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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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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 |
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author | Jaroszewicz, Artur Ernst, Jason |
author_facet | Jaroszewicz, Artur Ernst, Jason |
author_sort | Jaroszewicz, Artur |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10486095 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104860952023-09-09 ChromGene: gene-based modeling of epigenomic data Jaroszewicz, Artur Ernst, Jason Genome Biol Method 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. BioMed Central 2023-09-07 /pmc/articles/PMC10486095/ /pubmed/37679846 http://dx.doi.org/10.1186/s13059-023-03041-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 | Method Jaroszewicz, Artur Ernst, Jason ChromGene: gene-based modeling of epigenomic data |
title | ChromGene: gene-based modeling of epigenomic data |
title_full | ChromGene: gene-based modeling of epigenomic data |
title_fullStr | ChromGene: gene-based modeling of epigenomic data |
title_full_unstemmed | ChromGene: gene-based modeling of epigenomic data |
title_short | ChromGene: gene-based modeling of epigenomic data |
title_sort | chromgene: gene-based modeling of epigenomic data |
topic | Method |
url | 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 |
work_keys_str_mv | AT jaroszewiczartur chromgenegenebasedmodelingofepigenomicdata AT ernstjason chromgenegenebasedmodelingofepigenomicdata |