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Bacteria and Archaea display a variety of phenotypic traits and can adapt to diverse ecological niches. However, systematic annotation of prokaryotic phenotypes is lacking. We have therefore developed ProTraits, a resource containing ∼545 000 novel phenotype inferences, spanning 424 traits assigned to 3046 bacterial and archaeal species. These annotations were assigned by a computational pipeline that associates microbes with phenotypes by text-mining the scientific literature and the broader World Wide Web, while also being able to define novel concepts from unstructured text. Moreover, the ProTraits pipeline assigns phenotypes by drawing extensively on comparative genomics, capturing patterns in gene repertoires, codon usage biases, proteome composition and co-occurrence in metagenomes. Notably, we find that gene synteny is highly predictive of many phenotypes, and highlight examples of gene neighborhoods associated with spore-forming ability. A global analysis of trait interrelatedness outlined clusters in the microbial phenotype network, suggesting common genetic underpinnings. Our extended set of phenotype annotations allows detection of 57 088 high confidence gene-trait links, which recover many known associations involving sporulation, flagella, catalase activity, aerobicity, photosynthesis and other traits. Over 99% of the commonly occurring gene families are involved in genetic interactions conditional on at least one phenotype, suggesting that epistasis has a major role in shaping microbial gene content.
epistasis, phenomics ; microbial ecology ; gene-phenotype association ; epistasis ; text mining, Multifactorial Inheritance, 10009 Department of Informatics, text mining, Data Resources and Analyses, 000 Computer science, knowledge & systems, microbial ecology, Genes, Archaeal, 1311 Genetics, Artificial Intelligence, Genome, Archaeal, Databases, Genetic, Genetics, Data Mining, Codon, Biology, 000 Computer science, knowledge & systems, Bacteria, gene-phenotype association, Computational Biology, Reproducibility of Results, phenomics, Molecular Sequence Annotation, Archaea, Fenotip, Genòmica, Phenotype, Genes, Bacterial, FOS: Biological sciences, Metagenome, Genome, Bacterial
epistasis, phenomics ; microbial ecology ; gene-phenotype association ; epistasis ; text mining, Multifactorial Inheritance, 10009 Department of Informatics, text mining, Data Resources and Analyses, 000 Computer science, knowledge & systems, microbial ecology, Genes, Archaeal, 1311 Genetics, Artificial Intelligence, Genome, Archaeal, Databases, Genetic, Genetics, Data Mining, Codon, Biology, 000 Computer science, knowledge & systems, Bacteria, gene-phenotype association, Computational Biology, Reproducibility of Results, phenomics, Molecular Sequence Annotation, Archaea, Fenotip, Genòmica, Phenotype, Genes, Bacterial, FOS: Biological sciences, Metagenome, Genome, Bacterial
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