The University of Queensland, Brisbane 4072, Australia.
The University of Queensland, Brisbane 4072, Australia.
Methods. 2017 Dec 1;131:111-119. doi: 10.1016/j.ymeth.2017.09.001. Epub 2017 Sep 7.
Transcription factors regulate gene expression and play an essential role in development by maintaining proliferative states, driving cellular differentiation and determining cell fate. Transcription factors are capable of regulating multiple genes over potentially long distances making target gene identification challenging. Currently available experimental approaches to detect distal interactions have multiple weaknesses that have motivated the development of computational approaches. Although an improvement over experimental approaches, existing computational approaches are still limited in their application, with different weaknesses depending on the approach. Here, we review computational approaches with a focus on data dependency, cell type specificity and usability. With the aim of identifying transcription factor target genes, we apply available approaches to typical transcription factor experimental datasets. We show that approaches are not always capable of annotating all transcription factor binding sites; binding sites should be treated disparately; and a combination of approaches can increase the biological relevance of the set of genes identified as targets.
转录因子通过维持增殖状态、驱动细胞分化和决定细胞命运，在基因表达调控和发育中发挥着重要作用。转录因子能够在潜在的长距离上调节多个基因，这使得靶基因的识别具有挑战性。目前用于检测远程相互作用的实验方法存在多种缺陷，这促使人们开发了计算方法。尽管这些计算方法优于实验方法，但它们在应用方面仍然存在局限性，不同的方法也有不同的弱点。在这里，我们重点讨论了计算方法的数据依赖性、细胞类型特异性和可用性。为了鉴定转录因子的靶基因，我们将现有的方法应用于典型的转录因子实验数据集。我们表明，方法并不总是能够注释所有的转录因子结合位点；应该分别处理结合位点；并且结合使用多种方法可以提高作为靶基因鉴定的基因集的生物学相关性。
