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Related Experiment Video

Updated: Jun 4, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Identifying hypermethylated CpG islands using a quantile regression model.

Shuying Sun1, Zhengyi Chen, Pearlly S Yan

  • 1Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA. shuying.sun@case.edu

BMC Bioinformatics
|February 18, 2011
PubMed
Summary
This summary is machine-generated.

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This study optimizes DNA methylation analysis by comparing quantile regression models. Results show that quantile levels between 80% and 90% are most effective for identifying hypermethylated CpG islands in cancer.

Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • DNA methylation is crucial for tumor suppressor gene silencing in various cancers.
  • Differential methylation hybridization (DMH) assays methylation status of all known CpG islands (CGIs) via microarrays.
  • Microarray data can be affected by confounding factors, necessitating noise correction.

Purpose of the Study:

  • To determine the optimal quantile level for identifying hypermethylated CGIs using a quantile regression model.
  • To refine the analysis of DNA methylation patterns in cancer using microarray data.

Main Methods:

  • Implementation of a quantile regression model to analyze DNA methylation microarray data.
  • Comparison of model performance across various quantile levels (60%-95%).

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Last Updated: Jun 4, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
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  • Validation using known methylated and unmethylated genes from breast and ovarian cancer datasets.

    • Main Results:

    • The quantile regression model was evaluated at multiple quantile levels.
    • Quantile levels between 80% and 90% demonstrated superior performance in identifying methylated and unmethylated genes.
    • The model effectively identified hypermethylated CGIs in both breast and ovarian cancer data.

    Conclusions:

    • A quantile regression model incorporating probe effects can effectively identify hypermethylated CGIs.
    • The optimal quantile range for this model is determined to be 80%-90%.
    • This refined approach improves the accuracy of DNA methylation analysis in cancer research.