A hidden Markov model for identifying differentially methylated sites in bisulfite sequencing data

Farhad Shokoohi, David A. Stephens, Guillaume Bourque, Tomi Pastinen, Celia M.T. Greenwood, Aurélie Labbe: A hidden Markov model for identifying differentially methylated sites in bisulfite sequencing data. In: Biometrics, 75 , pp. 210-221, 2018.

Abstract

DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read‐depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three‐step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. Our proposed method is different from other HMM methods since it profiles methylation of each sample separately, hence exploiting inter‐CpG autocorrelation within samples, and it is more flexible than previous approaches by allowing multiple hidden states. Using simulations, we show that DMCHMM has the best performance among several competing methods. An analysis of cell‐separated blood methylation profiles is also provided.

BibTeX (Download)

@article{Shokoohi2019biom,
title = {A hidden Markov model for identifying differentially methylated sites in bisulfite sequencing data},
author = {Farhad Shokoohi and David A. Stephens and Guillaume Bourque and Tomi Pastinen and Celia M.T. Greenwood and Aurélie Labbe},
doi = {10.1111/biom.12965},
year  = {2018},
date = {2018-08-31},
journal = {Biometrics},
volume = {75},
pages = {210-221},
address = {https://doi.org/10.1111/biom.12965},
abstract = {DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read‐depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three‐step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. Our proposed method is different from other HMM methods since it profiles methylation of each sample separately, hence exploiting inter‐CpG autocorrelation within samples, and it is more flexible than previous approaches by allowing multiple hidden states. Using simulations, we show that DMCHMM has the best performance among several competing methods. An analysis of cell‐separated blood methylation profiles is also provided.},
keywords = {Blood cell-separated data, Differentially methylated region, Next‐generation sequencing, Read‐depth},
pubstate = {published},
tppubtype = {article}
}