Evidence map›Paper›PMID 28451057›Full record

ArticleCommunicative & integrative biology2017

A network perspective on the virus world.

Jaime Iranzo, Mart Krupovic, Eugene V Koonin

Abstract readComment
In one paragraph

Article in Communicative & integrative biology, 2017. 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.

  1. A generative model for bipartite gene-sharing networks.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Hidden diversity of double-stranded DNA phages in symbioticPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Article
  4. Article
  5. Article
  6. Global Organization and Proposed Megataxonomy of the Virus World.Microbiology and molecular biology reviews : MMBR · 2020
    Review
  7. Article
  8. Computational approaches in viral ecology.Computational and structural biotechnology journal · 2020
    Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Biological species in the viral world.Proceedings of the National Academy of Sciences of the United States of America · 2018
    Article
  14. Article
  15. Review
  16. Article
  17. The enigmatic archaeal virosphere.Nature reviews. Microbiology · 2017
    Review
  18. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Jaime IranzoNational Center for Biotechnology Information, National Library of Medicine, Bethesda, MD, USA.
Mart KrupovicInstitut Pasteur, Unité Biologie Moléculaire du Gène chez les Extrêmophiles, Paris, France.
Eugene V KooninNational Center for Biotechnology Information, National Library of Medicine, Bethesda, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Viral evolution is characterized by high rates of horizontal gene transfer and fast sequence divergence. Furthermore, there are no universal genes shared by all viruses. As a result, distant relationships among viruses are better represented by a network than by a tree. Here we discuss 3 network representations of the virus world with decreasing levels of complexity, from a multilayer network that integrates sequence conservation and patterns of gene sharing to a classic genome similarity network. As new tools for network analysis are developed, we expect that novel insights into virus evolution will result from the study of more complex representations of the virus world.

Indexed as

bipartite networkgene sharing networkmultilayer networkphylogenomicsviral evolutionviral taxonomy

Identifiers

PMID28451057
PMCPMC5398231

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.