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Updated: Jun 3, 2026

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Published on: June 16, 2011

Composition vector method based on maximum entropy principle for sequence comparison.

Raymond H Chan¹, Tony H Chan, Hau Man Yeung

¹The Chinese University of Hong Kong, Hong Kong.

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|March 9, 2011

Summary

This summary is machine-generated.

This study enhances sequence comparison using the composition vector (CV) method by applying entropy principles. The new entropy-maximizing formula improves accuracy in sequence analysis, outperforming existing methods on simulated and real biological data.

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A Practical Guide to Phylogenetics for Nonexperts

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Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

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A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

The composition vector (CV) method offers a simpler, alignment-free approach to sequence comparison.
Existing probabilistic formulas like Hao's and Yu's have limitations in sequence analysis.
Quantifying non-random patterns in sequences is crucial for accurate comparison.

Purpose of the Study:

To improve existing composition vector (CV) formulas using the entropy principle.
To develop a novel entropy-maximizing formula for enhanced sequence comparison.
To validate the accuracy and performance of the new formula against established methods.

Main Methods:

Applied the entropy principle to optimize existing composition vector (CV) formulas.
Derived a closed-form solution for the entropy maximization problem.
Generated a new entropy-maximizing formula from Yu's formula.
Validated Hao's formula as an entropy maximizer.

Main Results:

The new entropy-maximizing formula demonstrates superior accuracy on simulated data across various evolutionary models.
The novel formula correctly classifies tetrapod 18S rRNA sequences, grouping birds and reptiles, unlike Hao's and Yu's formulas.
The improved formula shows higher accuracy than Hao's and Yu's formulas, even with small datasets.

Conclusions:

The entropy-maximizing composition vector (CV) formula offers a significant advancement in sequence comparison accuracy.
This method provides more reliable phylogenetic insights, particularly for complex biological datasets.
The enhanced CV method is robust and accurate, outperforming previous probabilistic approaches.