Evidence map›Paper›PMID 26323763›Full record

ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2015

New genes from non-coding sequence: the role of de novo protein-coding genes in eukaryotic evolutionary innovation.

Aoife McLysaght, Daniele Guerzoni

Abstract readReview
In one paragraph

Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.

0numbers the graph read from it
0cells of the map it votes in
93citing 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

93 citing papers in PubMed.

  1. Review
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  6. NGATHA carpel development genes evolved in the common ancestor of seed plants.The Plant journal : for cell and molecular biology · 2025
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33 more citing papers are in PubMed but not listed here.

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

2 authors.

Aoife McLysaghtSmurfit Institute of Genetics, University of Dublin, Trinity College Dublin, Dublin 2, Republic of Ireland aoife.mclysaght@tcd.ie.ORCID http://orcid.org/0000-0003-2552-6220
Daniele GuerzoniSmurfit Institute of Genetics, University of Dublin, Trinity College Dublin, Dublin 2, Republic of Ireland.

Funding

European Research Council 309834
6 · The paper itself

Abstract

The origin of novel protein-coding genes de novo was once considered so improbable as to be impossible. In less than a decade, and especially in the last five years, this view has been overturned by extensive evidence from diverse eukaryotic lineages. There is now evidence that this mechanism has contributed a significant number of genes to genomes of organisms as diverse as Saccharomyces, Drosophila, Plasmodium, Arabidopisis and human. From simple beginnings, these genes have in some instances acquired complex structure, regulated expression and important functional roles. New genes are often thought of as dispensable late additions; however, some recent de novo genes in human can play a role in disease. Rather than an extremely rare occurrence, it is now evident that there is a relatively constant trickle of proto-genes released into the testing ground of natural selection. It is currently unknown whether de novo genes arise primarily through an 'RNA-first' or 'ORF-first' pathway. Either way, evolutionary tinkering with this pool of genetic potential may have been a significant player in the origins of lineage-specific traits and adaptations.

Indexed as

Biological EvolutionAnimalsEukaryotaGene Expression RegulationHumansPlantsRNA, UntranslatedRNA, Untranslatedde novo genesevolutionopen reading frameproto-genes

Identifiers

PMID26323763
PMCPMC4571571

What OpenQuestion holds

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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.