Evidence map›Paper›PMID 31560374›Full record

ArticleGenome biology and evolution2019

Machine-Learning Classification Suggests That Many Alphaproteobacterial Prophages May Instead Be Gene Transfer Agents.

Roman Kogay, Taylor B Neely, Daniel P Birnbaum, Camille R Hankel, Migun Shakya, Olga Zhaxybayeva

Open access · goldAbstract read
In one paragraph

Article in Genome biology and evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

16 citing papers in PubMed, 27 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Loss of the Rhodobacter capsulatus Serine Acetyl Transferase Gene,Applied and environmental microbiology · 2022
    Article
  10. Article
  11. Hidden diversity of double-stranded DNA phages in symbioticPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Gene Transfer Agents in Symbiotic Microbes.Results and problems in cell differentiation · 2020
    Review
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 at 1 institution in 1 country.

Roman KogayDepartment of Biological Sciences, Dartmouth College, Hanover, New Hampshire.
Taylor B NeelyDepartment of Biological Sciences, Dartmouth College, Hanover, New Hampshire.
Daniel P BirnbaumDepartment of Biological Sciences, Dartmouth College, Hanover, New Hampshire.
Camille R HankelDepartment of Biological Sciences, Dartmouth College, Hanover, New Hampshire.
Migun ShakyaDepartment of Biological Sciences, Dartmouth College, Hanover, New Hampshire.
Olga ZhaxybayevaDepartment of Biological Sciences, Dartmouth College, Hanover, New Hampshire.
Dartmouth College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many of the sequenced bacterial and archaeal genomes encode regions of viral provenance. Yet, not all of these regions encode bona fide viruses. Gene transfer agents (GTAs) are thought to be former viruses that are now maintained in genomes of some bacteria and archaea and are hypothesized to enable exchange of DNA within bacterial populations. In Alphaproteobacteria, genes homologous to the "head-tail" gene cluster that encodes structural components of the Rhodobacter capsulatus GTA (RcGTA) are found in many taxa, even if they are only distantly related to Rhodobacter capsulatus. Yet, in most genomes available in GenBank RcGTA-like genes have annotations of typical viral proteins, and therefore are not easily distinguished from their viral homologs without additional analyses. Here, we report a "support vector machine" classifier that quickly and accurately distinguishes RcGTA-like genes from their viral homologs by capturing the differences in the amino acid composition of the encoded proteins. Our open-source classifier is implemented in Python and can be used to scan homologs of the RcGTA genes in newly sequenced genomes. The classifier can also be trained to identify other types of GTAs, or even to detect other elements of viral ancestry. Using the classifier trained on a manually curated set of homologous viruses and GTAs, we detected RcGTA-like "head-tail" gene clusters in 57.5% of the 1,423 examined alphaproteobacterial genomes. We also demonstrated that more than half of the in silico prophage predictions are instead likely to be GTAs, suggesting that in many alphaproteobacterial genomes the RcGTA-like elements remain unrecognized.

Indexed as

Support Vector MachineAlphaproteobacteriaGenes, BacterialGenes, ViralGenome, BacterialProphagesbinary classificationcarbon depletionGTARhodobacter capsulatussupport vector machinevirus exaptation

Identifiers

PMID31560374
PMCPMC6821227
OpenAlexW2975884066

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.