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ChromGene: gene-based modeling of epigenomic data

Various computational approaches have been developed to annotate epigenomes on a per-position basis by modeling combinatorial and spatial patterns within epigenomic data. However, such annotations are less suitable for gene-based analyses. We present ChromGene, a method based on a mixture of learned...

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Detalles Bibliográficos
Autores principales: Jaroszewicz, Artur, Ernst, Jason
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486095/
https://www.ncbi.nlm.nih.gov/pubmed/37679846
http://dx.doi.org/10.1186/s13059-023-03041-5
Descripción
Sumario:Various computational approaches have been developed to annotate epigenomes on a per-position basis by modeling combinatorial and spatial patterns within epigenomic data. However, such annotations are less suitable for gene-based analyses. We present ChromGene, a method based on a mixture of learned hidden Markov models, to annotate genes based on multiple epigenomic maps across the gene body and flanks. We provide ChromGene assignments for over 100 cell and tissue types. We characterize the mixture components in terms of gene expression, constraint, and other gene annotations. The ChromGene method and annotations will provide a useful resource for gene-based epigenomic analyses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03041-5.