Multiple Genome Analytics Framework: The Case of All SARS-CoV-2 Complete Variants
Konstantinos Xylogiannopoulos

TL;DR
The paper introduces a novel framework for efficient, large-scale analysis of SARS-CoV-2 genomes, enabling rapid detection of patterns and repeats across thousands of variants with minimal computational resources.
Contribution
It presents a new data structure and algorithmic framework that can analyze multiple genomes simultaneously with O(nlogn) complexity, improving speed and resource efficiency.
Findings
Analyzed over 300,000 SARS-CoV-2 genomes.
Detected all repeated patterns up to length 60 nucleotides.
Enabled various bioinformatics analyses like pattern detection and genome comparison.
Abstract
Pattern detection and string matching are fundamental problems in computer science and the accelerated expansion of bioinformatics and computational biology have made them a core topic for both disciplines. The SARS-CoV-2 pandemic has made such problems more demanding with hundreds or thousands of new genome variants discovered every week, because of constant mutations, and there is a desperate need for fast and accurate analyses. The requirement for computational tools for genomic analyses, such as sequence alignment, is very important, although, in most cases the resources and computational power required are enormous. The presented Multiple Genome Analytics Framework combines data structures and algorithms, specifically built for text mining and pattern detection, that can help to efficiently address several computational biology and bioinformatics problems concurrently with minimal…
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Taxonomy
TopicsGenomics and Phylogenetic Studies · Machine Learning in Bioinformatics · Bacteriophages and microbial interactions
