Berkeley Lab

MAGI: A Method for Metabolite Annotation and Gene Integration

MAGI provides a fundamentally different approach for directly linking novel sequences to their biochemical functions and products

Erbilgin, Onur, Oliver Rübel, Katherine B. Louie, Matthew Trinh, Markus de Raad, Tony Wildish, Daniel Udwary, Cindi Hoover, Samuel Deutsch, Trent R. Northen, and Benjamin P. Bowen. 2019. ACS Chemical Biology 14 (4). American Chemical Society: 704–14. doi:10.1021/acschembio.8b01107.10.1021/acschembio.8b01107

MAGI was built to make connecting metabolomics data with genes easier for researchers. Metagenomics and single-cell sequencing have enabled glimpses into the metabolic potential of Earth’s biological systems. Yet, for the most part we can’t accurately predict or identify the products of most biosynthetic pathways. Most of what we know of microbial biochemistry is based on characterization of a few model microorganisms, and these findings have been extended through sequence correlations and connecting metabolomics observations with genomic predictions is crucial to overcome the limitations of each and to strengthen the biological conclusions made by both.

MAGI will be used in support of the Environmental Ark campaign. Specifically, it will enable us to link isolate genomes to metabolites to enable high throughput functional gene annotations. MAGI will also be used in the Environmental Simulations Campaign to integrate metabolomics and genomics data to design synthetic communities. Finally, we are using MAGI in collaboration with the Arkin lab to design synthetic communities.