Medical Subject Heading (MeSH) annotations illuminate maize genetics and evolution

• Published in: Plant methods Vol. 13; no. 1; p. 8
• Main Authors: Beissinger, Timothy M., Morota, Gota
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.02.2017; BioMed Central
• Subjects: Analysis; Cereal crops; Corn; data collection; DNA sequencing; evolution; gene ontology; genes; Genetic aspects; Genetics; Genotypes; Methodology; National Library of Medicine; Ontology; phenotype; Phenotypes; Plant species; sequence analysis; statistical analysis; Statistical methods; Zea mays; Overrepresentation analysis (ORA); Inflorescence; Domestication; MeSH; Seed size; Maize; Ear number; Gene ontology (GO)
• ISSN: 1746-4811; 1746-4811
• DOI: 10.1186/s13007-017-0159-5

High-density marker panels and/or whole-genome sequencing, coupled with advanced phenotyping pipelines and sophisticated statistical methods, have dramatically increased our ability to generate lists of candidate genes or regions that are putatively associated with phenotypes or processes of interest. However, the speed with which we can validate genes, or even make reasonable biological interpretations about the principles underlying them, has not kept pace. A promising approach that runs parallel to explicitly validating individual genes is analyzing a set of genes together and assessing the biological similarities among them. This is often achieved via gene ontology analysis, a powerful tool that involves evaluating publicly available gene annotations. However, additional resources such as Medical Subject Headings (MeSH) can also be used to evaluate sets of genes to make biological interpretations. In this manuscript, we describe utilizing MeSH terms to make biological interpretations in maize. MeSH terms are assigned to PubMed-indexed manuscripts by the National Library of Medicine, and can be directly mapped to genes to develop gene annotations. Once mapped, these terms can be evaluated for enrichment in sets of genes or similarity between gene sets to provide biological insights. Here, we implement MeSH analyses in five maize datasets to demonstrate how MeSH can be leveraged by the maize and broader crop-genomics community. We demonstrate that MeSH terms can be effectively leveraged to generate hypotheses and make biological interpretations in maize, and we provide a pipeline that enables the use of MeSH terms in other plant species.