Kleftogiannis Dimitrios, Kalnis Panos, Arner Erik, Bajic Vladimir B
Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
The Institute of Cancer Research (ICR), London, SW7 3RP, UK.
Nucleic Acids Res. 2017 Feb 28;45(4):e25. doi: 10.1093/nar/gkw1015.
Promoters and enhancers regulate the initiation of gene expression and maintenance of expression levels in spatial and temporal manner. Recent findings stemming from the Cap Analysis of Gene Expression (CAGE) demonstrate that promoters and enhancers, based on their expression profiles after stimulus, belong to different transcription response subclasses. One of the most promising biological features that might explain the difference in transcriptional response between subclasses is the local chromatin environment. We introduce a novel computational framework, PEDAL, for distinguishing effectively transcriptional profiles of promoters and enhancers using solely histone modification marks, chromatin accessibility and binding sites of transcription factors and co-activators. A case study on data from MCF-7 cell-line reveals that PEDAL can identify successfully the transcription response subclasses of promoters and enhancers from two different stimulations. Moreover, we report subsets of input markers that discriminate with minimized classification error MCF-7 promoter and enhancer transcription response subclasses. Our work provides a general computational approach for identifying effectively cell-specific and stimulation-specific promoter and enhancer transcriptional profiles, and thus, contributes to improve our understanding of transcriptional activation in human.
启动子和增强子以空间和时间方式调节基因表达的起始和表达水平的维持。基因表达帽分析(CAGE)的最新研究结果表明,启动子和增强子根据其刺激后的表达谱,属于不同的转录反应亚类。可能解释亚类之间转录反应差异的最有前景的生物学特征之一是局部染色质环境。我们引入了一种新颖的计算框架PEDAL,仅使用组蛋白修饰标记、染色质可及性以及转录因子和共激活因子的结合位点来有效区分启动子和增强子的转录谱。对MCF-7细胞系数据的案例研究表明,PEDAL可以成功识别来自两种不同刺激的启动子和增强子的转录反应亚类。此外,我们报告了以最小分类误差区分MCF-7启动子和增强子转录反应亚类的输入标记子集。我们的工作提供了一种通用的计算方法,用于有效识别细胞特异性和刺激特异性的启动子和增强子转录谱,从而有助于增进我们对人类转录激活的理解。