Evidence map›Paper›PMID 22064866›Full record

ArticleNucleic acids research2012

Detection of recombination events in bacterial genomes from large population samples.

Pekka Marttinen, William P Hanage, Nicholas J Croucher, Thomas R Connor, Simon R Harris, Stephen D Bentley, Jukka Corander

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
132citing papers in PubMed, 1 pooled it
7.3field-weighted citation impact, top 2% 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

132 citing papers in PubMed, 1 synthesis or guideline pooled it, 202 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Evolution of homologous recombination rates across bacteria.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  7. Article
  8. Current Methods for Recombination Detection in Bacteria.International journal of molecular sciences · 2022
    Review
  9. Review
  10. Microbial genomics · 2021
    Article
  11. Article
  12. Article
  13. Review
  14. Molecular Characterization ofFrontiers in microbiology · 2021
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

72 more citing papers are in PubMed but not listed here.

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

7 authors at 3 institutions in 3 countries.

Pekka MarttinenDepartment of Biomedical Engineering and Computational Science, Aalto University, PO Box 12200, FI-00076 AALTO, Finland. pekka.marttinen@aalto.fi
William P Hanage
Nicholas J Croucher
Thomas R Connor
Simon R Harris
Stephen D Bentley
Jukka Corander
Harvard University Press · USWellcome Sanger Institute · GBÅbo Akademi University · FI

Funding

Understanding and controlling antibiotic resistanceU54GM088558 · NIGMS · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANAGE, WILLIAM · 2009 to 2018
$26.4M
NIGMS NIH HHS U54 GM088558NIGMS NIH HHS U54GM088558
6 · The paper itself

Abstract

Analysis of important human pathogen populations is currently under transition toward whole-genome sequencing of growing numbers of samples collected on a global scale. Since recombination in bacteria is often an important factor shaping their evolution by enabling resistance elements and virulence traits to rapidly transfer from one evolutionary lineage to another, it is highly beneficial to have access to tools that can detect recombination events. Multiple advanced statistical methods exist for such purposes; however, they are typically limited either to only a few samples or to data from relatively short regions of a total genome. By harnessing the power of recent advances in Bayesian modeling techniques, we introduce here a method for detecting homologous recombination events from whole-genome sequence data for bacterial population samples on a large scale. Our statistical approach can efficiently handle hundreds of whole genome sequenced population samples and identify separate origins of the recombinant sequence, offering an enhanced insight into the diversification of bacterial clones at the level of the whole genome. A data set of 241 whole genome sequences from an important pandemic lineage of Streptococcus pneumoniae is used together with multiple simulated data sets to demonstrate the potential of our approach.

Indexed as

Genome, BacterialHomologous RecombinationAlgorithmsBayes TheoremGenomicsStreptococcus pneumoniae

Identifiers

PMID22064866
PMCPMC3245952
OpenAlexW2080618984

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.