Evidence map›Paper›PMID 36972309›Full record

ArticlePLoS genetics2023

Rhometa: Population recombination rate estimation from metagenomic read datasets.

Sidaswar Krishnan, Matthew Z DeMaere, Dominik Beck, Martin Ostrowski, Justin R Seymour, Aaron E Darling

Abstract read
In one paragraph

Article in PLoS genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Sidaswar KrishnanClimate Change Cluster, Faculty of Science, University of Technology Sydney, Sydney, NSW, Australia.ORCID 0000-0002-0568-4064
Matthew Z DeMaereAustralian Institute for Microbiology & Infection, University of Technology Sydney, Sydney, NSW, Australia.ORCID 0000-0002-7601-5108
Dominik BeckCentre for Health Technologies and the School of Biomedical Engineering, University of Technology Sydney, Sydney, NSW, Australia.
Martin OstrowskiClimate Change Cluster, Faculty of Science, University of Technology Sydney, Sydney, NSW, Australia.
Justin R SeymourClimate Change Cluster, Faculty of Science, University of Technology Sydney, Sydney, NSW, Australia.
Aaron E DarlingAustralian Institute for Microbiology & Infection, University of Technology Sydney, Sydney, NSW, Australia.ORCID 0000-0003-2397-7925

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prokaryotic evolution is influenced by the exchange of genetic information between species through a process referred to as recombination. The rate of recombination is a useful measure for the adaptive capacity of a prokaryotic population. We introduce Rhometa (https://github.com/sid-krish/Rhometa), a new software package to determine recombination rates from shotgun sequencing reads of metagenomes. It extends the composite likelihood approach for population recombination rate estimation and enables the analysis of modern short-read datasets. We evaluated Rhometa over a broad range of sequencing depths and complexities, using simulated and real experimental short-read data aligned to external reference genomes. Rhometa offers a comprehensive solution for determining population recombination rates from contemporary metagenomic read datasets. Rhometa extends the capabilities of conventional sequence-based composite likelihood population recombination rate estimators to include modern aligned metagenomic read datasets with diverse sequencing depths, thereby enabling the effective application of these techniques and their high accuracy rates to the field of metagenomics. Using simulated datasets, we show that our method performs well, with its accuracy improving with increasing numbers of genomes. Rhometa was validated on a real S. pneumoniae transformation experiment, where we show that it obtains plausible estimates of the rate of recombination. Finally, the program was also run on ocean surface water metagenomic datasets, through which we demonstrate that the program works on uncultured metagenomic datasets.

Indexed as

MetagenomeMetagenomicsAlgorithmsHigh-Throughput Nucleotide SequencingLikelihood FunctionsRecombination, GeneticSequence Analysis, DNASoftware

Identifiers

PMID36972309
PMCPMC10079220

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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.