Computational Methods for Tracing the Evolutionary History of Human Microproteins Encoded by Intronless Genes.

Area of Science:

• Genomics
• Evolutionary Biology
• Biochemistry

Background:

• MicroProteins (miPs) are small functional proteins from short open reading frames (sORFs) crucial for posttranslational regulation.
• Intronless genes (IGs) are linked to regulatory functions, but their role in encoding miPs and their evolutionary history are underexplored.

Purpose of the Study:

• To introduce a computational framework for investigating the evolution and functional roles of miPs encoded by IGs in vertebrates.
• To integrate bioinformatics tools for comprehensive analysis of miPs from IGs.

Main Methods:

• Utilized miPFinder2 to identify potential miPs from annotated small peptides.
• Employed IGFinder to classify IGs based on genomic and UTR features.
• Applied REvolutionH-tl for reconstructing evolutionary histories, including orthologs and gene/species trees.

Main Results:

• The framework enables detailed structural, functional, and evolutionary reconstruction of miPs encoded by IGs.
• Provides insights into the evolutionary trajectories and regulatory roles of these miPs.

Conclusions:

• The integrative computational approach offers significant advancements in functional genomics, evolutionary biology, and disease research.
• Highlights the importance of exploring miPs from IGs for understanding cellular regulation and disease mechanisms.