Evidence map›Paper›PMID 20825637›Full record

ArticleBiology direct2010

Asymmetric and non-uniform evolution of recently duplicated human genes.

Alexander Y Panchin, Mikhail S Gelfand, Vasily E Ramensky, Irena I Artamonova

Open access · goldAbstract read
In one paragraph

Article in Biology direct, 2010. 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
1.3field-weighted citation impact, top 17% 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, 35 citations in OpenAlex.

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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 at 3 institutions in 1 country.

Alexander Y PanchinM. V. Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Vorobyevy Gory 1-73, Moscow, 119992, Russia.
Mikhail S Gelfand
Vasily E Ramensky
Irena I Artamonova
Lomonosov Moscow State University · RUEngelhardt Institute of Molecular Biology · RUInstitute for Information Transmission Problems · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGene duplications are a source of new genes and protein functions. The innovative role of duplication events makes families of paralogous genes an interesting target for studies in evolutionary biology. Here we study global trends in the evolution of human genes that resulted from recent duplications.

resultsThe pressure of negative selection is weaker during a short time immediately after a duplication event. Roughly one fifth of genes in paralogous gene families are evolving asymmetrically: one of the proteins encoded by two closest paralogs accumulates amino acid substitutions significantly faster than its partner. This asymmetry cannot be explained by differences in gene expression levels. In asymmetric gene pairs the number of deleterious mutations is increased in one copy, while decreased in the other copy as compared to genes constituting non-asymmetrically evolving pairs. The asymmetry in the rate of synonymous substitutions is much weaker and not significant.

conclusionsThe increase of negative selection pressure over time after a duplication event seems to be a major trend in the evolution of human paralogous gene families. The observed asymmetry in the evolution of paralogous genes shows that in many cases one of two gene copies remains practically unchanged, while the other accumulates functional mutations. This supports the hypothesis that slowly evolving gene copies preserve their original functions, while fast evolving copies obtain new specificities or functions.

Indexed as

Evolution, MolecularGenes, DuplicateHumansSelection, Genetic

Identifiers

PMID20825637
PMCPMC2942815
OpenAlexW2128607794

What OpenQuestion holds

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LicenceCC BY
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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.