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Nat Commun. 2015 Oct 13;6:8493. doi: 10.1038/ncomms9493.

Harnessing the landscape of microbial culture media to predict new organism-media pairings.

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Blavatnik School of Computer Sciences and Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
Center for Bioinformatics and Computational Biology (CBCB), Department of Computer Science, and University of Maryland, Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, Maryland 20742, USA.
Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig 38124, Germany.
School of Biology, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.


Culturing microorganisms is a critical step in understanding and utilizing microbial life. Here we map the landscape of existing culture media by extracting natural-language media recipes into a Known Media Database (KOMODO), which includes >18,000 strain-media combinations, >3300 media variants and compound concentrations (the entire collection of the Leibniz Institute DSMZ repository). Using KOMODO, we show that although media are usually tuned for individual strains using biologically common salts, trace metals and vitamins/cofactors are the most differentiating components between defined media of strains within a genus. We leverage KOMODO to predict new organism-media pairings using a transitivity property (74% growth in new in vitro experiments) and a phylogeny-based collaborative filtering tool (83% growth in new in vitro experiments and stronger growth on predicted well-scored versus poorly scored media). These resources are integrated into a web-based platform that predicts media given an organism's 16S rDNA sequence, facilitating future cultivation efforts.

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