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An application of Me SH enrichment analysis in livestock
Author(s) -
Morota G.,
Peñagaricano F.,
Petersen J. L.,
Ciobanu D. C.,
Tsuyuzaki K.,
Nikaido I.
Publication year - 2015
Publication title -
animal genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.756
H-Index - 81
eISSN - 1365-2052
pISSN - 0268-9146
DOI - 10.1111/age.12307
Subject(s) - identification (biology) , biology , computational biology , annotation , gene ontology , gene , selection (genetic algorithm) , vocabulary , functional genomics , genomics , genome , genetics , computer science , artificial intelligence , linguistics , gene expression , botany , philosophy
Summary An integral part of functional genomics studies is to assess the enrichment of specific biological terms in lists of genes found to be playing an important role in biological phenomena. Contrasting the observed frequency of annotated terms with those of the background is at the core of overrepresentation analysis ( ORA ). Gene Ontology ( GO ) is a means to consistently classify and annotate gene products and has become a mainstay in ORA . Alternatively, Medical Subject Headings (Me SH ) offers a comprehensive life science vocabulary including additional categories that are not covered by GO . Although Me SH is applied predominantly in human and model organism research, its full potential in livestock genetics is yet to be explored. In this study, Me SH ORA was evaluated to discern biological properties of identified genes and contrast them with the results obtained from GO enrichment analysis. Three published datasets were employed for this purpose, representing a gene expression study in dairy cattle, the use of SNP s for genome‐wide prediction in swine and the identification of genomic regions targeted by selection in horses. We found that several overrepresented Me SH annotations linked to these gene sets share similar concepts with those of GO terms. Moreover, Me SH yielded unique annotations, which are not directly provided by GO terms, suggesting that Me SH has the potential to refine and enrich the representation of biological knowledge. We demonstrated that Me SH can be regarded as another choice of annotation to draw biological inferences from genes identified via experimental analyses. When used in combination with GO terms, our results indicate that Me SH can enhance our functional interpretations for specific biological conditions or the genetic basis of complex traits in livestock species.