Pattern Detection in Multiple Genome Sequences with Applications: The Case of All SARS-CoV-2 Complete Variants

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 the need for fast and accurate analyses. Medicines and, mostly, vaccines must be altered to adapt and efficiently address mutations. The need of computational tools for genomic analysis, such as sequence alignment, is very important, although, in most cases the resources and computational power needed is vast. The presented data structures and algorithms, specifically built for text mining and pattern detection, can help to address efficiently several bioinformatics problems. With a single execution of advanced algorithms, with limited space and time complexity, it is possible to acquire knowledge on all repeated patterns that exist in multiple genome sequences and this information can be used for further meta analyses. The potentials of the presented solutions are demonstrated with the analysis of more than 55,000 SARS-CoV-2 genome sequences (collected on March 10, 2021) and the detection of all repeated patterns with length up to 60 nucleotides in these sequences, something practically impossible with other algorithms due to its complexity. These results can be used to help provide answers to questions such as all variants common patterns, sequence alignment, palindromes and tandem repeats detection, genome comparisons, etc.