Cargando…

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

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 diff...

Descripción completa

Detalles Bibliográficos
Autores principales: Malonzo, Maia H., Lähdesmäki, Harri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945676/
https://www.ncbi.nlm.nih.gov/pubmed/36810075
http://dx.doi.org/10.1186/s12859-023-05174-7
_version_ 1784892186866745344
author Malonzo, Maia H.
Lähdesmäki, Harri
author_facet Malonzo, Maia H.
Lähdesmäki, Harri
author_sort Malonzo, Maia H.
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05174-7.
format Online
Article
Text
id pubmed-9945676
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-99456762023-02-23 LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model Malonzo, Maia H. Lähdesmäki, Harri BMC Bioinformatics Software 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05174-7. BioMed Central 2023-02-22 /pmc/articles/PMC9945676/ /pubmed/36810075 http://dx.doi.org/10.1186/s12859-023-05174-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Malonzo, Maia H.
Lähdesmäki, Harri
LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model
title LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model
title_full LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model
title_fullStr LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model
title_full_unstemmed LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model
title_short LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model
title_sort luxhmm: dna methylation analysis with genome segmentation via hidden markov model
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945676/
https://www.ncbi.nlm.nih.gov/pubmed/36810075
http://dx.doi.org/10.1186/s12859-023-05174-7
work_keys_str_mv AT malonzomaiah luxhmmdnamethylationanalysiswithgenomesegmentationviahiddenmarkovmodel
AT lahdesmakiharri luxhmmdnamethylationanalysiswithgenomesegmentationviahiddenmarkovmodel