ArticleBioinformatics (Oxford, England)2019
Predicting the optimal growth temperatures of prokaryotes using only genome derived features.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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49 citing papers in PubMed, 102 citations in OpenAlex.
- Article
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- Genomic and evolutionary factors influencing the prediction accuracy of optimal growth temperature in prokaryotes.mSystems · 2026Article
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- Bacterial Siderophore Production in Metal-Rich Environments: Underexplored Sources of Siderophores and Insights into Bioremediation.Journal of natural products · 2026Review
- Evolution of Translation Initiation Factor 2 Extensions Links Initiation to Bacterial Stress Response.bioRxiv : the preprint server for biology · 2026Article
- Constructing a "periodic table" of bacteria to map diversity in trait space.The ISME journal · 2026Article
- Ecological constraints and evolutionary trade-offs shape nitrogen fixation across habitats.ISME communications · 2026Article
- Improved prediction of microbial optimal growth temperatures with neural networks and protein language models.Frontiers in genetics · 2026Article
- Machine learning prediction of bacterial optimal growth temperature from protein domain signatures reveals thermoadaptation mechanisms.BMC genomics · 2025Article
- Integrating Intermediate Traits in Phylogenetic Genotype-to-Phenotype Studies.Integrative and comparative biology · 2025Review
- The defensome of prokaryotes in aquifers.Nature communications · 2025Article
- Improved maximum growth rate prediction from microbial genomes by integrating phylogenetic information.Nature communications · 2025Article
- Ecogenomic insights into the resilience of keystone Blastococcus Species in extreme environments: a comprehensive analysis.BMC genomics · 2025Article
- Bacterial Growth Temperature as a Horizontally Acquired Polygenic Trait.Genome biology and evolution · 2025Article
- Discovery and cultivation of prokaryotic taxa in the age of metagenomics and artificial intelligence.The ISME journal · 2025Article
- Evaluating agar-plating and dilution-to-extinction isolation methods for generating oak-associated microbial culture collections.ISME communications · 2025Article
- The Characterization of Ancient Methanococcales Malate Dehydrogenases Reveals That Strong Thermal Stability Prevents Unfolding Under Intense γ-Irradiation.Molecular biology and evolution · 2024Article
- Article
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2 authors at 1 institution in 1 country.
Funding
Abstract
motivationOptimal growth temperature is a fundamental characteristic of all living organisms. Knowledge of this temperature is central to the study of a prokaryote, the thermal stability and temperature dependent activity of its genes, and the bioprospecting of its genome for thermally adapted proteins. While high throughput sequencing methods have dramatically increased the availability of genomic information, the growth temperatures of the source organisms are often unknown. This limits the study and technological application of these species and their genomes. Here, we present a novel method for the prediction of growth temperatures of prokaryotes using only genomic sequences.
resultsBy applying the reverse ecology principle that an organism's genome includes identifiable adaptations to its native environment, we can predict a species' optimal growth temperature with an accuracy of 5.17°C root-mean-square error and a coefficient of determination of 0.835. The accuracy can be further improved for specific taxonomic clades or by excluding psychrophiles. This method provides a valuable tool for the rapid calculation of organism growth temperature when only the genome sequence is known. AVAILABILITY AND IMPLEMENTATION: Source code, genomes analyzed and features calculated are available at: https://github.com/DavidBSauer/OGT_prediction. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.