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Updated: Feb 17, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
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Machine learning can distinguish orphans that have resulted from sequence divergence beyond recognition.

Emilios Tassios1,2,3, Jori de Leuw4, Christoforos Nikolaou2

  • 1Hellenic Pasteur Institute, 115 21, Athens, Greece.

Bioinformatics Advances
|February 16, 2026
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Summary

We developed a machine learning method to identify species-specific orphan genes that have diverged significantly. This approach analyzes subtle sequence similarity patterns, aiding the discovery of genetic novelty and unique species traits.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Species-specific orphan genes contribute to unique traits but are hard to identify due to extreme sequence divergence.
  • Traditional methods discard non-statistically significant similarity hits, potentially missing divergent homologous genes.

Purpose of the Study:

  • To develop a machine learning approach for identifying highly divergent orphan genes.
  • To investigate the characteristics of these potentially divergent orphan genes.

Main Methods:

  • Simulated diverged orphan protein sequences under various parameters.
  • Trained machine learning classifiers on features from similarity search outputs, using reversed sequences as a negative control.
  • Applied trained classifiers to a set of real orphan genes.

Main Results:

  • Machine learning models achieved up to ~90% accuracy in identifying moderately diverged simulated orphans and ~70% for extremely diverged ones.
  • Approximately 30% of real orphan genes were predicted as divergent.
  • Predicted divergent orphans were found to be shorter and more disordered compared to other orphans.

Conclusions:

  • Subtle patterns in similarity search outputs can reveal highly diverged homologous genes.
  • This method enhances the identification of genetic novelty and aids in understanding the evolution of species-specific traits.
  • The developed models and data are publicly available for further research.