ArticleBMC genomics2018
Combining multiple functional annotation tools increases coverage of metabolic annotation.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
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- Chromosome-level assembly and phylophenetic insights of Cladosporium oxysporum A3.I.1, a fungus with the ability to degrade polyurethane polymers.G3 (Bethesda, Md.) · 2026Article
- Comparative Genomic and Functional Profiling of ECM-Targeting Enzymes inbioRxiv : the preprint server for biology · 2026Article
- Functional Annotation Workflow for Fungal Transcriptomes.Journal of fungi (Basel, Switzerland) · 2026Article
- Comprehensive annotation of the enzymes of Drosophila melanogaster.G3 (Bethesda, Md.) · 2026Article
- Microbiome-Driven Mechanisms in Breast Cancer: Emerging Evidence From Gut Microbial Signatures to Therapeutic Response.BioMed research international · 2026Review
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- MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques.Environmental microbiome · 2024Article
- Transporter annotations are holding up progress in metabolic modeling.Frontiers in systems biology · 2024Article
- Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites.Microorganisms · 2023Review
- kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase.Bioinformatics (Oxford, England) · 2023Article
- Multi-scale models of whole cells: progress and challenges.Frontiers in cell and developmental biology · 2023Review
- An informatic workflow for the enhanced annotation of excretory/secretory proteins ofComputational and structural biotechnology journal · 2023Article
- KinMod database: a tool for investigating metabolic regulation.Database : the journal of biological databases and curation · 2022Article
- Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation.PLoS computational biology · 2022Article
- Nutrition or nature: using elementary flux modes to disentangle the complex forces shaping prokaryote pan-genomes.BMC ecology and evolution · 2022Article
- Identification of novel prostate cancer genes in patients stratified by Gleason classification: Role of antitumoral genes.International journal of cancer · 2022Article
- Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach.Frontiers in artificial intelligence · 2022Article
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5 authors.
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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.
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