Evidence map›Paper›PMID 30664775›Full record

ArticleNature methods2019

Inferring bacterial recombination rates from large-scale sequencing datasets.

Mingzhi Lin, Edo Kussell

Abstract readHistorical Article
PubMed Publisher
In one paragraph

Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing 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

55 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Evolution of homologous recombination rates across bacteria.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  18. Article
  19. Article
  20. Article
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.

Mingzhi LinDepartment of Biology and Center for Genomics and Systems Biology, New York University, New York, NY, USA.ORCID http://orcid.org/0000-0002-8435-3780
Edo KussellDepartment of Biology and Center for Genomics and Systems Biology, New York University, New York, NY, USA. edo.kussell@nyu.edu.ORCID http://orcid.org/0000-0003-0590-4036

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

DNA, AncientRecombination, GeneticSequence Analysis, DNAComputational BiologyComputer SimulationDatabases, GeneticDNA, BacterialDrug Resistance, BacterialEscherichia coliGastrointestinal MicrobiomeGenetic TechniquesGenetic VariationHelicobacter pyloriHistory, MedievalHumansMetagenomicsDNA, AncientDNA, Bacterial

Identifiers

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.