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DMAP: differential methylation analysis package for RRBS and WGBS data.

Peter A Stockwell1, Aniruddha Chatterjee2, Euan J Rodger1

  • 1Department of Biochemistry, University of Otago, 710 Cumberland Street, Dunedin 9054, New Zealand, Department of Pathology, Dunedin School of Medicine, University of Otago, 270 Great King Street, Dunedin 9054, New Zealand and Gravida: National Centre for Growth and Development, 2-6 Park Ave, Grafton, Auckland 1142, New Zealand.

Bioinformatics (Oxford, England)
|March 11, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces DMAP, a new software package for analyzing DNA methylation data. DMAP simplifies the identification of differentially methylated regions from large sequencing datasets, aiding epigenetic research.

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Area of Science:

  • Epigenetics
  • Genomics
  • Bioinformatics

Background:

  • High-throughput sequencing enables large-scale DNA methylation quantification.
  • Increasing data volume presents significant processing and interpretation challenges.
  • Analyzing differential methylation across multiple genome-scale samples is complex.

Purpose of the Study:

  • To develop a user-friendly software package for differential DNA methylation analysis.
  • To address challenges in processing and interpreting large-scale epigenomic data.
  • To facilitate the identification of differentially methylated regions.

Main Methods:

  • Developed Differential Methylation Analysis Package (DMAP) software.
  • Implemented a novel fragment-based approach for reduced representation bisulphite sequencing data.
  • Generated coverage-filtered reference methylomes.

Main Results:

  • DMAP identifies differentially methylated regions across multiple samples.
  • The software supports both reduced representation and whole genome bisulphite sequencing data.
  • Provides gene and CpG feature information with distances to differentially methylated regions.

Conclusions:

  • DMAP offers an accessible format for analyzing DNA methylation patterns.
  • The package aids researchers with limited bioinformatics expertise.
  • Facilitates the investigation of DNA methylation differences in large datasets.