3GOLD: optimized Levenshtein distance for clustering third-generation sequencing data.

ABSTRACT:

Background

Third-generation sequencing offers some advantages over next-generation sequencing predecessors, but with the caveat of harboring a much higher error rate. Clustering-related sequences is an essential task in modern biology. To accurately cluster sequences rich in errors, error type and frequency need to be accounted for. Levenshtein distance is a well-established mathematical algorithm for measuring the edit distance between words and can specifically weight insertions, deletions and substitutions. However, there are drawbacks to using Levenshtein distance in a biological context and hence has rarely been used for this purpose. We present novel modifications to the Levenshtein distance algorithm to optimize it for clustering error-rich biological sequencing data.

Res

SUBMITTER: Logan R 

PROVIDER: S-EPMC8934446 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature