ORForise

ORForise provides a systematic framework to evaluate and compare Open Reading Frame (ORF) prediction tools, identifying prediction biases and informing selection for genome and metagenome annotation.

Key Features:

• Evaluation Framework: Uses 12 primary and 60 secondary metrics to assess ORF prediction performance.
• Tool Assessment: Evaluates 15 widely-used ab initio and model-based ORF prediction tools across diverse genomes.
• Performance Dependency: Quantifies genome-dependent performance, showing that no single tool consistently outperforms others across all genomes or metrics.
• Conflict Resolution: Applies a data-driven methodology to identify and reconcile conflicting gene collections produced by different prediction tools.

Scientific Applications:

• Genome and metagenome annotation: Supports accurate annotation of novel genomes and metagenomes.
• Refinement of historical annotations: Enables refinement of historical annotations to improve representation of prokaryotic diversity and function.
• Tool selection and workflow optimization: Provides a replicable framework to select appropriate ORF prediction tools and optimize analytical workflows for genomic database contributions.

Methodology:

Evaluation uses 12 primary and 60 secondary metrics to compare 15 ab initio and model-based ORF prediction tools and performs context-specific, data-led performance assessment across genomes.