PT - JOURNAL ARTICLE AU - Marc Griesemer AU - Jeffrey Kimbrel AU - Carol Zhou AU - Ali Navid AU - Patrik D'haeseleer TI - Combining multiple functional annotation tools increases completeness of metabolic annotation AID - 10.1101/160887 DP - 2017 Jan 01 TA - bioRxiv PG - 160887 4099 - http://biorxiv.org/content/early/2017/07/18/160887.short 4100 - http://biorxiv.org/content/early/2017/07/18/160887.full AB - The dirty little secret behind genome-scale systems biology modeling efforts is that they are invariably based on very incomplete functional annotations. Annotated genomes typically contain 30-50% of genes with little or no functional annotation, severely limiting our knowledge of the parts lists that the organisms have at their disposal. In metabolic modeling, these incomplete annotations are often sufficient to derive a reasonably complete model of the core metabolism at least, typically consisting of well-studied (and thus well-annotated) metabolic pathways that are sufficient for growth in pure culture. However secondary metabolic pathways or pathways that are important for growth on unusual metabolites exchanged in complex microbial communities are often much less well understood, resulting in missing or lower confidence functional annotations in newly sequenced genomes. Here, we present preliminary results on a comprehensive reannotation of 27 bacterial Tier 1 and Tier 2 reference genomes from BioCyc, 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.