Evidence map›Paper›PMID 18840286›Full record

ArticleBMC bioinformatics2008

Bayesian modeling of recombination events in bacterial populations.

Pekka Marttinen, Adam Baldwin, William P Hanage, Chris Dowson, Eshwar Mahenthiralingam, Jukka Corander

Abstract read
In one paragraph

Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
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

18 citing papers in PubMed.

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  9. Origin and proliferation of multiple-drug resistance in bacterial pathogens.Microbiology and molecular biology reviews : MMBR · 2015
    Review
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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

6 authors.

Pekka MarttinenDepartment of Mathematics and statistics, University of Helsinki, FIN-00014, Finland. pekka.marttinen@helsinki.fi
Adam Baldwin
William P Hanage
Chris Dowson
Eshwar Mahenthiralingam
Jukka Corander

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe consider the discovery of recombinant segments jointly with their origins within multilocus DNA sequences from bacteria representing heterogeneous populations of fairly closely related species. The currently available methods for recombination detection capable of probabilistic characterization of uncertainty have a limited applicability in practice as the number of strains in a data set increases.

resultsWe introduce a Bayesian spatial structural model representing the continuum of origins over sites within the observed sequences, including a probabilistic characterization of uncertainty related to the origin of any particular site. To enable a statistically accurate and practically feasible approach to the analysis of large-scale data sets representing a single genus, we have developed a novel software tool (BRAT, Bayesian Recombination Tracker) implementing the model and the corresponding learning algorithm, which is capable of identifying the posterior optimal structure and to estimate the marginal posterior probabilities of putative origins over the sites.

conclusionA multitude of challenging simulation scenarios and an analysis of real data from seven housekeeping genes of 120 strains of genus Burkholderia are used to illustrate the possibilities offered by our approach. The software is freely available for download at URL http://web.abo.fi/fak/mnf//mate/jc/software/brat.html.

Indexed as

Base SequenceBayes TheoremRecombination, GeneticSoftwareBurkholderiaChromosome MappingInformation Storage and RetrievalPattern Recognition, AutomatedPhylogenySequence AlignmentSequence Analysis, DNAUncertainty

Identifiers

PMID18840286
PMCPMC2579306

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.