Cargando…
Detecting broad domains and narrow peaks in ChIP-seq data with hiddenDomains
BACKGROUND: Correctly identifying genomic regions enriched with histone modifications and transcription factors is key to understanding their regulatory and developmental roles. Conceptually, these regions are divided into two categories, narrow peaks and broad domains, and different algorithms are...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4806451/ https://www.ncbi.nlm.nih.gov/pubmed/27009150 http://dx.doi.org/10.1186/s12859-016-0991-z |
_version_ | 1782423239461437440 |
---|---|
author | Starmer, Joshua Magnuson, Terry |
author_facet | Starmer, Joshua Magnuson, Terry |
author_sort | Starmer, Joshua |
collection | PubMed |
description | BACKGROUND: Correctly identifying genomic regions enriched with histone modifications and transcription factors is key to understanding their regulatory and developmental roles. Conceptually, these regions are divided into two categories, narrow peaks and broad domains, and different algorithms are used to identify each one. Datasets that span these two categories are often analyzed with a single program for peak calling combined with an ad hoc method for domains. RESULTS: We developed hiddenDomains, which identifies both peaks and domains, and compare it to the leading algorithms using H3K27me3, H3K36me3, GABP, ESR1 and FOXA ChIP-seq datasets. The output from the programs was compared to qPCR-validated enriched and depleted sites, predicted transcription factor binding sites, and highly-transcribed gene bodies. With every method, hiddenDomains, performed as well as, if not better than algorithms dedicated to a specific type of analysis. CONCLUSIONS: hiddenDomains performs as well as the best domain and peak calling algorithms, making it ideal for analyzing ChIP-seq datasets, especially those that contain a mixture of peaks and domains. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-0991-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4806451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48064512016-03-24 Detecting broad domains and narrow peaks in ChIP-seq data with hiddenDomains Starmer, Joshua Magnuson, Terry BMC Bioinformatics Software BACKGROUND: Correctly identifying genomic regions enriched with histone modifications and transcription factors is key to understanding their regulatory and developmental roles. Conceptually, these regions are divided into two categories, narrow peaks and broad domains, and different algorithms are used to identify each one. Datasets that span these two categories are often analyzed with a single program for peak calling combined with an ad hoc method for domains. RESULTS: We developed hiddenDomains, which identifies both peaks and domains, and compare it to the leading algorithms using H3K27me3, H3K36me3, GABP, ESR1 and FOXA ChIP-seq datasets. The output from the programs was compared to qPCR-validated enriched and depleted sites, predicted transcription factor binding sites, and highly-transcribed gene bodies. With every method, hiddenDomains, performed as well as, if not better than algorithms dedicated to a specific type of analysis. CONCLUSIONS: hiddenDomains performs as well as the best domain and peak calling algorithms, making it ideal for analyzing ChIP-seq datasets, especially those that contain a mixture of peaks and domains. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-0991-z) contains supplementary material, which is available to authorized users. BioMed Central 2016-03-24 /pmc/articles/PMC4806451/ /pubmed/27009150 http://dx.doi.org/10.1186/s12859-016-0991-z Text en © Starmer and Magnuson. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Starmer, Joshua Magnuson, Terry Detecting broad domains and narrow peaks in ChIP-seq data with hiddenDomains |
title | Detecting broad domains and narrow peaks in ChIP-seq data with hiddenDomains |
title_full | Detecting broad domains and narrow peaks in ChIP-seq data with hiddenDomains |
title_fullStr | Detecting broad domains and narrow peaks in ChIP-seq data with hiddenDomains |
title_full_unstemmed | Detecting broad domains and narrow peaks in ChIP-seq data with hiddenDomains |
title_short | Detecting broad domains and narrow peaks in ChIP-seq data with hiddenDomains |
title_sort | detecting broad domains and narrow peaks in chip-seq data with hiddendomains |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4806451/ https://www.ncbi.nlm.nih.gov/pubmed/27009150 http://dx.doi.org/10.1186/s12859-016-0991-z |
work_keys_str_mv | AT starmerjoshua detectingbroaddomainsandnarrowpeaksinchipseqdatawithhiddendomains AT magnusonterry detectingbroaddomainsandnarrowpeaksinchipseqdatawithhiddendomains |