A Metric to Characterize Differentially Methylated Region Sets Detected from Methylation Array Data

  • Authors: Peng X.1, Cui W.2, Zhang W.3, Li Z.1, Zhu X.4, Yuan L.5, Li J.6
  • Affiliations:
    1. Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University,
    2. Hunan Key Laboratory of Bioinformatics, School of Computer Science and Engineering,, Central South University
    3. Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences,, Central South University
    4. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology,
    5. Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University
    6. Department of Hematology, The Second Xiangya hospital,, Central South University
  • Issue: Vol 19, No 6 (2024)
  • Pages: 571-583
  • Section: Life Sciences
  • URL: https://jdigitaldiagnostics.com/1574-8936/article/view/643959
  • DOI: https://doi.org/10.2174/1574893618666230816141723
  • ID: 643959

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Abstract

Background:Identifying differentially methylated region (DMR) is a basic but important task in epigenomics, which can help investigate the mechanisms of diseases and provide methylation biomarkers for screening diseases. A set of methods have been proposed to identify DMRs from methylation array data. However, it lacks effective metrics to characterize different DMR sets and enable a straight way for comparison.

Methods:In this study, we introduce a metric, DMRn, to characterize DMR sets detected by different methods from methylation array data. To calculate DMRn, firstly, the methylation differences of DMRs are recalculated by incorporating the correlations between probes and their represented CpGs. Then, DMRn is calculated based on the number of probes and the dense of CpGs in DMRs with methylation differences falling in each interval.

Result & Discussion:By comparing the DMRn of DMR sets predicted by seven methods on four scenario, the results demonstrate that DMRn can make an efficient guidance for selecting DMR sets, and provide new insights in cancer genomics studies by comparing the DMR sets from the related pathological states. For example, there are many regions with subtle methylation alteration in subtypes of prostate cancer are altered oppositely in the benign state, which may indicate a possible revision mechanism in benign prostate cancer.

Conclusion:Futhermore, when applied to datasets that underwent different runs of batch effect removal, the DMRn can help to visualize the bias introduced by multi-runs of batch effect removal. The tool for calculating DMRn is available in the GitHub repository(https://github.com/xqpeng/DMRArrayMetric).

About the authors

Xiaoqing Peng

Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University,

Email: info@benthamscience.net

Wanxin Cui

Hunan Key Laboratory of Bioinformatics, School of Computer Science and Engineering,, Central South University

Email: info@benthamscience.net

Wenjin Zhang

Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences,, Central South University

Email: info@benthamscience.net

Zihao Li

Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University,

Email: info@benthamscience.net

Xiaoshu Zhu

Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology,

Email: info@benthamscience.net

Ling Yuan

Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University

Email: info@benthamscience.net

Ji Li

Department of Hematology, The Second Xiangya hospital,, Central South University

Author for correspondence.
Email: info@benthamscience.net

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