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Updated: Jun 6, 2025

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

DiMA: sequence diversity dynamics analyser for viruses.

Shan Tharanga¹, Eyyüb Selim Ünlü^2,3, Yongli Hu^1,4

¹Centre for Bioinformatics, School of Data Sciences, Perdana University, MAEPS Building, Jalan MAEPS Perdana, Serdang, Kuala Lumpur 50490, Malaysia.

Briefings in Bioinformatics

|November 26, 2024

Summary

This summary is machine-generated.

Sequence diversity poses challenges for viral interventions. DiMA is a new big data tool that analyzes viral sequence diversity dynamics, identifying motifs and variants for better understanding and intervention design.

Keywords:

diversity dynamics genome proteome sequence diversity viruses

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Last Updated: Jun 6, 2025

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

Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'

Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'

Published on: May 26, 2013

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

Area of Science:

Virology
Bioinformatics
Computational Biology

Background:

Viral sequence diversity is a critical hurdle for developing effective diagnostics, prophylactics, and therapeutics.
Understanding sequence diversity dynamics is essential for combating viral infections.

Purpose of the Study:

To introduce DiMA, a novel, big data-ready tool for dissecting viral sequence diversity dynamics.
To provide a quantitative overview of sequence diversity using entropy and k-mer analysis.
To enable detailed analysis of diversity motifs and variants within viral sequences.

Main Methods:

Utilizes Shannon's entropy corrected for size bias with a user-defined k-mer sliding window.
Dissects k-mer positions into diversity motifs: index (predominant), major variants, minor variants, and unique variants.
Incorporates user-defined sequence metadata enrichment for motif analysis.

Main Results:

Demonstrated DiMA's application on large datasets of SARS-CoV-2 Spike protein and HIV-1 pol gene.
Successfully quantified sequence diversity and identified distinct motifs and variant classifications.
Validated DiMA's capability to handle both conserved and highly diverse viral gene sequences.

Conclusions:

DiMA offers a powerful and versatile approach to analyzing viral sequence diversity.
The tool facilitates a deeper understanding of viral evolution and variation.
DiMA is publicly available as a web server, Python library, and command-line client for broad accessibility.