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Updated: May 27, 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

Algorithm for large-scale clustering across multiple genomes.

Gangman Yi¹, Jaehee Jung

¹Samsung Electronics Co., Ltd. 416, Maetan 3-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do, 442-742 Korea.

|November 30, 2011

Summary

This summary is machine-generated.

We developed an efficient gene clustering algorithm for analyzing hundreds of genomes, enabling large-scale studies of gene function, genome evolution, and operon formation. This method provides deeper biological insights into evolutionary relationships and gene organization across diverse species.

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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Published on: February 15, 2017

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Last Updated: May 27, 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

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Identifying homologous gene clusters is crucial for understanding gene function and genome evolution.
Existing computational methods for gene clustering are often limited to small datasets.
Large-scale comparative genomics requires efficient algorithms applicable to numerous genomes.

Purpose of the Study:

To develop an efficient gene clustering algorithm for analyzing hundreds of genomes simultaneously.
To enable large-scale studies of evolutionary relationships among gene clusters.
To investigate operon formation, destruction, and gene rearrangements across diverse genomes.

Main Methods:

Developed a novel, efficient gene clustering algorithm.
Applied the algorithm to a dataset comprising hundreds of genomes.
Analyzed evolutionary relationships, operon structures, and gene orientations within large-scale clusters.

Main Results:

The proposed algorithm efficiently clusters genes across hundreds of genomes.
Facilitated large-scale analysis of gene cluster evolution and operon dynamics.
Revealed insights into operon occurrences, gene orientations, and gene rearrangements.

Conclusions:

Analyzing gene clusters across hundreds of genomes provides significant biological insights.
The developed algorithm supports scalable evolutionary and functional genomic studies.
Enhanced understanding of operon biology and genome evolution is achievable through large-scale comparative analysis.