gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models.

Authors:

Johannes Zimmermann, Christoph Kaleta, Silvio Waschina

Year of publication:

2021

Volume:

22

Issue:

1

ISSN:

1474-7596

Journal (long):

Genome biology

Journal (short):

GENOME BIOL

Impact factor:

13.583

Abstract:

Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism’s genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

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