Data stream dataset of SARS-CoV-2 genome.

Area of Science:

• Bioinformatics
• Computational Biology
• Virology

Background:

• The COVID-19 pandemic highlighted the urgent need for rapid analysis of viral genetic material.
• Analyzing virus nucleotide sequences is critical in bioinformatics for understanding viral evolution and developing countermeasures.
• Existing methods require nucleotide sequences to be converted into numerical representations for computational analysis.

Purpose of the Study:

• To present a comprehensive dataset of Data Stream Representations (DSR) for SARS-CoV-2 and other viral nucleotide sequences.
• To facilitate advanced bioinformatics analyses of viral genetic data using numerical stream representations.

Main Methods:

• Generation of four distinct Data Stream Representations (DSR) for viral nucleotide sequences.
• Compilation of a dataset including 1557 SARS-CoV-2 instances, 11540 other viral instances from Virus-Host DB, and 3 Riboviria virus instances from NCBI.
• Utilizing established bioinformatics techniques for sequence transformation and data representation.

Main Results:

• A curated dataset containing diverse DSRs of SARS-CoV-2 and related viruses is now available.
• The dataset provides a foundation for applying data stream algorithms to viral sequence analysis.
• Includes representations for SARS-CoV-2, Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21.

Conclusions:

• The developed dataset is essential for advancing computational approaches in virology and bioinformatics.
• This resource will aid researchers in analyzing viral sequences more efficiently, contributing to pandemic preparedness.
• The numerical representations enable the application of advanced data stream mining techniques to virological data.