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A comprehensive comparison of tools for differential ChIP-seq analysis
ChIP-seq has become a widely adopted genomic assay in recent years to determine binding sites for transcription factors or enrichments for specific histone modifications. Beside detection of enriched or bound regions, an important question is to determine differences between conditions. While this i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5142015/ https://www.ncbi.nlm.nih.gov/pubmed/26764273 http://dx.doi.org/10.1093/bib/bbv110 |
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author | Steinhauser, Sebastian Kurzawa, Nils Eils, Roland Herrmann, Carl |
author_facet | Steinhauser, Sebastian Kurzawa, Nils Eils, Roland Herrmann, Carl |
author_sort | Steinhauser, Sebastian |
collection | PubMed |
description | ChIP-seq has become a widely adopted genomic assay in recent years to determine binding sites for transcription factors or enrichments for specific histone modifications. Beside detection of enriched or bound regions, an important question is to determine differences between conditions. While this is a common analysis for gene expression, for which a large number of computational approaches have been validated, the same question for ChIP-seq is particularly challenging owing to the complexity of ChIP-seq data in terms of noisiness and variability. Many different tools have been developed and published in recent years. However, a comprehensive comparison and review of these tools is still missing. Here, we have reviewed 14 tools, which have been developed to determine differential enrichment between two conditions. They differ in their algorithmic setups, and also in the range of applicability. Hence, we have benchmarked these tools on real data sets for transcription factors and histone modifications, as well as on simulated data sets to quantitatively evaluate their performance. Overall, there is a great variety in the type of signal detected by these tools with a surprisingly low level of agreement. Depending on the type of analysis performed, the choice of method will crucially impact the outcome. |
format | Online Article Text |
id | pubmed-5142015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-51420152016-12-08 A comprehensive comparison of tools for differential ChIP-seq analysis Steinhauser, Sebastian Kurzawa, Nils Eils, Roland Herrmann, Carl Brief Bioinform Software Review ChIP-seq has become a widely adopted genomic assay in recent years to determine binding sites for transcription factors or enrichments for specific histone modifications. Beside detection of enriched or bound regions, an important question is to determine differences between conditions. While this is a common analysis for gene expression, for which a large number of computational approaches have been validated, the same question for ChIP-seq is particularly challenging owing to the complexity of ChIP-seq data in terms of noisiness and variability. Many different tools have been developed and published in recent years. However, a comprehensive comparison and review of these tools is still missing. Here, we have reviewed 14 tools, which have been developed to determine differential enrichment between two conditions. They differ in their algorithmic setups, and also in the range of applicability. Hence, we have benchmarked these tools on real data sets for transcription factors and histone modifications, as well as on simulated data sets to quantitatively evaluate their performance. Overall, there is a great variety in the type of signal detected by these tools with a surprisingly low level of agreement. Depending on the type of analysis performed, the choice of method will crucially impact the outcome. Oxford University Press 2016-11 2016-01-13 /pmc/articles/PMC5142015/ /pubmed/26764273 http://dx.doi.org/10.1093/bib/bbv110 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Software Review Steinhauser, Sebastian Kurzawa, Nils Eils, Roland Herrmann, Carl A comprehensive comparison of tools for differential ChIP-seq analysis |
title | A comprehensive comparison of tools for differential ChIP-seq analysis |
title_full | A comprehensive comparison of tools for differential ChIP-seq analysis |
title_fullStr | A comprehensive comparison of tools for differential ChIP-seq analysis |
title_full_unstemmed | A comprehensive comparison of tools for differential ChIP-seq analysis |
title_short | A comprehensive comparison of tools for differential ChIP-seq analysis |
title_sort | comprehensive comparison of tools for differential chip-seq analysis |
topic | Software Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5142015/ https://www.ncbi.nlm.nih.gov/pubmed/26764273 http://dx.doi.org/10.1093/bib/bbv110 |
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