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Related Experiment Video

Updated: May 7, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

FastDMA: an infinium humanmethylation450 beadchip analyzer.

Dingming Wu1, Jin Gu, Michael Q Zhang

  • 1Bioinformatics Division/Center for Synthetic and Systems Biology, Tsinghua National Laboratory for Information Science and Technology (TNLIST), Department of Automation, Tsinghua University, Beijing, China.

Plos One
|September 17, 2013
PubMed
Summary

FastDMA is a new tool for analyzing genome-wide DNA methylation data from the Illumina HumanMethylation450 Beadchip. It efficiently identifies differentially methylated probes and regions, aiding cancer research.

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Area of Science:

  • Epigenetics and Genomics
  • Computational Biology
  • Cancer Research

Background:

  • DNA methylation is crucial for biological processes and diseases.
  • The Illumina Infinium HumanMethylation450 Beadchip enables genome-wide DNA methylation analysis.
  • Efficient analysis tools are needed for large-scale methylation datasets.

Purpose of the Study:

  • To develop FastDMA, a computational tool for analyzing Illumina HumanMethylation450 Beadchip data.
  • To identify significantly differentially methylated probes and regions (DMRs).
  • To provide a computationally efficient solution for large-scale DNA methylation studies.

Main Methods:

  • Development of FastDMA software.
  • Utilized a unified analysis of covariance (ANCOVA) statistical model.

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Last Updated: May 7, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

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Published on: November 19, 2013

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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  • Applied FastDMA to analyze three large-scale DNA methylation datasets from The Cancer Genome Atlas (TCGA).

    • Main Results:

    • FastDMA successfully identified numerous differentially methylated genomic sites across various cancer types.
    • The tool demonstrated significantly higher computational efficiency compared to existing methods on testing datasets.
    • FastDMA provides an integrated pipeline for analyzing complex DNA methylation data.

    Conclusions:

    • FastDMA offers a powerful and efficient solution for analyzing genome-wide DNA methylation data.
    • The tool facilitates the identification of key methylation changes in cancer.
    • FastDMA is freely available and can benefit large-scale epigenomic studies.