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BMC bioinformatics
Jan 23, 2024;25 (1): 37.

A novel approach toward optimal workflow selection for DNA methylation biomarker discovery.

Naghme Nazer, Mohammad Hossein Sepehri, Hoda Mohammadzade, Mahya Mehrmohamadi
Author Information
  1. Naghme Nazer: Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
  2. Mohammad Hossein Sepehri: Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
  3. Hoda Mohammadzade: Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
  4. Mahya Mehrmohamadi: Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. mehrmohamadi@ut.ac.ir.
PMID: 38262949 DOI: 10.1186/s12859-024-05658-0

Abstract

DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis workflows. Our study demonstrates that the choice of analysis pipeline for DNA methylation-based marker discovery is crucial and different across different contexts.

Keywords

DNA methylation marker discovery Data analysis pipeline optimization Simulation of DNA methylation array data

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Grants

  1. 99012262/Iran National Science Foundation

MeSH Term

DNA Methylation
Workflow
Epigenesis, Genetic
Biomedical Research
Data Analysis
Journal Article

Word Cloud

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