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

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

Nat Commun

Blavatnik School of Computer Sciences and Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.

Published: October 2015

AI Article Synopsis

Article Abstract

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4633754PMC
http://dx.doi.org/10.1038/ncomms9493DOI Listing

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