ReviewFEMS microbiology reviews2023
From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.
Review in FEMS microbiology reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Improved prediction of microbial optimal growth temperatures with neural networks and protein language models.Frontiers in genetics · 2026Article
- Genome-based reassessment ofCurrent research in microbial sciences · 2026Article
- Comparative genomic analysis of hydrogen peroxide and nitric oxide metabolic pathways in Limosilactobacillus fermentum.Scientific reports · 2025Article
- Rapid key gene discovery for bacterial shape: a cross-species machine learning approach.BMC microbiology · 2025Article
- Machine learning prediction of bacterial optimal growth temperature from protein domain signatures reveals thermoadaptation mechanisms.BMC genomics · 2025Article
- Bacterial Systematic Genetics and Integrated Multi-Omics: Beyond Static Genomics Toward Predictive Models.International journal of molecular sciences · 2025Review
- Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets.Communications biology · 2025Article
- Integrating Deep Learning Models with Genome-Wide Association Study-Based Identification Enhanced Phenotype Predictions in Group AJournal of microbiology and biotechnology · 2025Article
- Environmental matrix and moisture influence soil microbial phenotypes in a simplified porous media incubation.mSystems · 2025Article
- Functional and genomic analyses of plant growth promoting traits in Priestia aryabhattai and Paenibacillus sp. isolates from tomato rhizosphere.Scientific reports · 2025Article
- Understanding plant-microorganism interactions: The key roles of soil, rhizosphere, and direct and indirect mechanisms.AIMS microbiology · 2025Review
- AMRomics: a scalable workflow to analyze large microbial genome collections.BMC genomics · 2024Article
- AMRViz enables seamless genomics analysis and visualization of antimicrobial resistance.BMC bioinformatics · 2024Article
- PNNGS, a multi-convolutional parallel neural network for genomic selection.Frontiers in plant science · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.
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Registered trials
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