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Nucleic Acids Res. 2017 Oct 13;45(18):10415-10427. doi: 10.1093/nar/gkx751.

Functional 5' UTR motif discovery with LESMoN: Local Enrichment of Sequence Motifs in biological Networks.

Author information

1
McGill Centre for Bioinformatics and School of Computer Science, McGill University, Montréal, Québec H3A 0E9, Canada.
2
Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario K1H 8M5, Canada.
3
Translational Proteomics Laboratory, Institut de recherches cliniques de Montréal, Montréal, Québec H2W 1R7, Canada.
4
Département de biochimie et médecine moléculaire, Université de Montréal, Montréal, Québec H3C 3J7, Canada.

Abstract

Biological networks are rich representations of the relationships between entities such as genes or proteins and have become increasingly complete thanks to various high-throughput network mapping experimental approaches. Here, we propose a method to use such networks to guide the search for functional sequence motifs. Specifically, we introduce Local Enrichment of Sequence Motifs in biological Networks (LESMoN), an enumerative motif discovery algorithm that identifies 5' untranslated region (UTR) sequence motifs whose associated proteins form unexpectedly dense clusters in a given biological network. When applied to the human protein-protein interaction network from BioGRID, LESMoN identifies several highly significant 5' UTR sequence motifs, including both previously known motifs and uncharacterized ones. The vast majority of these motifs are evolutionary conserved and the genes containing them are significantly enriched for various gene ontology terms suggesting new associations between 5' UTR motifs and a number of biological processes. We validate in vivo the role in protein expression regulation of three motifs identified by LESMoN.

PMID:
28977652
PMCID:
PMC5737372
DOI:
10.1093/nar/gkx751
[Indexed for MEDLINE]
Free PMC Article

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