DE Novo emerged gene SEarch in Eukaryotes with DENSE

Abstract

The discovery of de novo emerged genes, originating from previously noncoding DNA regions, challenges traditional views of species evolution. Indeed, the hypothesis of neutrally evolving sequences giving rise to functional proteins is highly unlikely. This conundrum has sparked numerous studies to quantify and characterize these genes, aiming to understand their functional roles and contributions to genome evolution. Yet, no fully automated pipeline for their identification is available. Therefore, we introduce DENSE (DE Novo emerged gene Search), an automated Nextflow pipeline based on two distinct steps: detection of Taxonomically Restricted Genes (TRGs) through phylostratigraphy, and filtering of TRGs for de novo emerged genes via genome comparisons and synteny search. DENSE is available as a user-friendly command-line tool, while the second step is accessible through a web server upon providing a list of TRGs. Highly flexible, DENSE provides various strategy and parameter combinations, enabling users to adapt to specific configurations or define their own strategy through a rational framework, facilitating protocol communication, and study interoperability. We apply DENSE to seven model organisms, exploring the impact of its strategies and parameters on de novo gene predictions. This thorough analysis across species with different evolutionary rates reveals useful metrics for users to define input datasets, identify favorable/unfavorable conditions for de novo gene detection, and control potential biases in genome annotations. Additionally, predictions made for the seven model organisms are compiled into a requestable database, that we hope will serve as a reference for de novo emerged gene lists generated with specific criteria combinations. Significance StatementThe identification and classification of de novo genes, which originate from noncoding regions of DNA, remain an ongoing challenge in genomics research. While various approaches have been employed for their identification, the lack of a standardized protocol has resulted in varying lists of de novo genes across studies. This study introduces a novel tool: DENSE, that formalizes the common practices used in the field into a comprehensive and automated pipeline. DENSE streamlines the identification of taxonomically restricted genes, homology searches, and synteny analysis. This standardized methodology aims to enhance the accuracy and reliability of de novo gene identification, fostering a deeper understanding of the evolutionary mechanisms that drive gene birth and shape the genetic diversity of organisms.