Detecting actively translated open reading frames in ribosome profiling data.

Area of Science:

• Molecular Biology
• Genomics
• Proteomics

Background:

• RNA sequencing quantifies gene expression from transcription to protein synthesis.
• Ribosome profiling (Ribo-seq) maps ribosome positions across the transcriptome, indicating active translation.

Purpose of the Study:

• To develop a rigorous statistical method, RiboTaper, for identifying translated regions using Ribo-seq data.
• To create a comprehensive map of translation and identify novel peptide-encoding regions.

Main Methods:

• Developed RiboTaper, a statistical approach leveraging the three-nucleotide periodicity in Ribo-seq data.
• Applied RiboTaper to deep Ribo-seq data from HEK293 cells.
• Validated novel peptide products using mass spectrometry.

Main Results:

• Generated an extensive translation map covering over 11,000 protein-coding genes.
• Identified distinct ribosomal signatures for hundreds of upstream open reading frames (ORFs) and ORFs in noncoding genes (ncORFs).
• Mass spectrometry confirmed RiboTaper's proteome coverage and validated dozens of novel peptide products, with few long noncoding RNAs encoding stable polypeptides.

Conclusions:

• RiboTaper is a powerful tool for comprehensive, de novo identification of actively used ORFs from Ribo-seq data.
• The study provides an extensive map of active translation and reveals novel insights into the coding potential of the genome, including ncORFs.