Multiple genome analytics framework: The case of all SARS-CoV-2 complete variants

Area of Science:

• Bioinformatics
• Computational Biology
• Computer Science

Background:

• Genomic analysis requires significant computational resources.
• Efficient pattern detection and string matching are crucial for bioinformatics.
• Existing tools often demand substantial computational power.

Purpose of the Study:

• To present a novel Multiple Genome Analytics Framework.
• To enable efficient, resource-minimal analysis of multiple genome sequences.
• To facilitate advanced pattern detection and bioinformatics tasks.

Main Methods:

• Developed a framework combining data structures and algorithms for text mining and pattern detection.
• Implemented advanced algorithms with O(nlogn) time and space complexity.
• Utilized a dataset of over 300,000 SARS-CoV-2 genome sequences for validation.

Main Results:

• Successfully detected all repeated patterns within multiple genome sequences.
• Demonstrated scalability, agility, and efficiency of the framework.
• Enabled subsequent analyses like sequence alignment and primer detection.

Conclusions:

• The Multiple Genome Analytics Framework efficiently addresses computational biology challenges.
• Minimal resources are needed for comprehensive multi-genome pattern analysis.
• The framework supports diverse downstream applications in genomic research.