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

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 functio...

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Detalles Bibliográficos
Autores principales: Lavallée-Adam, Mathieu, Cloutier, Philippe, Coulombe, Benoit, Blanchette, Mathieu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737372/
https://www.ncbi.nlm.nih.gov/pubmed/28977652
http://dx.doi.org/10.1093/nar/gkx751
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author Lavallée-Adam, Mathieu
Cloutier, Philippe
Coulombe, Benoit
Blanchette, Mathieu
author_facet Lavallée-Adam, Mathieu
Cloutier, Philippe
Coulombe, Benoit
Blanchette, Mathieu
author_sort Lavallée-Adam, Mathieu
collection PubMed
description 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.
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spelling pubmed-57373722018-01-08 Functional 5′ UTR motif discovery with LESMoN: Local Enrichment of Sequence Motifs in biological Networks Lavallée-Adam, Mathieu Cloutier, Philippe Coulombe, Benoit Blanchette, Mathieu Nucleic Acids Res Computational Biology 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. Oxford University Press 2017-10-13 2017-08-31 /pmc/articles/PMC5737372/ /pubmed/28977652 http://dx.doi.org/10.1093/nar/gkx751 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Lavallée-Adam, Mathieu
Cloutier, Philippe
Coulombe, Benoit
Blanchette, Mathieu
Functional 5′ UTR motif discovery with LESMoN: Local Enrichment of Sequence Motifs in biological Networks
title Functional 5′ UTR motif discovery with LESMoN: Local Enrichment of Sequence Motifs in biological Networks
title_full Functional 5′ UTR motif discovery with LESMoN: Local Enrichment of Sequence Motifs in biological Networks
title_fullStr Functional 5′ UTR motif discovery with LESMoN: Local Enrichment of Sequence Motifs in biological Networks
title_full_unstemmed Functional 5′ UTR motif discovery with LESMoN: Local Enrichment of Sequence Motifs in biological Networks
title_short Functional 5′ UTR motif discovery with LESMoN: Local Enrichment of Sequence Motifs in biological Networks
title_sort functional 5′ utr motif discovery with lesmon: local enrichment of sequence motifs in biological networks
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737372/
https://www.ncbi.nlm.nih.gov/pubmed/28977652
http://dx.doi.org/10.1093/nar/gkx751
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