Evidence map›Paper›PMID 22151602›Full record

ArticleBMC bioinformatics2011

The evolution of the tape measure protein: units, duplications and losses.

Mahdi Belcaid, Anne Bergeron, Guylaine Poisson

Abstract read
In one paragraph

Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
  2. Article
  3. Fundamental research · 2024
    Article
  4. Article
  5. Phage P2-71 against multi-drug resistantFrontiers in cellular and infection microbiology · 2024
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. NovelMicroorganisms · 2021
    Article
  15. Article
  16. Article
  17. 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

3 authors.

Mahdi BelcaidInformation and Computer Sciences, University of Hawaii, Honolulu, Hawaii, USA.
Anne Bergeron
Guylaine Poisson

Funding

USING EEG COMPLEXITY AND HIGH PERFORMANCE COMPUTING TO PROFILE SLEEPP20RR016467 · NCRR · UNIVERSITY OF HAWAII AT MANOA · PI HOLMES, ERIC · 2001 to 2011
$24.5M
RESEARCH DESIGN AND BIOSTATISTICS COREU54MD007584 · NIMHD · UNIVERSITY OF HAWAII AT MANOA · PI MOKUAU, NOREEN, SHIRAMIZU, BRUCE T · 2012 to 2018
$21.1M
UHI COBRE: P3: GROUP A STREPT IN RELATION TO ACUTE RHEUMATIC FEVER IN HAWAIIP20RR018727 · NCRR · UNIVERSITY OF HAWAII AT MANOA · PI HOANG, TUNG T · 2003 to 2011
$13.8M
RESEARCH ETHICSU54RR026136 · NCRR · UNIVERSITY OF HAWAII AT MANOA · PI INAZU, JUDITH · 2010 to 2011
$8.5M
NCRR NIH HHS P20RR016467NCRR NIH HHS P20RR018727NCRR NIH HHS U54RR026136
6 · The paper itself

Abstract

backgroundA large family of viruses that infect bacteria, called phages, is characterized by long tails used to inject DNA into their victims' cells. The tape measure protein got its name because the length of the corresponding gene is proportional to the length of the phage's tail: a fact shown by actually copying or splicing out parts of DNA in exemplar species. A natural question is whether there exist units for these tape measures, and if different tape measures have different units and lengths. Such units would allow us to retrace the evolution of tape measure proteins using their duplication/loss history. The vast number of sequenced phages genomes allows us to attack this problem with a comparative genomics approach.

resultsHere we describe a subset of phages whose tape measure proteins contain variable numbers of an 11 amino acids sequence repeat, aligned with sequence similarity, structural properties, and simple arithmetics. This subset provides a unique opportunity for the combinatorial study of phage evolution, without the added uncertainties of multiple alignments, which are trivial in this case, or of protein functions, that are well established. We give a heuristic that reconstructs the duplication history of these sequences, using divergent strains to discriminate between mutations that occurred before and after speciation, or lineage divergence. The heuristic is based on an efficient algorithm that gives an exhaustive enumeration of all possible parsimonious reconstructions of the duplication/speciation history of a single nucleotide. Finally, we present a method that allows, when possible, to discriminate between duplication and loss events.

conclusionsEstablishing the evolutionary history of viruses is difficult, in part due to extensive recombinations and gene transfers, and high mutation rates that often erase detectable similarity between homologous genes. In this paper, we introduce new tools to address this problem.

Indexed as

Evolution, MolecularRepetitive Sequences, Amino AcidAlgorithmsBacteriophagesGene DuplicationGenomicsSequence DeletionViral ProteinsViral Proteins

Identifiers

PMID22151602
PMCPMC3271669

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.