ORFik

ORFik analyzes translation and transcript-level regulatory features from high-throughput sequencing assays, extending GenomicRanges to operate on genome and transcriptome coordinates to process, quantify, and visualize translational dynamics with emphasis on initiation, ribosome scanning, and elongation.

Key Features:

• GenomicRanges extension: Extends GenomicRanges to operate on both genome and transcriptome coordinates for integrated analyses.
• Supported sequencing modalities: Supports Ribo-seq, RNA-seq, TCP-seq/RCP-seq, and CAGE for multi-modal translational profiling.
• Transcript start site reassignment: Reassigns transcript start sites using CAGE data.
• uORF detection and annotation: Detects upstream open reading frames (uORFs) and annotates translated coding and non-coding regions.
• Translation metrics: Quantifies over 30 literature-derived translation metrics capturing scanning efficiency, initiation context, ribosome occupancy, elongation behavior, and translational efficiency relative to RNA abundance.
• Computational performance: Implements optimized C++ code, data.table backends, and accelerated extensions to core Bioconductor functions for large-scale analyses.
• ORF prediction and P-site mapping: Performs automated ORF prediction across whole genomes and read-shifting corrections required for accurate P-site mapping.
• Processing and visualization: Provides processing, quantification, and visualization of translational dynamics with emphasis on initiation, scanning, and elongation.

Scientific Applications:

• 5′ UTR architecture analysis: Enables detailed exploration of 5′ UTR architecture and its effects on translation.
• Tissue-specific translational dynamics: Characterizes tissue-specific dynamics in translational regulation.
• uORF functional impact: Assesses the functional impact of uORFs on protein output.
• Multi-omics translational characterization: Integrates Ribo-seq, RNA-seq, TCP-seq/RCP-seq, and CAGE to comprehensively characterize translational regulation.

Methodology:

Extends GenomicRanges to genome and transcriptome coordinates; reassigns transcript start sites using CAGE; detects uORFs and annotates translated regions; performs automated ORF prediction across whole genomes and read-shifting corrections for accurate P-site mapping; quantifies >30 translation metrics; and uses optimized C++ implementations, data.table backends, and accelerated Bioconductor function extensions for processing, quantification, and visualization.