Combining multiple functional annotation tools increases coverage of metabolic annotation

Abstract

BackgroundGenome-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 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.ResultsHere, 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.ConclusionsMetabolic 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 non-core pathways.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5221-9) contains supplementary material, which is available to authorized users.

Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines

“…In this study, we sought to assess the impact of selected annotation pipelines on draft metabolic network reconstructions and downstream analyses of metabolic complementarity. Our results highlight significant differences between the output of the five tested standard pipelines at all examined levels, from the prediction of coding sequences to the selection of microbial communities, especially for EC annotations and hypothetical proteins, with levels of variability similar to those previously reported by Griesemer et al (2018) . Overall, the number of reactions predicted in the final network mirrored the number of EC numbers predicted, with Prokka and IMG yielding both the highest number of EC annotations and reactions.…”

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

“…No single approach is a panacea. Rather, it has been demonstrated numerous times that optimal results in bioinformatics are obtained by combining many different approaches and data sources together to obtain a consensus result ( 71 ). One of the biggest impediments to building such a consensus approach for biology today is the lack of a single standard ontology for describing gene functions.…”

Nutrition or nature: disentangling the complex forces shaping prokaryote pan-genomes

“…Diverse evolutionary groups have evolved specific sets of metabolic reactions to obtain their required energy and biomass. On average, more than 50% of the genes in microbial genomes code for metabolic functions (Griesemer et al, 2018) and metabolic genes are often found to be horizontally transferred (Goyal, 2018). The different sets of metabolic reactions used in different contexts by microbes reflect patterns and mechanisms of their genome evolution.…”