prior haplotype information.
  • To explore the functional enrichment and disease relevance of ASM.

Main Methods:

  • Development of GeneASM, a method utilizing high-throughput bisulfite sequencing data.
  • Application of GeneASM to GM12878 lymphocyte data.
  • Validation using methylated DNA immunoprecipitation sequencing and methylation-sensitive restriction enzyme sequencing data.
  • Analysis of ASM in The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) data.

Main Results:

  • Identification of 2194 allele-specific DNA methylated genes in the GM12878 lymphocyte lineage using GeneASM.
  • Enrichment analysis revealed that these genes are primarily involved in cell cytoplasm function, subcellular component movement, and cellular linkages.
  • Demonstrated the utility of GeneASM by analyzing ASM-disease relationships in LUAD.

Conclusions:

  • GeneASM provides an effective approach for identifying ASM from high-throughput bisulfite sequencing data, even without haplotype information.
  • The identified ASM genes offer new insights into cellular functions and genomic imprinting.
  • This method opens new avenues for studying the role of ASM in diseases like lung adenocarcinoma.