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PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals
Many biological studies of transcriptional control mechanisms produce lists of genes and non-coding genomic intervals from corresponding gene expression and epigenomic assays. In higher organisms, such as eukaryotes, genes may be regulated by distal elements, with these elements lying 10s–100s of ki...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8406447/ https://www.ncbi.nlm.nih.gov/pubmed/34504687 http://dx.doi.org/10.12688/f1000research.53926.2 |
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author | Briggs, Peter Hunter, A. Louise Yang, Shen-hsi Sharrocks, Andrew D. Iqbal, Mudassar |
author_facet | Briggs, Peter Hunter, A. Louise Yang, Shen-hsi Sharrocks, Andrew D. Iqbal, Mudassar |
author_sort | Briggs, Peter |
collection | PubMed |
description | Many biological studies of transcriptional control mechanisms produce lists of genes and non-coding genomic intervals from corresponding gene expression and epigenomic assays. In higher organisms, such as eukaryotes, genes may be regulated by distal elements, with these elements lying 10s–100s of kilobases away from a gene transcription start site. To gain insight into these distal regulatory mechanisms, it is important to determine comparative enrichment of genes of interest in relation to genomic regions of interest, and to be able to do so at a range of distances. Existing bioinformatics tools can annotate genomic regions to nearest known genes, or look for transcription factor binding sites in relation to gene transcription start sites. Here, we present PEGS ( Peak set Enrichment in Gene Sets). This tool efficiently provides an exploratory analysis by calculating enrichment of multiple gene sets, associated with multiple non-coding elements (peak sets), at multiple genomic distances, and within topologically associated domains. We apply PEGS to gene sets derived from gene expression studies, and genomic intervals from corresponding ChIP-seq and ATAC-seq experiments to derive biologically meaningful results. We also demonstrate an extended application to tissue-specific gene sets and publicly available GWAS data, to find enrichment of sleep trait associated SNPs in relation to tissue-specific gene expression profiles. |
format | Online Article Text |
id | pubmed-8406447 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-84064472021-09-08 PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals Briggs, Peter Hunter, A. Louise Yang, Shen-hsi Sharrocks, Andrew D. Iqbal, Mudassar F1000Res Software Tool Article Many biological studies of transcriptional control mechanisms produce lists of genes and non-coding genomic intervals from corresponding gene expression and epigenomic assays. In higher organisms, such as eukaryotes, genes may be regulated by distal elements, with these elements lying 10s–100s of kilobases away from a gene transcription start site. To gain insight into these distal regulatory mechanisms, it is important to determine comparative enrichment of genes of interest in relation to genomic regions of interest, and to be able to do so at a range of distances. Existing bioinformatics tools can annotate genomic regions to nearest known genes, or look for transcription factor binding sites in relation to gene transcription start sites. Here, we present PEGS ( Peak set Enrichment in Gene Sets). This tool efficiently provides an exploratory analysis by calculating enrichment of multiple gene sets, associated with multiple non-coding elements (peak sets), at multiple genomic distances, and within topologically associated domains. We apply PEGS to gene sets derived from gene expression studies, and genomic intervals from corresponding ChIP-seq and ATAC-seq experiments to derive biologically meaningful results. We also demonstrate an extended application to tissue-specific gene sets and publicly available GWAS data, to find enrichment of sleep trait associated SNPs in relation to tissue-specific gene expression profiles. F1000 Research Limited 2021-11-02 /pmc/articles/PMC8406447/ /pubmed/34504687 http://dx.doi.org/10.12688/f1000research.53926.2 Text en Copyright: © 2021 Briggs P et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Tool Article Briggs, Peter Hunter, A. Louise Yang, Shen-hsi Sharrocks, Andrew D. Iqbal, Mudassar PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals |
title | PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals |
title_full | PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals |
title_fullStr | PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals |
title_full_unstemmed | PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals |
title_short | PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals |
title_sort | pegs: an efficient tool for gene set enrichment within defined sets of genomic intervals |
topic | Software Tool Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8406447/ https://www.ncbi.nlm.nih.gov/pubmed/34504687 http://dx.doi.org/10.12688/f1000research.53926.2 |
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