HOME: a histogram based machine learning approach for effective identification of differentially methylated regions.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate.

Authors

  • Akanksha Srivastava
    ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Australia.
  • Yuliya V Karpievitch
    ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Australia.
  • Steven R Eichten
    ARC Centre of Excellence in Plant Energy Biology, The Australian National University, Canberra, Australia.
  • Justin O Borevitz
    ARC Centre of Excellence in Plant Energy Biology, The Australian National University, Canberra, Australia.
  • Ryan Lister
    ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Australia. ryan.lister@uwa.edu.au.