nORFs

nORFs identifies and analyzes novel open reading frames (nORFs) in eukaryotic genomes to assess their translation, evolutionary constraint, and potential implications for human disease.

Key Features:

• Dataset curation: Curates a dataset that includes experimental evidence supporting translation of nORFs from proteomics and deep massively parallel sequencing.
• nORF scope: Detects nORFs largely under 100 codons located in long noncoding RNAs, pseudogenes, untranslated regions (UTRs), and alternative reading frames of canonical protein-coding exons.
• Evolutionary analysis: Employs measures of heritability and selection to identify signals indicative of functional importance among nORFs.
• Variant reinterpretation: Reevaluates genetic variants previously classified as benign or of uncertain significance when those variants occur within nORFs.
• Integration of evidence: Integrates experimental translation evidence with computational analyses to assess pathway-level impacts and disease relevance of nORFs.

Scientific Applications:

• Large-scale annotation: Enables large-scale evaluation and annotation of previously unannotated transcripts and proteins within biological pathways.
• Disease association studies: Supports reinterpretation of disease-associated genetic variants in the context of translated nORFs.
• Functional element discovery: Identifies novel functional elements distributed across lncRNAs, pseudogenes, UTRs, and alternative reading frames that may contribute to human health and disease.
• Therapeutic target prioritization: Prioritizes nORFs as candidate novel therapeutic targets based on evidence of translation and evolutionary constraint.

Methodology:

Curates experimental translation evidence from proteomics and deep massively parallel sequencing and applies computational analyses measuring heritability and selection to identify functionally constrained nORFs and reinterpret genetic variants.