Evidence map›Paper›PMID 28977652›Full record

ArticleNucleic acids research2017

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

Mathieu Lavallée-Adam, Philippe Cloutier, Benoit Coulombe, Mathieu Blanchette

Abstract readValidation Study
In one paragraph

Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mathieu Lavallée-AdamMcGill Centre for Bioinformatics and School of Computer Science, McGill University, Montréal, Québec H3A 0E9, Canada.
Philippe CloutierTranslational Proteomics Laboratory, Institut de recherches cliniques de Montréal, Montréal, Québec H2W 1R7, Canada.
Benoit CoulombeTranslational Proteomics Laboratory, Institut de recherches cliniques de Montréal, Montréal, Québec H2W 1R7, Canada.
Mathieu BlanchetteMcGill Centre for Bioinformatics and School of Computer Science, McGill University, Montréal, Québec H3A 0E9, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AlgorithmsGene Expression RegulationGene Regulatory NetworksRegulatory Elements, Transcriptional5' Untranslated RegionsBinding SitesComputational BiologyGene OntologyGenetic Association StudiesHumansMutationProtein Interaction MapsTranscription Factors5' Untranslated RegionsTranscription Factors

Identifiers

PMID28977652
PMCPMC5737372

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.