Identity: rapid alignment-free prediction of sequence alignment identity scores using self-supervised general linear models.

ABSTRACT: Pairwise global alignment is a fundamental step in sequence analysis. Optimal alignment algorithms are quadratic-slow especially on long sequences. In many applications that involve large sequence datasets, all what is needed is calculating the identity scores (percentage of identical nucleotides in an optimal alignment-including gaps-of two sequences); there is no need for visualizing how every two sequences are aligned. For these applications, we propose Identity, which produces global identity scores for a large number of pairs of DNA sequences using alignment-free methods and self-supervised general linear models. For the first time, the new tool can predict pairwise identity scores in linear time and space. On two large-scale sequence databases, Identity provided the bes

SUBMITTER: Girgis HZ 

PROVIDER: S-EPMC7850047 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature