CRISPRloci

CRISPRloci annotates CRISPR-Cas systems in prokaryotic genomes, providing comprehensive identification of CRISPR arrays, leader sequences, cas gene classification, Cas subtyping, cassette boundaries, repeat structure and orientation assessment, virus–host interaction signals, and self-targeting potential.

Key Features:

• CRISPR array prediction: Accurately predicts CRISPR arrays in their correct orientation across prokaryotic genomes.
• Leader sequence definition: Identifies and defines leader sequences for each CRISPR locus.
• cas gene annotation and classification: Provides unambiguous annotation and classification of cas genes associated with each CRISPR system.
• Cas subtyping: Determines Cas subtypes for annotated Cas proteins.
• Cassette boundary determination: Delineates cassette boundaries for CRISPR-Cas loci.
• Repeat structure and orientation assessment: Evaluates repeat sequence accuracy and orientation and confirms leader sequence placement.
• Virus–host interaction inference: Identifies signals indicative of virus–host interactions associated with CRISPR loci.
• Self-targeting potential detection: Detects potential self-targeting spacers indicative of autoimmunity risks.
• Machine Learning integration: Integrates advanced Machine Learning algorithms for prediction and annotation tasks.
• Output format: Exports predictions in GFF format for integration with genome browsers and downstream analysis.

Scientific Applications:

• CRISPR-Cas system characterization: Comprehensive annotation of CRISPR arrays, leaders, and cas genes to study structure and function of CRISPR-Cas systems.
• Evolutionary and comparative analyses: Comparative analysis of Cas subtypes, cassette boundaries, and repeat sequences to investigate evolutionary dynamics.
• Virus–host interaction studies: Detection of signals linking CRISPR loci to viral exposure and host immune responses.
• Self-targeting and autoimmunity assessment: Identification of self-targeting spacers to assess potential autoimmunity within genomes.
• Genome browser integration and downstream analysis: GFF-formatted outputs enable integration with genome browsers and further locus-level inspection.

Methodology:

Integrates advanced Machine Learning algorithms for prediction and annotation and exports results in GFF format.