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intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data
BACKGROUND: RNA-Seq, the high-throughput sequencing (HT-Seq) of mRNAs, has become an essential tool for characterizing gene expression differences between different cell types and conditions. Gene expression is regulated by several mechanisms, including epigenetically by post-translational histone m...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771091/ https://www.ncbi.nlm.nih.gov/pubmed/33372591 http://dx.doi.org/10.1186/s12864-020-07205-6 |
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author | Cao, Yingying Kitanovski, Simo Hoffmann, Daniel |
author_facet | Cao, Yingying Kitanovski, Simo Hoffmann, Daniel |
author_sort | Cao, Yingying |
collection | PubMed |
description | BACKGROUND: RNA-Seq, the high-throughput sequencing (HT-Seq) of mRNAs, has become an essential tool for characterizing gene expression differences between different cell types and conditions. Gene expression is regulated by several mechanisms, including epigenetically by post-translational histone modifications which can be assessed by ChIP-Seq (Chromatin Immuno-Precipitation Sequencing). As more and more biological samples are analyzed by the combination of ChIP-Seq and RNA-Seq, the integrated analysis of the corresponding data sets becomes, theoretically, a unique option to study gene regulation. However, technically such analyses are still in their infancy. RESULTS: Here we introduce intePareto, a computational tool for the integrative analysis of RNA-Seq and ChIP-Seq data. With intePareto we match RNA-Seq and ChIP-Seq data at the level of genes, perform differential expression analysis between biological conditions, and prioritize genes with consistent changes in RNA-Seq and ChIP-Seq data using Pareto optimization. CONCLUSION: intePareto facilitates comprehensive understanding of high dimensional transcriptomic and epigenomic data. Its superiority to a naive differential gene expression analysis with RNA-Seq and available integrative approach is demonstrated by analyzing a public dataset. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (doi:10.1186/s12864-020-07205-6). |
format | Online Article Text |
id | pubmed-7771091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77710912020-12-30 intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data Cao, Yingying Kitanovski, Simo Hoffmann, Daniel BMC Genomics Software BACKGROUND: RNA-Seq, the high-throughput sequencing (HT-Seq) of mRNAs, has become an essential tool for characterizing gene expression differences between different cell types and conditions. Gene expression is regulated by several mechanisms, including epigenetically by post-translational histone modifications which can be assessed by ChIP-Seq (Chromatin Immuno-Precipitation Sequencing). As more and more biological samples are analyzed by the combination of ChIP-Seq and RNA-Seq, the integrated analysis of the corresponding data sets becomes, theoretically, a unique option to study gene regulation. However, technically such analyses are still in their infancy. RESULTS: Here we introduce intePareto, a computational tool for the integrative analysis of RNA-Seq and ChIP-Seq data. With intePareto we match RNA-Seq and ChIP-Seq data at the level of genes, perform differential expression analysis between biological conditions, and prioritize genes with consistent changes in RNA-Seq and ChIP-Seq data using Pareto optimization. CONCLUSION: intePareto facilitates comprehensive understanding of high dimensional transcriptomic and epigenomic data. Its superiority to a naive differential gene expression analysis with RNA-Seq and available integrative approach is demonstrated by analyzing a public dataset. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (doi:10.1186/s12864-020-07205-6). BioMed Central 2020-12-29 /pmc/articles/PMC7771091/ /pubmed/33372591 http://dx.doi.org/10.1186/s12864-020-07205-6 Text en © The Author(s) 2020 Open Access This 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/. 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 in a credit line to the data. |
spellingShingle | Software Cao, Yingying Kitanovski, Simo Hoffmann, Daniel intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data |
title | intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data |
title_full | intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data |
title_fullStr | intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data |
title_full_unstemmed | intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data |
title_short | intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data |
title_sort | intepareto: an r package for integrative analyses of rna-seq and chip-seq data |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771091/ https://www.ncbi.nlm.nih.gov/pubmed/33372591 http://dx.doi.org/10.1186/s12864-020-07205-6 |
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