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Updated: May 12, 2026

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

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Published on: December 22, 2017

Ancestral genome organization: an alignment approach.

Patrick Holloway¹, Krister Swenson, David Ardell

¹Département d'Informatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Montreal, Canada.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|April 9, 2013

Summary

This study introduces a new comparative genomics method to reconstruct ancestral genomes and evolutionary paths using gene sequences. The approach accurately models gene duplication and loss, providing insights into bacterial genome evolution.

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Last Updated: May 12, 2026

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Area of Science:

Genomics
Computational Biology
Evolutionary Biology

Background:

Inferring ancestral genome organization is crucial for understanding evolutionary trajectories.
Existing methods often simplify evolutionary models, neglecting gene duplication and loss events.

Purpose of the Study:

To develop a comparative genomics approach for inferring ancestral genome organization and evolutionary scenarios.
To model genome evolution considering gene duplication, loss, and rearrangements.

Main Methods:

Developed a methodology based on gene sequences with duplicates, extending models to include content-modifying operations and inversions.
Utilized pseudo-boolean linear programming for an exact algorithm to find optimal alignments and duplication-loss scenarios.
Applied the algorithm to analyze the evolution of stable RNA gene content in Bacillus genomes.

Main Results:

The developed algorithm demonstrates low running times on real and synthetic datasets, despite worst-case exponential complexity.
Revealed insights into ribosomal RNA (rRNA) gene proliferation rates and their impact on transfer RNA (tRNA) gene content.
Provided evidence for tRNA class conversion during the evolution of Bacillus genomes.

Conclusions:

The new computational approach enables accurate reconstruction of ancestral genomes and evolutionary histories.
The findings offer significant biological insights into the dynamics of stable RNA gene evolution in bacteria.