CRISPRtracrRNA

CRISPRtracrRNA predicts trans-activating CRISPR RNAs (tracrRNAs) associated with Type II (Cas9) and Type V (Cas12) CRISPR-Cas systems to identify candidates involved in crRNA maturation and interference.

Key Features:

• End-to-End Prediction Pipeline: Integrates multiple sources of evidence to predict tracrRNA candidates across genomes.
• Structural Model Development: Uses covariance models derived from sequence-structure alignments of experimentally validated tracrRNAs to identify candidates.
• Terminator Signal Detection: Detects transcription terminator signals associated with predicted tracrRNAs as additional evidence.
• RNA-RNA Interaction Analysis: Assesses interactions between the CRISPR array repeat and the 5'-part of the tracrRNA to refine candidate selection.
• Repeat Detection via Machine Learning: Employs a machine learning-based repeat detection approach (CRISPRidenify) to locate CRISPR repeats in genomic sequences.
• Cas Effector Protein Association: Identifies cassettes containing Cas9 and Cas12 effector proteins to associate tracrRNA candidates with Type II and Type V systems.

Scientific Applications:

• Genome engineering: Enables identification of tracrRNAs relevant to CRISPR-Cas9 and Cas12 systems used in genome editing workflows.
• CRISPR system discovery and characterization: Facilitates discovery and characterization of novel CRISPR loci and their associated tracrRNAs across diverse genomes.

Methodology:

Combines covariance models from sequence-structure alignments of validated tracrRNAs, terminator signal identification, RNA-RNA interaction analysis between CRISPR array repeats and tracrRNA 5'-parts, machine learning-based repeat detection (CRISPRidenify), and identification of Cas9/Cas12 effector protein cassettes.