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Related Experiment Video

Updated: Feb 5, 2026

Single-Molecule F&#246;rster Resonance Energy Transfer Methods for Real-Time Investigation of the Holliday Junction Resolution by GEN1
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A Statistical Method for Observing Personal Diploid Methylomes and Transcriptomes with Single-Molecule Real-Time

Yuta Suzuki1, Yunhao Wang2, Kin Fai Au3,4

  • 1Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo 277-8561, Japan. yuta_suzuki@edu.k.u-tokyo.ac.jp.

Genes
|September 22, 2018
PubMed
Summary

This study introduces a statistical model to accurately phase CpG methylation on homologous chromosomes using Single Molecule Real-Time sequencing data. The method enables precise analysis of diploid methylomes and allele-specific methylation patterns.

Keywords:
DNA methylationallele-specific analysisgene expressionsingle molecule real-time sequencingstatistical methods

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Area of Science:

  • Epigenetics and Genomics
  • Computational Biology

Background:

  • Observing diploid methylomes (CpG methylation on homologous chromosomes) is difficult due to limited phased heterozygous variants (PHVs).
  • Single Molecule Real-Time (SMRT) sequencing offers long reads with methylation data but suffers from high error rates (∼15%) in PHV identification.

Purpose of the Study:

  • To develop a statistical model for accurate phasing of CpG sites in personal diploid methylomes.
  • To enable precise detection of allele-specific CpG hypomethylation using SMRT sequencing data.

Main Methods:

  • Proposed a novel statistical model to reduce the error rate of phasing CpG sites from SMRT sequencing reads.
  • Applied the model to analyze CpG methylation patterns in the *GNAS* complex locus.

Main Results:

  • Reduced the error rate of phasing CpG sites to approximately 1%.
  • Achieved >90% precision and sensitivity in calling CpG hypomethylation for each haplotype.
  • Observed allele-specific methylation patterns in the *GNAS* locus that closely mirrored allele-specific expression.

Conclusions:

  • The developed statistical model effectively overcomes SMRT sequencing errors for diploid methylome analysis.
  • Demonstrates the capability to elucidate comprehensive personal diploid methylomes and transcriptomes, revealing allele-specific epigenetic regulation.