In Silico Genome-Genome Hybridization Values Accurately and Precisely Predict Empirical DNA-DNA Hybridization Values for Classifying Prokaryotes

TL;DR

This paper presents a computational method that accurately predicts traditional DNA-DNA hybridization values from whole genome sequences, simplifying prokaryotic classification.

Contribution

The authors developed an in silico pipeline based on BLAST comparisons that reliably estimates empirical hybridization values, improving reproducibility and efficiency.

Findings

High accuracy in predicting DNA-DNA hybridization values

Method is precise and consistent with wet lab results

Streamlines prokaryotic genome classification

Abstract

For nearly 50 years microbiologists have been determining prokaryotic genome relatedness by means of nucleic acid reassociation kinetics. These methods, however, are technically challenging, difficult to reproduce, and - given the time and resources it takes to generate a single data-point - not cost effective. In the post genomic era, with the cost of sequencing whole prokaryotic genomes no longer a limiting factor, we believed that computationally predicting the output value from a traditional DNA-DNA hybridization experiment using pair-wise comparisons of whole genome sequences to be of value. While other computational whole-genome classification methods exist, they predict values on widely different scales than DNA-DNA hybridization, introducing yet another metric into the polyphasic approach of defining microbial species. Our goal was to develop an in silico BLAST based pipeline that would predict with a high level of certainty the value of the wet lab-based DNA-DNA hybridization values. Here we report on one such method that produces estimates that are both accurate and precise with respect to the DNA-DNA hybridization values they are designed to emulate.