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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...

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Autores principales: Eder, Thomas, Grebien, Florian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
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.
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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
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