Evidence map›Paper›PMID 24172133›Full record

ArticleGenetics2014

Inference of the properties of the recombination process from whole bacterial genomes.

M Azim Ansari, Xavier Didelot

Open access · bronzeAbstract read
In one paragraph

Article in Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 52 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

M Azim AnsariDepartment of Statistics, University of Oxford, Oxford OX1 3TG, United Kingdom.
Xavier Didelot
Imperial College London · GBUniversity of Oxford · GB

Funding

Biotechnology and Biological Sciences Research CouncilDepartment of Health G0800778Medical Research Council G0800778Medical Research Council MR/K010174/1Wellcome Trust 087646/Z/08/Z
6 · The paper itself

Abstract

Patterns of linkage disequilibrium, homoplasy, and incompatibility are difficult to interpret because they depend on several factors, including the recombination process and the population structure. Here we introduce a novel model-based framework to infer recombination properties from such summary statistics in bacterial genomes. The underlying model is sequentially Markovian so that data can be simulated very efficiently, and we use approximate Bayesian computation techniques to infer parameters. As this does not require us to calculate the likelihood function, the model can be easily extended to investigate less probed aspects of recombination. In particular, we extend our model to account for the bias in the recombination process whereby closely related bacteria recombine more often with one another. We show that this model provides a good fit to a data set of Bacillus cereus genomes and estimate several recombination properties, including the rate of bias in recombination. All the methods described in this article are implemented in a software package that is freely available for download at http://code.google.com/p/clonalorigin/.

Indexed as

Models, GeneticRecombination, GeneticBacillus cereusBayes TheoremGenetics, PopulationGenetic VariationGenome, BacterialLinkage DisequilibriumMarkov ChainsSoftwarebacteriabiased recombinationfour-gamete test (G4)homologous recombinationhomology-dependent recombinationhomoplasylinkage disequilibrium (LD)

Identifiers

PMID24172133
PMCPMC3872189
OpenAlexW2040322059

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.