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Predicting evolutionary targets and parameters of gene deletion from expression data.

Andre Luiz Campelo Dos Santos1, Michael DeGiorgio1, Raquel Assis1,2

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Gene deletion, often overlooked, can drive adaptation by removing redundant genes. Our new machine learning tool, CLOUDe, predicts deleted gene functions, revealing deletion primarily targets unique genes, aiding evolutionary studies.

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Area of Science:

  • Evolutionary biology
  • Genomics
  • Bioinformatics

Background:

  • Gene deletion is traditionally viewed as a nonadaptive process removing genomic redundancy.
  • Emerging evidence suggests deletion can drive adaptation through the "less-is-more" hypothesis.
  • Understanding the functional nature of deleted genes is crucial for evolutionary studies.

Purpose of the Study:

  • To develop machine learning methods for predicting the functional roles of genes targeted by deletion events.
  • To differentiate between redundant and unique functions among deleted genes.
  • To investigate the evolutionary parameters influencing gene deletion.

Main Methods:

  • Development of CLOUDe, a machine learning suite utilizing expression data to predict gene deletion targets.
  • Modeling gene expression evolution using an Ornstein-Uhlenbeck process.
  • Employing multi-layer neural networks, gradient boosting, random forests, and support vector machines.

Main Results:

  • CLOUDe accurately differentiates between redundant and unique gene functions and estimates evolutionary parameters.
  • The neural network architecture within CLOUDe demonstrated optimal performance.
  • Application to *Drosophila* data indicated that gene deletion predominantly targets unique functions, particularly those involved in protein deubiquitination.

Conclusions:

  • CLOUDe provides a powerful tool for analyzing the evolutionary impact of gene deletion.
  • Gene deletion plays a significant role in functional evolution and adaptation.
  • The findings highlight the importance of unique gene functions in the context of deletion-driven evolution.