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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "DMCHMM" in publications use:'
type: software
license: GPL-3.0-only
title: 'DMCHMM: Differentially Methylated CpG using Hidden Markov Model'
version: 1.27.0
abstract: 'A pipeline for identifying differentially methylated CpG sites using Hidden
  Markov Model in bisulfite sequencing data. 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.'
authors:
- family-names: Shokoohi
  given-names: Farhad
  email: shokoohi@icloud.com
  orcid: https://orcid.org/0000-0002-6224-2609
preferred-citation:
  type: manual
  title: 'DMCHMM: an R package to identify differentially methylated CpG Sites using
    Hidden Markov Models, Version 1.11.1, DOI: 10.18129/B9.bioc.DMCHMM'
  authors:
  - family-names: Shokoohi
    given-names: Farhad
    email: shokoohi@icloud.com
    orcid: https://orcid.org/0000-0002-6224-2609
  year: '2020'
  url: http://bioconductor.org/packages/DMCHMM/
repository: https://bioc.r-universe.dev
repository-code: https://github.com/shokoohi/DMCHMM
url: https://github.com/shokoohi/DMCHMM
date-released: '2020-09-27'
contact:
- family-names: Shokoohi
  given-names: Farhad
  email: shokoohi@icloud.com
  orcid: https://orcid.org/0000-0002-6224-2609
references:
- type: article
  title: A Hidden Markov Model for Identifying Differentially Methylated Sites in
    Bisulfite Sequencing Data
  authors:
  - family-names: Shokoohi
    given-names: Farhad
  - family-names: Stephens
    given-names: David
  - family-names: Bourque
    given-names: Guillaume
  - family-names: Pastinen
    given-names: Tomi
  - family-names: Greenwood
    given-names: Celia
  - family-names: Labbe
    given-names: Aurelie
  year: '2018'
  journal: Biometrics
  volume: '75'
  url: https://doi.org/10.1111/biom.12965