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Predicting the optimal growth temperatures of prokaryotes using only genome derived features.

ABSTRACT: MOTIVATION:Optimal 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. RESULTS:By 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.

SUBMITTER: Sauer DB

PROVIDER: S-EPMC6748728 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Predicting the optimal growth temperatures of prokaryotes using only genome derived features.

Sauer David B DB Wang Da-Neng DN

Bioinformatics (Oxford, England) 20190901 18

<h4>Motivation</h4>Optimal 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 limi ...[more]

PMID: 30689741

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Dataset Information

Predicting the optimal growth temperatures of prokaryotes using only genome derived features.

Publications

Predicting the optimal growth temperatures of prokaryotes using only genome derived features.

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