Multiple genome analytics framework: 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 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 (repeated) pattern detection, that can help to efficiently address several computational biology and bioinformatics problems, concurrently, with minimal resources. A single execution of advanced algorithms, with space and time complexity O(nlogn), is enough to acquire knowledge on all repeated patterns that exist in multiple genome sequences and this information can be used as input by meta-algorithms for further meta-analyses. For the proof of concept and technology of the proposed Framework scalability, agility and efficiency, a publicly available dataset of more than 300,000 SARS-CoV-2 genome sequences from the National Center for Biotechnology Information has been used for the detection of all repeated patterns. These results have been used by newly introduced algorithms to provide answers to questions such as common patterns among all variants, sequence alignment, palindromes and tandem repeats detection, different organism genome comparisons, polymerase chain reaction primers detection, etc.