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.
复制标题
DOI：
10.12688/f1000research.53926.2
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Iqbal M
中科院分区：
文献类型：
--
作者：
Briggs P;Hunter AL;Yang SH;Sharrocks AD;Iqbal M
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测定中产生基因和非编码基因组间隔的列表。在高等生物体中，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个酶的位置。为了深入了解这些远端调控机制，重要的是确定感兴趣的基因相对于感兴趣的基因组区域的相对富集，并且能够在一定范围的距离上这样做。现有的生物信息学工具可以将基因组区域注释到最近的已知基因，或者寻找与基因转录起始位点相关的转录因子结合位点。PEGS（Peak set Enrichment in Gene Sets，基因集中的峰集富集）该工具通过计算与多个非编码元件（峰集）相关的多个基因集在多个基因组距离处和拓扑相关域内的富集来有效地提供探索性分析。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性状相关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.