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Medientyp:
E-Artikel
Titel:
EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery
Beteiligte:
Schmitz, Johanna Elena;
Aggarwal, Nihit;
Laufer, Lukas;
Walter, Jörn;
Salhab, Abdulrahman;
Rahmann, Sven
Erschienen:
Oxford University Press (OUP), 2024
Erschienen in:Bioinformatics
Sprache:
Englisch
DOI:
10.1093/bioinformatics/btae178
ISSN:
1367-4811
Entstehung:
Anmerkungen:
Beschreibung:
<jats:title>Abstract</jats:title>
<jats:sec>
<jats:title>Motivation</jats:title>
<jats:p>Automated chromatin segmentation based on ChIP-seq (chromatin immunoprecipitation followed by sequencing) data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>We introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with existing tools, ChromHMM, Segway, and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an accurate probabilistic model, which has the potential to increase the biological interpretability of chromatin states.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Availability and implementation</jats:title>
<jats:p>Source code: https://gitlab.com/rahmannlab/episegmix.</jats:p>
</jats:sec>