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

Abstract
In mammalian cells, transcribed enhancers (TrEns) play important roles in the initiation of gene expression and maintenance of gene expression levels in a spatiotemporal manner. One of the most challenging questions is how the genomic characteristics of enhancers relate to enhancer activities. To date, only a limited number of enhancer sequence characteristics have been investigated, leaving space for exploring the enhancers’ DNA code in a more systematic way. To address this problem, we developed a novel computational framework, Transcribed Enhancer Landscape Search (TELS), aimed at identifying predictive cell type/tissue-specific motif signatures of TrEns. As a case study, we used TELS to compile a comprehensive catalogue of motif signatures for all known TrEns identified by the FANTOM5 consortium across 112 human primary cells and tissues. Our results confirm that combinations of different short motifs characterize in an optimized manner cell type/tissue-specific TrEns. Our study is the first to report combinations of motifs that maximize classification performance of TrEns exclusively transcribed in one cell type/tissue from TrEns exclusively transcribed in different cell types/tissues. Moreover, we also report 31 motif signatures predictive of enhancers’ broad activity. TELS codes and material are publicly available at http://www.cbrc.kaust.edu.sa/TELS.

Citation
Kleftogiannis D, Ashoor H, Bajic VB (2018) TELS: A Novel Computational Framework for Identifying Motif Signatures of Transcribed Enhancers. Genomics, Proteomics & Bioinformatics. Available: http://dx.doi.org/10.1016/j.gpb.2018.05.003.

Acknowledgements
The authors would like to thank Nikolaos Zarokanellos for his help on the experimentation with MATLAB. This study was supported by the base funding (Grant No. BAS/1/1606-01-01) to VBB by the King Abdullah University of Science and Technology (KAUST), Saudi Aribia.

Publisher
Elsevier BV

Journal
Genomics, Proteomics & Bioinformatics

DOI
10.1016/j.gpb.2018.05.003

Additional Links
https://www.sciencedirect.com/science/article/pii/S1672022918304340

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