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Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection
BACKGROUND: The analysis of chromatin binding patterns of proteins in different biological states is a main application of chromatin immunoprecipitation followed by sequencing (ChIP-seq). A large number of algorithms and computational tools for quantitative comparison of ChIP-seq datasets exist, but...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9128273/ https://www.ncbi.nlm.nih.gov/pubmed/35606795 http://dx.doi.org/10.1186/s13059-022-02686-y |
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author | Eder, Thomas Grebien, Florian |
author_facet | Eder, Thomas Grebien, Florian |
author_sort | Eder, Thomas |
collection | PubMed |
description | BACKGROUND: The analysis of chromatin binding patterns of proteins in different biological states is a main application of chromatin immunoprecipitation followed by sequencing (ChIP-seq). A large number of algorithms and computational tools for quantitative comparison of ChIP-seq datasets exist, but their performance is strongly dependent on the parameters of the biological system under investigation. Thus, a systematic assessment of available computational tools for differential ChIP-seq analysis is required to guide the optimal selection of analysis tools based on the present biological scenario. RESULTS: We created standardized reference datasets by in silico simulation and sub-sampling of genuine ChIP-seq data to represent different biological scenarios and binding profiles. Using these data, we evaluated the performance of 33 computational tools and approaches for differential ChIP-seq analysis. Tool performance was strongly dependent on peak size and shape as well as on the scenario of biological regulation. CONCLUSIONS: Our analysis provides unbiased guidelines for the optimized choice of software tools in differential ChIP-seq analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02686-y. |
format | Online Article Text |
id | pubmed-9128273 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91282732022-05-25 Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection Eder, Thomas Grebien, Florian Genome Biol Research BACKGROUND: The analysis of chromatin binding patterns of proteins in different biological states is a main application of chromatin immunoprecipitation followed by sequencing (ChIP-seq). A large number of algorithms and computational tools for quantitative comparison of ChIP-seq datasets exist, but their performance is strongly dependent on the parameters of the biological system under investigation. Thus, a systematic assessment of available computational tools for differential ChIP-seq analysis is required to guide the optimal selection of analysis tools based on the present biological scenario. RESULTS: We created standardized reference datasets by in silico simulation and sub-sampling of genuine ChIP-seq data to represent different biological scenarios and binding profiles. Using these data, we evaluated the performance of 33 computational tools and approaches for differential ChIP-seq analysis. Tool performance was strongly dependent on peak size and shape as well as on the scenario of biological regulation. CONCLUSIONS: Our analysis provides unbiased guidelines for the optimized choice of software tools in differential ChIP-seq analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02686-y. BioMed Central 2022-05-24 /pmc/articles/PMC9128273/ /pubmed/35606795 http://dx.doi.org/10.1186/s13059-022-02686-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Research Eder, Thomas Grebien, Florian Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection |
title | Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection |
title_full | Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection |
title_fullStr | Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection |
title_full_unstemmed | Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection |
title_short | Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection |
title_sort | comprehensive assessment of differential chip-seq tools guides optimal algorithm selection |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9128273/ https://www.ncbi.nlm.nih.gov/pubmed/35606795 http://dx.doi.org/10.1186/s13059-022-02686-y |
work_keys_str_mv | AT ederthomas comprehensiveassessmentofdifferentialchipseqtoolsguidesoptimalalgorithmselection AT grebienflorian comprehensiveassessmentofdifferentialchipseqtoolsguidesoptimalalgorithmselection |