Evidence map›Paper›PMID 39004885›Full record

ArticleGenome biology and evolution2024

Ancestral Sequence Reconstruction as a Tool to Detect and Study De Novo Gene Emergence.

Nikolaos Vakirlis, Omer Acar, Vijay Cherupally, Anne-Ruxandra Carvunis

Erratum issuedAbstract read
In one paragraph

Article in Genome biology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. DetectNAR genomics and bioinformatics · 2026
    Article
  2. Review
  3. Review
  4. Article
  5. Impact of GC content on de novo gene birth.Nature communications · 2026
    Article
  6. Review
  7. De Novo Genes: Current Status and Future Goals.Genome biology and evolution · 2025
    Article
  8. Article
  9. Review
  10. Review
  11. Article
  12. Article
  13. De Novo Emerged Gene Search in Eukaryotes with DENSE.Genome biology and evolution · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Nikolaos VakirlisInstitute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece.ORCID 0000-0001-7606-6987
Omer AcarPittsburgh Center for Evolutionary Biology and Medicine, Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.ORCID 0000-0001-5361-256X
Vijay CherupallyPittsburgh Center for Evolutionary Biology and Medicine, Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.ORCID 0009-0009-4781-956X
Anne-Ruxandra CarvunisPittsburgh Center for Evolutionary Biology and Medicine, Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.ORCID 0000-0002-6474-6413

Funding

From non-coding to coding: uncovering the hidden coding potential of non-coding sequences and its role in de novo gene evolutionDP2GM137422 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CARVUNIS, ANNE-RUXANDRA · 2019 to 2019
$2.3M
NIGMS NIH HHS DP2 GM137422
6 · The paper itself

Abstract

New protein-coding genes can evolve from previously noncoding genomic regions through a process known as de novo gene emergence. Evidence suggests that this process has likely occurred throughout evolution and across the tree of life. Yet, confidently identifying de novo emerged genes remains challenging. Ancestral sequence reconstruction is a promising approach for inferring whether a gene has emerged de novo or not, as it allows us to inspect whether a given genomic locus ancestrally harbored protein-coding capacity. However, the use of ancestral sequence reconstruction in the context of de novo emergence is still in its infancy and its capabilities, limitations, and overall potential are largely unknown. Notably, it is difficult to formally evaluate the protein-coding capacity of ancestral sequences, particularly when new gene candidates are short. How well-suited is ancestral sequence reconstruction as a tool for the detection and study of de novo genes? Here, we address this question by designing an ancestral sequence reconstruction workflow incorporating different tools and sets of parameters and by introducing a formal criterion that allows to estimate, within a desired level of confidence, when protein-coding capacity originated at a particular locus. Applying this workflow on ∼2,600 short, annotated budding yeast genes (<1,000 nucleotides), we found that ancestral sequence reconstruction robustly predicts an ancient origin for the most widely conserved genes, which constitute "easy" cases. For less robust cases, we calculated a randomization-based empirical P-value estimating whether the observed conservation between the extant and ancestral reading frame could be attributed to chance. This formal criterion allowed us to pinpoint a branch of origin for most of the less robust cases, identifying 49 genes that can unequivocally be considered de novo originated since the split of the Saccharomyces genus, including 37 Saccharomyces cerevisiae-specific genes. We find that for the remaining equivocal cases we cannot rule out different evolutionary scenarios including rapid evolution, multiple gene losses, or a recent de novo origin. Overall, our findings suggest that ancestral sequence reconstruction is a valuable tool to study de novo gene emergence but should be applied with caution and awareness of its limitations.

Indexed as

Evolution, MolecularGenes, FungalGenome, FungalPhylogenySaccharomyces cerevisiaeancestral sequence reconstructionbudding yeastde novo gene emergencenovel genesphylostratigraphy

Identifiers

PMID39004885
PMCPMC11299112

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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.