Journal
NUCLEIC ACIDS RESEARCH
Volume 45, Issue 18, Pages 10415-10427Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkx751
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Funding
- Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant
- Canadian Institutes of Health Research
- Fonds de recherche du Quebec-Sante
- Vanier Canada graduate scholarship from NSERC
- University of Ottawa Start-up Funds
- Bell-Bombardier Research Chair of Excellence
- NSERC Discovery Grant
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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 sequencemotifs. Specifically, we introduce Local Enrichment of Sequence Motifs in biological Networks (LESMoN), an enumerativemotif 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.
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