Discovering microproteins: making the most of ribosome profiling data

Area of Science:

• Genomics
• Proteomics
• Molecular Biology

Background:

• Traditional gene models rely on cDNA sequencing and long open reading frames (ORFs), often missing small ORFs (smORFs).
• Small ORFs and their encoded proteins remain largely unannotated due to limitations in conventional detection methods.
• Ribosome profiling (Ribo-seq) offers a length-independent approach to distinguish translated regions from untranslated sequences.

Purpose of the Study:

• To review the utility of Ribo-seq data for detecting smORFs.
• To discuss challenges in smORF identification, including data quality, depth, and sparseness.
• To highlight variations in human smORF cataloguing efforts due to differing data and methodologies.

Main Methods:

• Review of Ribo-seq applications in smORF detection.
• Analysis of challenges in identifying smORF translation start and end sites.
• Comparison of human smORF cataloguing initiatives and their discrepancies.

Main Results:

• Ribo-seq is powerful for identifying smORFs, overcoming length limitations of traditional methods.
• Data quality, depth, and sparseness significantly impact the accuracy of smORF start and end site identification.
• Current human smORF reference sets show considerable variation stemming from differences in data, methods, and assumptions.

Conclusions:

• While current smORF reference sets aid hypothesis generation, they are limited by data inconsistencies.
• Standardized processing and consideration of data limitations are recommended for future smORF catalog editions.
• Establishing a canonical catalog of translated smORFs requires community-wide adoption of unified approaches.