Evidence map›Paper›PMID 19098983›Full record

ArticlePloS one2008

A method for the simultaneous estimation of selection intensities in overlapping genes.

Niv Sabath, Giddy Landan, Dan Graur

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 12% 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

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.

  1. Pooled it
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  11. Concomitant emergence of the antisense protein gene of HIV-1 and of the pandemic.Proceedings of the National Academy of Sciences of the United States of America · 2016
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  16. Identification of an overprinting gene in Merkel cell polyomavirus provides evolutionary insight into the birth of viral genes.Proceedings of the National Academy of Sciences of the United States of America · 2013
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  20. Is life unique?Life (Basel, Switzerland) · 2011
    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

3 authors at 1 institution in 1 country.

Niv SabathDepartment of Biology and Biochemistry, University of Houston, Houston, Texas, United States of America. nsabath@uh.edu
Giddy Landan
Dan Graur
University of Houston · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inferring the intensity of positive selection in protein-coding genes is important since it is used to shed light on the process of adaptation. Recently, it has been reported that overlapping genes, which are ubiquitous in all domains of life, seem to exhibit inordinate degrees of positive selection. Here, we present a new method for the simultaneous estimation of selection intensities in overlapping genes. We show that the appearance of positive selection is caused by assuming that selection operates independently on each gene in an overlapping pair, thereby ignoring the unique evolutionary constraints on overlapping coding regions. Our method uses an exact evolutionary model, thereby voiding the need for approximation or intensive computation. We test the method by simulating the evolution of overlapping genes of different types as well as under diverse evolutionary scenarios. Our results indicate that the independent estimation approach leads to the false appearance of positive selection even though the gene is in reality subject to negative selection. Finally, we use our method to estimate selection in two influenza A genes for which positive selection was previously inferred. We find no evidence for positive selection in both cases.

Indexed as

Evolution, MolecularGenes, OverlappingGenetic TechniquesSelection, GeneticAnimalsInfluenza A virusModels, Genetic

Identifiers

PMID19098983
PMCPMC2601044
OpenAlexW2162277420

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.