TELS: A Novel Computational Framework for Identifying Motif Signatures of Transcribed Enhancers.
10.1016/j.gpb.2018.05.003
- Author:
Dimitrios KLEFTOGIANNIS
1
;
Haitham ASHOOR
2
;
Vladimir B BAJIC
3
Author Information
1. Centre for Evolution and Cancer, Division of Molecular Pathology, The Institute of Cancer Research (ICR), London SM2 5NG, United Kingdom.
2. The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
3. Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia. Electronic address: vladimir.bajic@kaust.edu.sa.
- Publication Type:Journal Article
- Keywords:
Machine learning;
Motif identification;
Sequence analysis;
Transcribed enhancer;
Transcription regulation
- MeSH:
Enhancer Elements, Genetic;
Genomics;
methods;
Humans;
Nucleotide Motifs;
Sequence Analysis, DNA;
methods;
Transcription, Genetic
- From:
Genomics, Proteomics & Bioinformatics
2018;16(5):332-341
- CountryChina
- Language:English
-
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 catalog 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.
