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MeSH-informed enrichment analysis and MeSH-guided semantic similarity among functional terms and gene products in chicken

Gota Morota, Timothy M. Beisinger, Francisco Peñagaricano
G3·GenesGenomesGenetics 2016 v.6 no.8 pp. 2447-2453
chickens, data collection, epistasis, gene expression, gene expression regulation, genes, genomics
Biomedical vocabularies and ontologies aid in recapitulating biological knowledge. The annotation of gene products is mainly accelerated by Gene Ontology (GO) and more recently by Medical Subject Headings (MeSH). MeSH is the National Library of Medicine's controlled vocabulary and it is making inroads into gene annotation and enrichment analysis. The availability of MeSH annotation in farm animals poses both new challenges and opportunities for downstream analysis in functional genomic studies. Here we report the MeSH annotation of the chicken genome and we illustrate some features of different MeSH-based analyses, including MeSH-informed enrichment analysis and MeSH-guided semantic similarity among terms and gene products, using two lists of chicken genes available in public repositories. The two published datasets that were employed represent (i) differentially expressed genes detected in a genome-wide gene expression study and (ii) candidate genes under selective sweep or epistatic selection that were detected in a whole-genome scan study. The comparison of MeSH with GO overrepresentation analyses suggested not only that MeSH supports the findings obtained from GO analysis but also that MeSH is able to further enrich the representation of biological knowledge. Based on the hierarchical structures of MeSH and GO, we computed semantic similarities among vocabularies as well as semantic similarities among selected genes. The respective hierarchies of MeSH and GO yielded the similarity levels between significant functional terms, and the annotation of each term yielded then the measures of gene similarity. Our findings show the benefits of using MeSH as an alternative choice of annotation in order to draw biological inferences from a list of genes of interest. We argue that the use of MeSH in conjunction with GO will be instrumental in facilitating the understanding of the genetic basis of complex traits.