Circall

Circall detects circular RNAs (circRNAs) from paired-end RNA-sequencing data while controlling false discoveries to support large-scale circRNA analyses.

Key Features:

• Robust False Discovery Rate Control: Employs a multidimensional local false discovery rate that leverages circRNA length and expression levels to distinguish true circRNAs from false positives among reads supporting back-splicing junctions (BSJs).
• Quasi-mapping-based Efficiency: Uses a quasi-mapping algorithm for fast RNA read alignments to improve processing speed on large datasets.
• Evaluated Performance: Demonstrated higher sensitivity and precision than existing methods on simulated and experimental human cell-line datasets while offering faster processing for extensive datasets.

Scientific Applications:

• Large-scale circRNA profiling: Enables comprehensive detection of circRNAs across numerous samples for genomics and transcriptomics studies.
• Biomarker discovery and disease research: Supports identification of circRNAs as potential biomarkers for diagnosis, prognosis, or therapeutic targets in diseases including cancer, cardiovascular disorders, and autoimmune conditions.
• Functional circRNA analysis: Provides accurate circRNA calls for downstream investigations of circRNA roles in health and disease.

Methodology:

Processes paired-end RNA-seq reads, identifies reads supporting back-splicing junctions (BSJs), aligns reads using a quasi-mapping algorithm, and applies a multidimensional local false discovery rate model that incorporates circRNA length and expression to control false positives.