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Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns
Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These algorithms take as input epigenomic datasets, such as chromatin immunoprecipitation-sequencing (ChIP-seq) measurements of histone modifications or transcription factor bindin...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516206/ https://www.ncbi.nlm.nih.gov/pubmed/34648491 http://dx.doi.org/10.1371/journal.pcbi.1009423 |
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author | Libbrecht, Maxwell W. Chan, Rachel C. W. Hoffman, Michael M. |
author_facet | Libbrecht, Maxwell W. Chan, Rachel C. W. Hoffman, Michael M. |
author_sort | Libbrecht, Maxwell W. |
collection | PubMed |
description | Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These algorithms take as input epigenomic datasets, such as chromatin immunoprecipitation-sequencing (ChIP-seq) measurements of histone modifications or transcription factor binding. They partition the genome and assign a label to each segment such that positions with the same label exhibit similar patterns of input data. SAGA algorithms discover categories of activity such as promoters, enhancers, or parts of genes without prior knowledge of known genomic elements. In this sense, they generally act in an unsupervised fashion like clustering algorithms, but with the additional simultaneous function of segmenting the genome. Here, we review the common methodological framework that underlies these methods, review variants of and improvements upon this basic framework, and discuss the outlook for future work. This review is intended for those interested in applying SAGA methods and for computational researchers interested in improving upon them. |
format | Online Article Text |
id | pubmed-8516206 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-85162062021-10-15 Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns Libbrecht, Maxwell W. Chan, Rachel C. W. Hoffman, Michael M. PLoS Comput Biol Review Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These algorithms take as input epigenomic datasets, such as chromatin immunoprecipitation-sequencing (ChIP-seq) measurements of histone modifications or transcription factor binding. They partition the genome and assign a label to each segment such that positions with the same label exhibit similar patterns of input data. SAGA algorithms discover categories of activity such as promoters, enhancers, or parts of genes without prior knowledge of known genomic elements. In this sense, they generally act in an unsupervised fashion like clustering algorithms, but with the additional simultaneous function of segmenting the genome. Here, we review the common methodological framework that underlies these methods, review variants of and improvements upon this basic framework, and discuss the outlook for future work. This review is intended for those interested in applying SAGA methods and for computational researchers interested in improving upon them. Public Library of Science 2021-10-14 /pmc/articles/PMC8516206/ /pubmed/34648491 http://dx.doi.org/10.1371/journal.pcbi.1009423 Text en © 2021 Libbrecht et al 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 use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Review Libbrecht, Maxwell W. Chan, Rachel C. W. Hoffman, Michael M. Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns |
title | Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns |
title_full | Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns |
title_fullStr | Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns |
title_full_unstemmed | Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns |
title_short | Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns |
title_sort | segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516206/ https://www.ncbi.nlm.nih.gov/pubmed/34648491 http://dx.doi.org/10.1371/journal.pcbi.1009423 |
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