Inferring bacterial recombination rates from large-scale sequencing datasets

Mingzhi Lin, Edo Kussell

Research output: Contribution to journalArticle

Abstract

We present a robust, computationally efficient method (https://github.com/kussell-lab/mcorr) for inferring the parameters of homologous recombination in bacteria, which can be applied in diverse datasets, from whole-genome sequencing to metagenomic shotgun sequencing data. Using correlation profiles of synonymous substitutions, we determine recombination rates and diversity levels of the shared gene pool that has contributed to a given sample. We validated the recombination parameters using data from laboratory experiments. We determined the recombination parameters for a wide range of bacterial species, and inferred the distribution of shared gene pools for global Helicobacter pylori isolates. Using metagenomics data of the infant gut microbiome, we measured the recombination parameters of multidrug-resistant Escherichia coli ST131. Lastly, we analyzed ancient samples of bacterial DNA from the Copper Age ‘Iceman’ mummy and from 14th century victims of the Black Death, obtaining measurements of bacterial recombination rates and gene pool diversity of earlier eras.

Original languageEnglish (US)
Pages (from-to)199-204
Number of pages6
JournalNature methods
Volume16
Issue number2
DOIs
StatePublished - Feb 1 2019

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

Fingerprint Dive into the research topics of 'Inferring bacterial recombination rates from large-scale sequencing datasets'. Together they form a unique fingerprint.

  • Cite this