Journal Article

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

Borevitz, Justin O., Eichten, Steven R., Karpievitch, Yuliya V., Srivastava, Akanksha, Lister, Ryan

2019

Overview

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. Results We present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organism’s dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME . Conclusion HOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Artificial intelligence / Bioinformatics / Biological variation / Biomedical and Life Sciences / Bisulfite / Cell differentiation / Chromatin / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Computer simulation / Datasets / Deoxyribonucleic acid / DMR identification / DNA / DNA methylation / DNA sequencing / Epigenetic inheritance / Epigenetics / Gene sequencing / Genes / Genetic research / Genomes / Genomics / Histograms / Identification / Learning algorithms / Life Sciences / Machine learning / Mammals / Methodology / Methodology Article / Methods / Methylation / Microarrays / Novels / Sequence analysis (methods) / Stem cells / Sulfites / Support vector machines / SVM / Time / Time series / Whole genome bisulfite sequencing