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Updated: Aug 15, 2025

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

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Published on: August 12, 2019

A Novel Information-Theory-Based Genetic Distance That Approximates Phenotypic Differences.

David S Campo¹, Alexander Mosa², Yury Khudyakov¹

¹Molecular Epidemiology & Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

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

|January 5, 2023

Summary

We developed a new method, Mutual Information and Entropy H (MIH) distance, to better measure phenotypic relatedness between biological sequences. MIH accurately distinguishes between sequences with similar or different functions, proving useful for biological studies.

Keywords:

Shannon entropy categorical variables genetic distance machine learning mutual information natural and artificial selection phenotype protein

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Measuring phenotypic relatedness from genetic sequences is complex due to the genotype-phenotype relationship.
Existing distance measures may not fully capture structural and functional similarities crucial for accurate biological assessments.

Purpose of the Study:

Introduce a novel distance metric, Mutual Information and Entropy H (MIH), for categorical sequence data.
Evaluate MIH's efficacy in assessing phenotypic similarity and its association with structural/functional properties.

Main Methods:

Developed the MIH distance using an information matrix (IM) based on Shannon entropy and mutual information.
Compared MIH against traditional distances (e.g., Hamming) using experimental and simulated datasets.
Analyzed hepatitis C virus sequences, MHC binding peptides, and simulated RNA secondary structures.

Main Results:

MIH demonstrated superior performance in distinguishing functionally related sequence pairs across multiple datasets.
MIH effectively differentiated between same and different RNA secondary structures, outperforming Hamming distance.
Analysis of in silico generated phenotypes confirmed MIH's strong association with phenotypic differences and robustness to selection and sampling.

Conclusions:

MIH distance is a robust measure that approximates structural and functional distances between biological sequences.
The new metric shows significant potential for applications in evolutionary biology, artificial selection, and phenotypic similarity assessments.