Evidence map›Paper›PMID 26439115›Full record

ArticlePLoS computational biology2015

Detecting Horizontal Gene Transfer between Closely Related Taxa.

Orit Adato, Noga Ninyo, Uri Gophna, Sagi Snir

Abstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

37 citing papers in PubMed.

  1. Article
  2. Article
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  4. Review
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  7. Review
  8. Review
  9. Article
  10. Article
  11. Origins and Evolution of NovelbioRxiv : the preprint server for biology · 2023
    Article
  12. Article
  13. Article
  14. hgtseq: A Standard Pipeline to Study Horizontal Gene Transfer.International journal of molecular sciences · 2022
    Article
  15. The Role of the Environment in Horizontal Gene Transfer.Molecular biology and evolution · 2022
    Article
  16. Article
  17. Current Methods for Recombination Detection in Bacteria.International journal of molecular sciences · 2022
    Review
  18. Article
  19. Article
  20. Genomes of NovelApplied and environmental microbiology · 2021
    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

4 authors.

Orit AdatoDepartment of Evolutionary Biology, University of Haifa, Haifa, Israel.
Noga NinyoDepartment of Evolutionary Biology, University of Haifa, Haifa, Israel.
Uri GophnaDepartment of Molecular Microbiology and Biotechnology Tel Aviv University, Tel-Aviv, Israel.
Sagi SnirDepartment of Evolutionary Biology, University of Haifa, Haifa, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Horizontal gene transfer (HGT), the transfer of genetic material between organisms, is crucial for genetic innovation and the evolution of genome architecture. Existing HGT detection algorithms rely on a strong phylogenetic signal distinguishing the transferred sequence from ancestral (vertically derived) genes in its recipient genome. Detecting HGT between closely related species or strains is challenging, as the phylogenetic signal is usually weak and the nucleotide composition is normally nearly identical. Nevertheless, there is a great importance in detecting HGT between congeneric species or strains, especially in clinical microbiology, where understanding the emergence of new virulent and drug-resistant strains is crucial, and often time-sensitive. We developed a novel, self-contained technique named Near HGT, based on the synteny index, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the constant relative mutability (CRM). Using CRM, the algorithm assigns a confidence score based on "unusual" sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three E. coli strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set. When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain.

Indexed as

Evolution, MolecularAlgorithmsChromosome MappingEscherichia coliGene Transfer, HorizontalGenome, BacterialModels, GeneticPhylogenySynteny

Identifiers

PMID26439115
PMCPMC4595140

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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.