CRISPRclassify: Repeat-Based Classification of CRISPR Loci.

ABSTRACT: Detection and classification of CRISPR-Cas systems in metagenomic data have become increasingly prevalent in recent years due to their potential for diverse applications in genome editing. Traditionally, CRISPR-Cas systems are classified through reference-based identification of proximate cas genes. Here, we present a machine learning approach for the detection and classification of CRISPR loci using repeat sequences in a cas-independent context, enabling identification of unclassified loci missed by traditional cas-based approaches. Using biological attributes of the CRISPR repeat, the core element in CRISPR arrays, and leveraging methods from natural language processing, we developed a machine learning model capable of accurate classification of CRISPR loci in an ext

SUBMITTER: Nethery MA 

PROVIDER: S-EPMC8392126 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature