A Computational Framework for Pattern Detection on Unaligned Sequences: An Application on SARS-CoV-2 Data.

ABSTRACT: The exponential growth of genome sequences available has spurred research on pattern detection with the aim of extracting evolutionary signal. Traditional approaches, such as multiple sequence alignment, rely on positional homology in order to reconstruct the phylogenetic history of taxa. Yet, mining information from the plethora of biological data and delineating species on a genetic basis, still proves to be an extremely difficult problem to consider. Multiple algorithms and techniques have been developed in order to approach the problem multidimensionally. Here, we propose a computational framework for identifying potentially meaningful features based on k-mers retrieved from unaligned sequence data. Specifically, we have developed a process which makes use of unsupervised learni

SUBMITTER: Pechlivanis N 

PROVIDER: S-EPMC8194296 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature