ArticleComputational and structural biotechnology journal2022
KEMET - A python tool for KEGG Module evaluation and microbial genome annotation expansion.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- Adaptive Evolution Reveals Metabolic Plasticity and Functional Redundancy in an Anaerobic Microbiome under Extreme Ammonia Stress.Environmental science & technology · 2026Article
- Obligate cross-feeding of metabolites is common in soil microbial communities.Nature microbiology · 2026Article
- Machine Learning Reveals Quantitative Amino Acid Preferences in Bifidobacterium longum Growth.Microbial biotechnology · 2026Article
- AliFilter: a machine learning approach to alignment filtering.Molecular biology and evolution · 2026Article
- Metagenomic insights into microbial communities of terrestrial mud volcanos: functional diversity of subsurface archaea and bacteria.Frontiers in microbiology · 2026Article
- Microbes with higher metabolic independence are enriched in human gut microbiomes under stress.eLife · 2025Article
- Sustainable Food Waste Management in Anaerobic Digesters: Prediction of the Organic Load Impact by Metagenome-Scale Metabolic Modeling.Environmental science & technology · 2025Article
- Importance, structure, cultivability, and resilience of the bacterial microbiota during infection of laboratory-grown Haematococcus spp. by the blastocladialean pathogen Paraphysoderma sedebokerense: evidence for a domesticated microbiota and its potential for biocontrol.FEMS microbiology ecology · 2025Article
- Core cooperative metabolism in low-complexity CO2-fixing anaerobic microbiota.The ISME journal · 2025Article
- pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes.Genome biology · 2024Article
- MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques.Environmental microbiome · 2024Article
- The Metabolism ofMicroorganisms · 2024Article
- Article
- A unified compendium of prokaryotic and viral genomes from over 300 anaerobic digestion microbiomes.Environmental microbiome · 2024Article
- A metagenomic approach to demystify the anaerobic digestion black box and achieve higher biogas yield: a review.Frontiers in microbiology · 2024Review
- Article
- Uncovering Microbiome Adaptations in a Full-Scale Biogas Plant: Insights from MAG-Centric Metagenomics and Metaproteomics.Microorganisms · 2023Article
- Article
- Comparative genomic insights into habitat adaptation of coral-associatedFrontiers in microbiology · 2023Article
- Analysis of the anaerobic digestion metagenome under environmental stresses stimulating prophage induction.Microbiome · 2022Article
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Authors and funding
7 authors.
Funding
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Abstract
Background: The rapid accumulation of sequencing data from metagenomic studies is enabling the generation of huge collections of microbial genomes, with new challenges for mapping their functional potential. In particular, metagenome-assembled genomes are typically incomplete and harbor partial gene sequences that can limit their annotation from traditional tools. New scalable solutions are thus needed to facilitate the evaluation of functional potential in microbial genomes. Methods: To resolve annotation gaps in microbial genomes, we developed KEMET, an open-source Python library devised for the analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) functional units. KEMET focuses on the in-depth analysis of metabolic reaction networks to identify missing orthologs through hidden Markov model profiles. Results: We evaluate the potential of KEMET for expanding functional annotations by simulating the effect of assembly issues on real gene sequences and showing that our approach can identify missing KEGG orthologs. Additionally, we show that recovered gene annotations can sensibly increase the quality of draft genome-scale metabolic models obtained from metagenome-assembled genomes, in some cases reaching the accuracy of models generated from complete genomes. Conclusions: KEMET therefore allows expanding genome annotations by targeted searches for orthologous sequences, enabling a better qualitative and quantitative assessment of metabolic capabilities in novel microbial organisms.
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