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Continuous chromatin state feature annotation of the human epigenome
MOTIVATION: Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These methods take as input a set of sequencing-based assays of epigenomic activity, such as ChIP-seq measurements of histone modification and transcription factor bind...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9154241/ https://www.ncbi.nlm.nih.gov/pubmed/35451453 http://dx.doi.org/10.1093/bioinformatics/btac283 |
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author | Daneshpajouh, Habib Chen, Bowen Shokraneh, Neda Masoumi, Shohre Wiese, Kay C Libbrecht, Maxwell W |
author_facet | Daneshpajouh, Habib Chen, Bowen Shokraneh, Neda Masoumi, Shohre Wiese, Kay C Libbrecht, Maxwell W |
author_sort | Daneshpajouh, Habib |
collection | PubMed |
description | MOTIVATION: Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These methods take as input a set of sequencing-based assays of epigenomic activity, such as ChIP-seq measurements of histone modification and transcription factor binding. They output an annotation of the genome that assigns a chromatin state label to each genomic position. Existing SAGA methods have several limitations caused by the discrete annotation framework: such annotations cannot easily represent varying strengths of genomic elements, and they cannot easily represent combinatorial elements that simultaneously exhibit multiple types of activity. To remedy these limitations, we propose an annotation strategy that instead outputs a vector of chromatin state features at each position rather than a single discrete label. Continuous modeling is common in other fields, such as in topic modeling of text documents. We propose a method, epigenome-ssm-nonneg, that uses a non-negative state space model to efficiently annotate the genome with chromatin state features. We also propose several measures of the quality of a chromatin state feature annotation and we compare the performance of several alternative methods according to these quality measures. RESULTS: We show that chromatin state features from epigenome-ssm-nonneg are more useful for several downstream applications than both continuous and discrete alternatives, including their ability to identify expressed genes and enhancers. Therefore, we expect that these continuous chromatin state features will be valuable reference annotations to be used in visualization and downstream analysis. AVAILABILITY AND IMPLEMENTATION: Source code for epigenome-ssm is available at https://github.com/habibdanesh/epigenome-ssm and Zenodo (DOI: 10.5281/zenodo.6507585). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-9154241 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-91542412022-06-04 Continuous chromatin state feature annotation of the human epigenome Daneshpajouh, Habib Chen, Bowen Shokraneh, Neda Masoumi, Shohre Wiese, Kay C Libbrecht, Maxwell W Bioinformatics Original Papers MOTIVATION: Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These methods take as input a set of sequencing-based assays of epigenomic activity, such as ChIP-seq measurements of histone modification and transcription factor binding. They output an annotation of the genome that assigns a chromatin state label to each genomic position. Existing SAGA methods have several limitations caused by the discrete annotation framework: such annotations cannot easily represent varying strengths of genomic elements, and they cannot easily represent combinatorial elements that simultaneously exhibit multiple types of activity. To remedy these limitations, we propose an annotation strategy that instead outputs a vector of chromatin state features at each position rather than a single discrete label. Continuous modeling is common in other fields, such as in topic modeling of text documents. We propose a method, epigenome-ssm-nonneg, that uses a non-negative state space model to efficiently annotate the genome with chromatin state features. We also propose several measures of the quality of a chromatin state feature annotation and we compare the performance of several alternative methods according to these quality measures. RESULTS: We show that chromatin state features from epigenome-ssm-nonneg are more useful for several downstream applications than both continuous and discrete alternatives, including their ability to identify expressed genes and enhancers. Therefore, we expect that these continuous chromatin state features will be valuable reference annotations to be used in visualization and downstream analysis. AVAILABILITY AND IMPLEMENTATION: Source code for epigenome-ssm is available at https://github.com/habibdanesh/epigenome-ssm and Zenodo (DOI: 10.5281/zenodo.6507585). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-04-22 /pmc/articles/PMC9154241/ /pubmed/35451453 http://dx.doi.org/10.1093/bioinformatics/btac283 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Daneshpajouh, Habib Chen, Bowen Shokraneh, Neda Masoumi, Shohre Wiese, Kay C Libbrecht, Maxwell W Continuous chromatin state feature annotation of the human epigenome |
title | Continuous chromatin state feature annotation of the human epigenome |
title_full | Continuous chromatin state feature annotation of the human epigenome |
title_fullStr | Continuous chromatin state feature annotation of the human epigenome |
title_full_unstemmed | Continuous chromatin state feature annotation of the human epigenome |
title_short | Continuous chromatin state feature annotation of the human epigenome |
title_sort | continuous chromatin state feature annotation of the human epigenome |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9154241/ https://www.ncbi.nlm.nih.gov/pubmed/35451453 http://dx.doi.org/10.1093/bioinformatics/btac283 |
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