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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4srzxifvfrdlhjhg3dimznkp7m" style="color: black;">BMC Genomics</a>
Genome-scale metabolic modeling is a cornerstone of systems biology analysis of microbial organisms and communities, yet these genome-scale modeling efforts are invariably based on incomplete functional annotations. Annotated genomes typically contain 30-50% of genes without functional annotation, severely limiting our knowledge of the "parts lists" that the organisms have at their disposal. These incomplete annotations may be sufficient to derive a model of a core set of well-studied metabolic<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12864-018-5221-9">doi:10.1186/s12864-018-5221-9</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/trsiwwlqtff3lgspipoq4eucay">fatcat:trsiwwlqtff3lgspipoq4eucay</a> </span>
more »... pathways that support growth in pure culture. However, pathways important for growth on unusual metabolites exchanged in complex microbial communities are often less understood, resulting in missing functional annotations in newly sequenced genomes. Results: Here, we present results on a comprehensive reannotation of 27 bacterial reference genomes, focusing on enzymes with EC numbers annotated by KEGG, RAST, EFICAz, and the BRENDA enzyme database, and on membrane transport annotations by TransportDB, KEGG and RAST. Our analysis shows that annotation using multiple tools can result in a drastically larger metabolic network reconstruction, adding on average 40% more EC numbers, 3-8 times more substrate-specific transporters, and 37% more metabolic genes. These results are even more pronounced for bacterial species that are phylogenetically distant from well-studied model organisms such as E. coli. Conclusions: Metabolic annotations are often incomplete and inconsistent. Combining multiple functional annotation tools can greatly improve genome coverage and metabolic network size, especially for non-model organisms and noncore pathways.
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