ArticleBMC bioinformatics2010
Predicting phenotypic traits of prokaryotes from protein domain frequencies.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Article
- Machine learning prediction of bacterial optimal growth temperature from protein domain signatures reveals thermoadaptation mechanisms.BMC genomics · 2025Article
- Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets.Communications biology · 2025Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Inference of phenotype-defining functional modules of protein families for microbial plant biomass degraders.Biotechnology for biofuels · 2014Article
- Protein signature-based estimation of metagenomic abundances including all domains of life and viruses.Bioinformatics (Oxford, England) · 2013Article
- Bayesian prediction of microbial oxygen requirement.F1000Research · 2013Article
- A domain sequence approach to pangenomics: applications to Escherichia coli.F1000Research · 2012Article
- Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEstablishing the relationship between an organism's genome sequence and its phenotype is a fundamental challenge that remains largely unsolved. Accurately predicting microbial phenotypes solely based on genomic features will allow us to infer relevant phenotypic characteristics when the availability of a genome sequence precedes experimental characterization, a scenario that is favored by the advent of novel high-throughput and single cell sequencing techniques.
resultsWe present a novel approach to predict the phenotype of prokaryotes directly from their protein domain frequencies. Our discriminative machine learning approach provides high prediction accuracy of relevant phenotypes such as motility, oxygen requirement or spore formation. Moreover, the set of discriminative domains provides biological insight into the underlying phenotype-genotype relationship and enables deriving hypotheses on the possible functions of uncharacterized domains.
conclusionsFast and accurate prediction of microbial phenotypes based on genomic protein domain content is feasible and has the potential to provide novel biological insights. First results of a systematic check for annotation errors indicate that our approach may also be applied to semi-automatic correction and completion of the existing phenotype annotation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.