Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, Victoria, Australia.
Department of Molecular Translational Science, Monash University, Clayton, Victoria, Australia.
PLoS One. 2019 Sep 4;14(9):e0215495. doi: 10.1371/journal.pone.0215495. eCollection 2019.
The availability of large amounts of high-throughput genomic, transcriptomic and epigenomic data has provided opportunity to understand regulation of the cellular transcriptome with an unprecedented level of detail. As a result, research has advanced from identifying gene expression patterns associated with particular conditions to elucidating signalling pathways that regulate expression. There are over 1,000 transcription factors (TFs) in vertebrates that play a role in this regulation. Determining which of these are likely to be controlling a set of genes can be assisted by computational prediction, utilising experimentally verified binding site motifs. Here we present CiiiDER, an integrated computational toolkit for transcription factor binding analysis, written in the Java programming language, to make it independent of computer operating system. It is operated through an intuitive graphical user interface with interactive, high-quality visual outputs, making it accessible to all researchers. CiiiDER predicts transcription factor binding sites (TFBSs) across regulatory regions of interest, such as promoters and enhancers derived from any species. It can perform an enrichment analysis to identify TFs that are significantly over- or under-represented in comparison to a bespoke background set and thereby elucidate pathways regulating sets of genes of pathophysiological importance.
大量高通量基因组、转录组和表观基因组数据的可用性为我们提供了前所未有的机会来理解细胞转录组的调控。因此，研究已经从识别与特定条件相关的基因表达模式推进到阐明调控表达的信号通路。脊椎动物中有超过 1000 种转录因子（TFs）在这种调控中发挥作用。通过利用实验验证的结合位点基序进行计算预测，可以帮助确定哪些转录因子可能控制一组基因。这里我们展示了 CiiiDER，这是一个用于转录因子结合分析的集成计算工具包，用 Java 编程语言编写，使其独立于计算机操作系统。它通过直观的图形用户界面进行操作，具有交互式、高质量的可视化输出，使所有研究人员都能够使用它。CiiiDER 可以预测来自任何物种的感兴趣的调控区域（如启动子和增强子）中的转录因子结合位点（TFBS）。它可以进行富集分析，以识别与特定背景集相比显著过表达或低表达的 TF，从而阐明调控与病理生理重要性相关的基因集的通路。
