De Novo Gene Emergence: Summary, Classification, and Challenges of Current Methods

A novel mechanism of 'de novo' gene origination from nongenic sequences was first proposed in the early 2000s. Subsequent studies have since provided evidence of 'de novo' gene emergence across all domains of life, revealing its occurrence to be more frequent than initially anticipated. While studies mainly agree on the general concept of 'de novo' emergence from nongenic DNA, the exact methods and definitions for detecting 'de novo' genes differ significantly. Here, we provide a comprehensive step-by-step description of the most commonly used methods for 'de novo' gene detection. In addition, we address the limitations of nomenclature and detection methods and clarify some complex concepts that are sometimes misused. This review is accompanied by the publication of a 'de novo' gene annotation format to standardize the reporting of methodology, enable reproducibility and improve the comparability of datasets.

Publication Details

Journal
Universitäts- und Landesbibliothek Münster
Published
2025-10-23
DOI
https://doi.org/10.17879/31918497459
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

De Novo Gene Emergence: Summary, Classification, and Challenges of Current Methods

Universitäts- und Landesbibliothek Münster
Gene expression and cancer classification
article

De Novo Gene Emergence: Summary, Classification, and Challenges of Current Methods

article en

Abstract

A novel mechanism of 'de novo' gene origination from nongenic sequences was first proposed in the early 2000s. Subsequent studies have since provided evidence of 'de novo' gene emergence across all domains of life, revealing its occurrence to be more frequent than initially anticipated. While studies mainly agree on the general concept of 'de novo' emergence from nongenic DNA, the exact methods and definitions for detecting 'de novo' genes differ significantly. Here, we provide a comprehensive step-by-step description of the most commonly used methods for 'de novo' gene detection. In addition, we address the limitations of nomenclature and detection methods and clarify some complex concepts that are sometimes misused. This review is accompanied by the publication of a 'de novo' gene annotation format to standardize the reporting of methodology, enable reproducibility and improve the comparability of datasets.

Universitäts- und Landesbibliothek Münster
Openalex Percentile: Top 42%
Gene expression and cancer classification
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.