LuxHMM : DNA methylation analysis with genome segmentation via hidden Markov model

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2023-12
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
BMC Bioinformatics, Volume 24, issue 1
Abstract
Background: DNA methylation plays an important role in studying the epigenetics of various biological processes including many diseases. Although differential methylation of individual cytosines can be informative, given that methylation of neighboring CpGs are typically correlated, analysis of differentially methylated regions is often of more interest. Results: We have developed a probabilistic method and software, LuxHMM, that uses hidden Markov model (HMM) to segment the genome into regions and a Bayesian regression model, which allows handling of multiple covariates, to infer differential methylation of regions. Moreover, our model includes experimental parameters that describe the underlying biochemistry in bisulfite sequencing and model inference is done using either variational inference for efficient genome-scale analysis or Hamiltonian Monte Carlo (HMC). Conclusions: Analyses of real and simulated bisulfite sequencing data demonstrate the competitive performance of LuxHMM compared with other published differential methylation analysis methods.
Description
Funding Information: This work was supported by the Ella and Georg Ehrnrooth Foundation and the Academy of Finland (grant number 314445). The funding body played no role in the design of the study, the collection, analysis, interpretation of data, or in writing the manuscript. Publisher Copyright: © 2023, The Author(s).
Keywords
Bisulfite sequencing, HMM, Methylation, Probabilistic
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Citation
Malonzo , M H & Lähdesmäki , H 2023 , ' LuxHMM : DNA methylation analysis with genome segmentation via hidden Markov model ' , BMC Bioinformatics , vol. 24 , no. 1 , 58 . https://doi.org/10.1186/s12859-023-05174-7