Generator based approach to analyze mutations in genomic datasets.

ABSTRACT: In contrast to the conventional approach of directly comparing genomic sequences using sequence alignment tools, we propose a computational approach that performs comparisons between sequence generators. These sequence generators are learned via a data-driven approach that empirically computes the state machine generating the genomic sequence of interest. As the state machine based generator of the sequence is independent of the sequence length, it provides us with an efficient method to compute the statistical distance between large sets of genomic sequences. Moreover, our technique provides a fast and efficient method to cluster large datasets of genomic sequences, characterize their temporal and spatial evolution in a continuous manner, get insights into the locality sensitive informati

SUBMITTER: Jain S 

PROVIDER: S-EPMC8548350 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature