PT - JOURNAL ARTICLE AU - Timothy M. Beissinger AU - Gota Morota TI - Medical subject heading (MeSH) annotations illuminate maize genetics and evolution AID - 10.1101/048132 DP - 2016 Jan 01 TA - bioRxiv PG - 048132 4099 - http://biorxiv.org/content/early/2016/04/13/048132.short 4100 - http://biorxiv.org/content/early/2016/04/13/048132.full AB - In the modern era, 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 at 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 (GO) analysis, a powerful tool that involves evaluating publicly available gene annotations. However, GO has limitations including its automated nature and sometimes-difficult interpretability. Here, we describe using Medical Subject Headings (MeSH terms) as an alternative tool for evaluating sets of genes to make biological interpretations and to generate hypotheses. MeSH terms are assigned to PubMed-indexed manuscripts by the National Library of Medicine, and can be mapped to directly 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.