Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.16.384834v1?rss=1 Authors: Verschaffelt, P., Van Den Bossche, T., Gabriel, W., Burdukiewicz, M., Soggiu, A., Martens, L., Renard, B. Y., Schiebenhoefer, H., Mesuere, B. Abstract: The study of microbiomes has gained in importance over the past few years, and has led to the fields of metagenomics, metatranscriptomics and metaproteomics. While initially focused on the study of biodiversity within these communities the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute functional similarity between two data sets. MegaGO is highly performant: each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license, and is available at https://github.com/MEGA-GO/. Copy rights belong to original authors. Visit the link for more info