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A new DNA sequence entropy-based Kullback-Leibler algorithm for gene clustering.

Houshang Dehghanzadeh¹, Mostafa Ghaderi-Zefrehei², Seyed Ziaeddin Mirhoseini³

¹Department of Animal Science Research, Guilan Agricultural and Natural Resources Research and Education Center, AREEO, Rasht, Iran.

Journal of Applied Genetics

|January 26, 2020

Summary

This study introduces a novel gene clustering method using information theory, entropy, and Kullback-Leibler divergence. The approach efficiently groups genes, offering accurate and fast results for biological pathway analysis and improving genomic annotation.

Keywords:

Dairy cattle Gene clustering Information theory Kullback-Leibler divergence

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Area of Science:

Information theory
Computational biology
Bioinformatics

Background:

Entropy quantifies uncertainty in information sets.
Gene and exon set entropy calculations are crucial for understanding biological data.
Existing methods may have limitations in handling gene sequence length and memory requirements.

Purpose of the Study:

To develop and evaluate a new gene clustering method based on information theory principles.
To assess the efficiency and accuracy of the proposed method for gene annotation and pathway analysis.
To address limitations of existing gene clustering techniques regarding sequence length and memory usage.

Main Methods:

Calculated entropy for gene and exon sets (orders 1-4).
Computed Kullback-Leibler divergence based on relative entropy.
Applied 7 clustering algorithms (single, complete, average, weighted, centroid, median, K-means) and aggregated results using AdaBoost.
Validated findings using the GeneMANIA prediction server.

Main Results:

The proposed clustering method produced correct, logical, and fast results for gene metabolic pathway investigation.
The method effectively considered gene length and content without alignment disadvantages.
It demonstrated low memory requirements for large sequences.
AdaBoost aggregation and GeneMANIA validation confirmed the method's efficacy.

Conclusions:

The developed information-theory-based gene clustering method is efficient and accurate.
It offers advantages over traditional methods, particularly for large datasets and weak genomic annotations.
The method holds potential for grouping biologically relevant gene sets and improving gene annotation predictions.