Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
Contradictory Results

From de novo to ‘de nono’: most novel protein coding genes identified with phylostratigraphy represent old genes or recent duplicates

Claudio Casola
doi: https://doi.org/10.1101/287193
Claudio Casola
1Department of Ecosystem Science and Management Texas A&M University, College Station, TX 77843-2138
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: ccasola@tamu.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

The evolution of novel protein-coding genes from noncoding regions of the genome is one of the most compelling evidence for genetic innovations in nature. One popular approach to identify de novo genes is phylostratigraphy, which consists of determining the approximate time of origin (age) of a gene based on its distribution along a species phylogeny. Several studies have revealed significant flaws in determining the age of genes, including de novo genes, using phylostratigraphy alone. However, the rate of false positives in de novo gene surveys, based on phylostratigraphy, remains unknown. Here, I re-analyze the findings from three studies, two of which identified tens to hundreds of rodent-specific de novo genes adopting a phylostratigraphy-centered approach. Most of the putative de novo genes discovered in these investigations are no longer included in recently updated mouse gene sets. Using a combination of synteny information and sequence similarity searches, I show that about 60% of the remaining 381 putative de novo genes share homology with genes from other vertebrates, originated through gene duplication, and/or share no synteny information with non-rodent mammals. These results led to an estimated rate of ∼12 de novo genes per million year in mouse. Contrary to a previous study (Wilson et al. 2017), I found no evidence supporting the preadaptation hypothesis of de novo gene formation. Nearly half of the de novo genes confirmed in this study are within older genes, indicating that co-option of preexisting regulatory regions and a higher GC content may facilitate the origin of novel genes.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted March 26, 2018.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
From de novo to ‘de nono’: most novel protein coding genes identified with phylostratigraphy represent old genes or recent duplicates
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
From de novo to ‘de nono’: most novel protein coding genes identified with phylostratigraphy represent old genes or recent duplicates
Claudio Casola
bioRxiv 287193; doi: https://doi.org/10.1101/287193
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
From de novo to ‘de nono’: most novel protein coding genes identified with phylostratigraphy represent old genes or recent duplicates
Claudio Casola
bioRxiv 287193; doi: https://doi.org/10.1101/287193

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (2409)
  • Biochemistry (4757)
  • Bioengineering (3300)
  • Bioinformatics (14584)
  • Biophysics (6591)
  • Cancer Biology (5132)
  • Cell Biology (7384)
  • Clinical Trials (138)
  • Developmental Biology (4327)
  • Ecology (6826)
  • Epidemiology (2057)
  • Evolutionary Biology (9843)
  • Genetics (7309)
  • Genomics (9471)
  • Immunology (4509)
  • Microbiology (12597)
  • Molecular Biology (4904)
  • Neuroscience (28113)
  • Paleontology (198)
  • Pathology (799)
  • Pharmacology and Toxicology (1372)
  • Physiology (1996)
  • Plant Biology (4452)
  • Scientific Communication and Education (970)
  • Synthetic Biology (1293)
  • Systems Biology (3894)
  • Zoology (718)