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PLoS Comput Biol. 2014 Oct 16;10(10):e1003882. doi: 10.1371/journal.pcbi.1003882. eCollection 2014 Oct.

Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models.

Author information

1
Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
2
Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America.
3
Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, Illinois, United States of America.
4
Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America; Department of Surgery, Mayo Clinic, Rochester, Minnesota, United States of America; Department of Physiology and Bioengineering, Mayo Clinic, Rochester, Minnesota, United States of America.
5
Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America; Institute for Systems Biology, Seattle, Washington, United States of America.

Abstract

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.

PMID:
25329157
PMCID:
PMC4199484
DOI:
10.1371/journal.pcbi.1003882
[Indexed for MEDLINE]
Free PMC Article

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