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CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes

BACKGROUND: Multiple studies rely on ChIP-seq experiments to assess the effect of gene modulation and drug treatments on protein binding and chromatin structure. However, most methods commonly used for the normalization of ChIP-seq binding intensity signals across conditions, e.g., the normalization...

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Autores principales: Polit, Lélia, Kerdivel, Gwenneg, Gregoricchio, Sebastian, Esposito, Michela, Guillouf, Christel, Boeva, Valentina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371782/
https://www.ncbi.nlm.nih.gov/pubmed/34404353
http://dx.doi.org/10.1186/s12859-021-04320-3
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author Polit, Lélia
Kerdivel, Gwenneg
Gregoricchio, Sebastian
Esposito, Michela
Guillouf, Christel
Boeva, Valentina
author_facet Polit, Lélia
Kerdivel, Gwenneg
Gregoricchio, Sebastian
Esposito, Michela
Guillouf, Christel
Boeva, Valentina
author_sort Polit, Lélia
collection PubMed
description BACKGROUND: Multiple studies rely on ChIP-seq experiments to assess the effect of gene modulation and drug treatments on protein binding and chromatin structure. However, most methods commonly used for the normalization of ChIP-seq binding intensity signals across conditions, e.g., the normalization to the same number of reads, either assume a constant signal-to-noise ratio across conditions or base the estimates of correction factors on genomic regions with intrinsically different signals between conditions. Inaccurate normalization of ChIP-seq signal may, in turn, lead to erroneous biological conclusions. RESULTS: We developed a new R package, CHIPIN, that allows normalizing ChIP-seq signals across different conditions/samples when spike-in information is not available, but gene expression data are at hand. Our normalization technique is based on the assumption that, on average, no differences in ChIP-seq signals should be observed in the regulatory regions of genes whose expression levels are constant across samples/conditions. In addition to normalizing ChIP-seq signals, CHIPIN provides as output a number of graphs and calculates statistics allowing the user to assess the efficiency of the normalization and qualify the specificity of the antibody used. In addition to ChIP-seq, CHIPIN can be used without restriction on open chromatin ATAC-seq or DNase hypersensitivity data. We validated the CHIPIN method on several ChIP-seq data sets and documented its superior performance in comparison to several commonly used normalization techniques. CONCLUSIONS: The CHIPIN method provides a new way for ChIP-seq signal normalization across conditions when spike-in experiments are not available. The method is implemented in a user-friendly R package available on GitHub: https://github.com/BoevaLab/CHIPIN SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04320-3.
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spelling pubmed-83717822021-08-18 CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes Polit, Lélia Kerdivel, Gwenneg Gregoricchio, Sebastian Esposito, Michela Guillouf, Christel Boeva, Valentina BMC Bioinformatics Software BACKGROUND: Multiple studies rely on ChIP-seq experiments to assess the effect of gene modulation and drug treatments on protein binding and chromatin structure. However, most methods commonly used for the normalization of ChIP-seq binding intensity signals across conditions, e.g., the normalization to the same number of reads, either assume a constant signal-to-noise ratio across conditions or base the estimates of correction factors on genomic regions with intrinsically different signals between conditions. Inaccurate normalization of ChIP-seq signal may, in turn, lead to erroneous biological conclusions. RESULTS: We developed a new R package, CHIPIN, that allows normalizing ChIP-seq signals across different conditions/samples when spike-in information is not available, but gene expression data are at hand. Our normalization technique is based on the assumption that, on average, no differences in ChIP-seq signals should be observed in the regulatory regions of genes whose expression levels are constant across samples/conditions. In addition to normalizing ChIP-seq signals, CHIPIN provides as output a number of graphs and calculates statistics allowing the user to assess the efficiency of the normalization and qualify the specificity of the antibody used. In addition to ChIP-seq, CHIPIN can be used without restriction on open chromatin ATAC-seq or DNase hypersensitivity data. We validated the CHIPIN method on several ChIP-seq data sets and documented its superior performance in comparison to several commonly used normalization techniques. CONCLUSIONS: The CHIPIN method provides a new way for ChIP-seq signal normalization across conditions when spike-in experiments are not available. The method is implemented in a user-friendly R package available on GitHub: https://github.com/BoevaLab/CHIPIN SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04320-3. BioMed Central 2021-08-17 /pmc/articles/PMC8371782/ /pubmed/34404353 http://dx.doi.org/10.1186/s12859-021-04320-3 Text en © The Author(s) 2021 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 Software
Polit, Lélia
Kerdivel, Gwenneg
Gregoricchio, Sebastian
Esposito, Michela
Guillouf, Christel
Boeva, Valentina
CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes
title CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes
title_full CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes
title_fullStr CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes
title_full_unstemmed CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes
title_short CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes
title_sort chipin: chip-seq inter-sample normalization based on signal invariance across transcriptionally constant genes
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371782/
https://www.ncbi.nlm.nih.gov/pubmed/34404353
http://dx.doi.org/10.1186/s12859-021-04320-3
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