Identification of coupling DNA motif pairs on long-range chromatin interactions in human K562 cells

Type
Article

Authors
Wong, Ka-Chun
Li, Yue
Peng, Chengbin

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Online Publication Date
2015-09-26

Print Publication Date
2016-02-01

Date
2015-09-26

Abstract
Motivation: The protein-DNA interactions between transcription factors (TFs) and transcription factor binding sites (TFBSs, also known as DNA motifs) are critical activities in gene transcription. The identification of the DNA motifs is a vital task for downstream analysis. Unfortunately, the long-range coupling information between different DNA motifs is still lacking. To fill the void, as the first-of-its-kind study, we have identified the coupling DNA motif pairs on long-range chromatin interactions in human. Results: The coupling DNA motif pairs exhibit substantially higher DNase accessibility than the background sequences. Half of the DNA motifs involved are matched to the existing motif databases, although nearly all of them are enriched with at least one gene ontology term. Their motif instances are also found statistically enriched on the promoter and enhancer regions. Especially, we introduce a novel measurement called motif pairing multiplicity which is defined as the number of motifs that are paired with a given motif on chromatin interactions. Interestingly, we observe that motif pairing multiplicity is linked to several characteristics such as regulatory region type, motif sequence degeneracy, DNase accessibility and pairing genomic distance. Taken into account together, we believe the coupling DNA motif pairs identified in this study can shed lights on the gene transcription mechanism under long-range chromatin interactions. © The Author 2015. Published by Oxford University Press.

Citation
Wong K-C, Li Y, Peng C (2015) Identification of coupling DNA motif pairs on long-range chromatin interactions in human K562 cells. Bioinformatics 32: 321–324. Available: http://dx.doi.org/10.1093/bioinformatics/btv555.

Acknowledgements
The work described in this article was substantially supported by a grant from City University of Hong Kong (Project No 7200444/CS).

Publisher
Oxford University Press (OUP)

Journal
Bioinformatics

DOI
10.1093/bioinformatics/btv555

PubMed ID
26411866

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