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OLGenie: Estimating Natural Selection to Predict Functional Overlapping Genes.

Chase W Nelson^1,2, Zachary Ardern³, Xinzhu Wei^4,5

¹Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY.

Molecular Biology and Evolution

|April 4, 2020

Summary

We developed OLGenie, a new method to detect purifying selection in overlapping genes (OLGs). This tool accurately identifies functional constraint in genes that share nucleotide sites, aiding genome annotation and evolutionary studies.

Keywords:

antisense protein (asp) gene d N/dS gene prediction genome annotation human immunodeficiency virus-1 open reading frame overlapping gene (OLG)purifying (negative) selection

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Area of Science:

Genomics
Evolutionary Biology
Bioinformatics

Background:

Purifying selection, detected by dN/dS ratio, is crucial for functional biological sequences.
Overlapping genes (OLGs) pose challenges for standard dN/dS analysis due to frame-dependent substitution effects.

Purpose of the Study:

To develop a scalable method, OLGenie, for estimating functional constraint in overlapping genes.
To accurately differentiate true OLGs from non-OLGs and identify genes under purifying selection.

Main Methods:

OLGenie modifies the Wei-Zhang method for dN/dS estimation in OLGs.
The method was assessed using simulations and viral genomic data, including 58 OLGs and 176 non-OLGs.

Main Results:

OLGenie demonstrated low false-positive rates in simulations.
The tool showed good discriminatory ability between OLGs and non-OLGs.
Significant purifying selection was detected in HIV-1's putative antisense protein gene.

Conclusions:

OLGenie provides a reliable approach for studying functional constraint in OLGs.
The software can aid in the identification of known and novel OLGs in genome annotation.
This method advances the study of gene evolution in overlapping genomic regions.