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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Updated: Jun 12, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Identifying gene clusters within localized regions in multiple genomes.

Qingwu Yang¹, Gangman Yi, Fenghui Zhang

¹Department of Computer Science and Engineering, Texas A&M University, College Station, Texas 77843-3112, USA.

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

|May 27, 2010

Summary

This study introduces a novel method for identifying gene clusters in multiple genomes by constraining cluster size, not just gene distance. The developed algorithm efficiently finds biologically relevant and functionally enriched gene clusters in bacterial and yeast genomes.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Comparative genomics
Bioinformatics
Computational biology

Background:

Studying genome evolution often involves clustering orthologous genes across multiple genomes.
Existing methods typically focus on minimizing the distance between adjacent genes within a cluster.
A limitation of current approaches is the difficulty in comparing clusters of varying sizes.

Purpose of the Study:

To develop a new formulation for gene cluster identification based on constraining the overall cluster size.
To create statistical significance estimates for direct comparison of gene clusters irrespective of their size.
To implement practical algorithms for identifying gene clusters in complex genomic scenarios, including paralogs and incomplete cluster representation.

Main Methods:

Investigated a restricted version of the problem with strictly ordered orthologous genes, solvable in polynomial time.
Developed exact algorithms for the unrestricted problem, accommodating paralogs and genes appearing in multiple orthologous groups.
Applied the algorithms to bacterial (Bacillus subtilis, Streptococcus pyogenes, Streptococcus pneumoniae, Clostridium acetobutylicum) and yeast (Saccharomyces cerevisiae, Saccharomyces paradoxus, Saccharomyces mikatae, Saccharomyces bayanus) genomes.

Main Results:

The algorithm successfully identified biologically relevant gene clusters in four bacterial genomes.
Significantly more functionally enriched gene clusters were identified in four yeast genomes compared to previous algorithms.
The developed statistical significance estimates allow for direct comparison of clusters of different sizes.

Conclusions:

The new approach provides a robust method for gene cluster identification and comparison in comparative genomics.
The developed algorithms are effective in handling complex genomic data and identifying functionally important gene groups.
A software tool (GCFinder) and identified gene clusters are publicly available for further research.