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

Kuvaus

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.
Näytä enemmän

Julkaisuvuosi

2023

Aineiston tyyppi

Tekijät

Department of Computer Science

Harri Lähdesmäki - Tekijä

Maia H. Malonzo - Tekijä

figshare - Julkaisija

Projekti

Muut tiedot

Tieteenalat

Tietojenkäsittely ja informaatiotieteet

Kieli

Saatavuus

Avoin

Lisenssi

Creative Commons Nimeä 4.0 Kansainvälinen (CC BY 4.0)

Avainsanat

Asiasanat

Ajallinen kattavuus

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