Discriminative identification of transcriptional responses of promoters and enhancers after stimulus

Abstract

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.

TELS: A Novel Computational Framework for Identifying Motif Signatures of Transcribed Enhancers

“…In addition, applying the same analysis to CAGE-defined promoters from FANTOM5 will answer equally important questions about promoters’ sequence characteristics ‘encrypted' within their genomic sequence. Lastly, we would like to point out that stratifying TrEn data by their expression levels similarly to the data reported by Arner et al [9] and our laboratory [10], and inferring the expression levels of TrEns using sequence characteristics, may complement the findings presented in this study.…”

“…Enhancers’ transcription produces enhancer-derived RNAs (eRNAs), a class of non-coding RNAs whose functions are unclear [6], [7]. It is interesting to note that it may be difficult to clearly separate enhancers from promoters, based on the transcriptional activation similarity, since both categories of DNA regulatory regions act as promoters but generate different classes of transcripts [8], [9], [10].…”

Comprehensive epigenetic analyses reveal master regulators driving lung metastasis of breast cancer

“…7 In addition, novel drivers of hepatocellular carcinoma were recently identified by integrating epigenetic marks with transcription data. 8 Although many previous studies had explored the whole-genome histone modification profiles of non-metastatic breast cancer subtype, 9,10 the comprehensive analyses of epigenome in metastatic breast cancer cells were barely reported. Most current studies about breast cancer metastasis focused on the epigenetic alteration of single gene, 11,12 the holistic epigenome perturbation still remains unclear.…”

Deciphering the Gene Regulatory Landscape Encoded in DNA Biophysical Features

“…Several laboratories have attempted to employ computational approaches to predict enhancers based on sequence information (Kleftogiannis et al, 2016;Lee et al, 2011;Rusk, 2014). Although these methods were able to predict enhancers to a certain degree, they were unable to decipher the underlying code that drives enhancer selection and strength (Pennacchio et al, 2013).…”