PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals.
PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals.
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DOI：
10.12688/f1000research.53926.1
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发表时间：
2021-01-01
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影响因子：
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通讯作者：
Iqbal, Mudassar
中科院分区：
文献类型：
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作者：
Briggs, Peter;Hunter, A Louise;Iqbal, Mudassar
许多转录控制机制的生物学研究从相应的基因表达和表观基因组分析中得出了基因列表和非编码基因组区间。在高等生物中，如真核生物，基因可能受到远端元件的调控，这些元件位于距离基因转录起始位点10 -100千碱基的地方。为了深入了解这些远端调控机制，确定与感兴趣的基因组区域相关的感兴趣基因的相对富集是很重要的，并且能够在一定距离范围内这样做。现有的生物信息学工具可以注释基因组区域到最近的已知基因，或者寻找与基因转录起始位点相关的转录因子结合位点。在这里，我们提出了PEGS（基因集的峰集富集）。该工具通过计算多个基因组距离上与多个非编码元件（峰集）相关的多个基因集的富集程度，以及在拓扑相关域内，有效地提供了探索性分析。我们将PEGS应用于来自基因表达研究的基因集，以及来自相应ChIP-seq和ATAC-seq实验的基因组间隔，以获得具有生物学意义的结果。我们还展示了对组织特异性基因集和公开可用的GWAS数据的扩展应用，以发现与组织特异性基因表达谱相关的睡眠特征相关snp的富集。
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.