Translation Signature Scores: Data-Driven Approach to Assess Evidence for Active Translation.

Area of Science:

• Genomics
• Proteomics
• Molecular Biology

Background:

• Small open reading frames (smORFs) encode microproteins with potential physiological roles, but are difficult to identify and often excluded from genome annotations.
• Ribosome profiling (Ribo-seq) has revealed thousands of novel ORFs, but distinguishing true smORFs from noise is challenging due to data variability.

Purpose of the Study:

• To describe a robust method, the translation signature approach, for identifying and prioritizing translated smORFs.
• To provide a framework for analyzing large-scale ribosome profiling data to uncover novel microproteins.

Main Methods:

• Utilized pooled ribosome profiling (Ribo-seq) data for enhanced visualization of translation in individual ORFs.
• Developed translation signature scores incorporating P-sites in frame, uniformity, and drop-off metrics.
• Used annotated protein-coding ORFs as a reference to establish expected score ranges for active translation.

Main Results:

• The translation signature approach enables clearer visualization and quantification of translation evidence in smORFs.
• Prioritized novel ORFs exhibiting translation signature scores indicative of active translation.
• Established a web resource for accessing a default set of smORFs identified using this workflow.

Conclusions:

• The translation signature approach offers a robust framework for identifying microproteins encoded by smORFs.
• This method enhances the accuracy of genome annotation by reliably detecting novel translated regions.
• Increasing availability of high-quality Ribo-seq data will further empower this approach for microprotein discovery.