Taxonomic weighting improves the accuracy of a gap-filling algorithm for metabolic models.

ABSTRACT:

Motivation

The increasing availability of annotated genome sequences enables construction of genome-scale metabolic networks, which are useful tools for studying organisms of interest. However, due to incomplete genome annotations, draft metabolic models contain gaps that must be filled in a time-consuming process before they are usable. Optimization-based algorithms that fill these gaps have been developed, however, gap-filling algorithms show significant error rates and often introduce incorrect reactions.

Results

Here, we present a new gap-filling method that computes the costs of candidate gap-filling reactions from a universal reaction database (MetaCyc) based on taxonomic information. When gap-filling a metabolic model for an organism M (such as Escherichia coli), the

SUBMITTER: Ong WK 

PROVIDER: S-EPMC7523652 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature