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Medical subject heading (MeSH) annotations illuminate maize genetics and evolution

By Timothy Mathes Beissinger, Gota Morota

Posted 12 Apr 2016
bioRxiv DOI: 10.1101/048132 (published 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 (GO) analysis, a powerful tool that involves evaluating publicly available gene annotations. However, additional tools such as Medical Subject Headings (MeSH terms) 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.

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