CRISPRmap web server

CRISPRmap web server predicts crRNA orientation and classifies conserved CRISPR repeat families to analyze CRISPR-Cas system conservation and diversity.

Key Features:

• Orientation prediction: Predicts the strand from which crRNAs are processed by leveraging repeat conservation within CRISPR loci.
• Graph-kernel machine learning: Implements a graph kernel that processes repeat sequence and mutation information to learn higher-order correlations and predict repeat orientation, trained on a curated dataset of over 4,500 CRISPR loci (0.95 AUC ROC).
• Conserved sequence and structure detection: Detects conserved repeat sequence families and potential structural motifs associated with Cas-endoribonucleases.
• CRISPR classification: Performs comprehensive clustering analysis of a large CRISPR dataset to classify CRISPR systems and identify novel conserved sequence families.
• Evolutionary analysis: Provides insights into evolutionary relationships among CRISPR-Cas subtypes and patterns consistent with horizontal gene transfer between bacteria and archaea.
• Repeat-spacer architecture analysis: Analyzes repeat and variable-length spacer organization within CRISPR loci to support conservation and family assignment.

Scientific Applications:

• crRNA strand determination: Determining the crRNA-encoding strand to support accurate annotation of CRISPR loci.
• Leader, protospacer and PAM analysis: Detection of leader regions, identification of protospacers on invading genetic elements, and characterization of protospacer-adjacent motifs (PAMs).
• Classification and diversity mapping: Assigning CRISPR loci to conserved sequence families and mapping sequence and structure similarities for CRISPR-Cas diversity studies.
• Evolutionary studies: Investigating evolutionary relationships and horizontal transfer among bacterial and archaeal CRISPR-Cas systems.

Methodology:

Uses a graph kernel that processes repeat sequence and mutation information; trained on a curated dataset of >4,500 CRISPR loci (0.95 AUC ROC); and applies comprehensive clustering analysis of the compiled CRISPR dataset to identify conserved families and structural motifs.