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Comparison of software packages for detecting unannotated translated small open reading frames by Ribo-seq.

Gregory Tong1, Nasun Hah2, Thomas F Martinez1,3,4

  • 1Department of Pharmaceutical Sciences, University of California, Irvine, CA 92617, USA.

Biorxiv : the Preprint Server for Biology
|January 18, 2024
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Summary

Identifying translated microproteins requires careful analysis of ribosome profiling (Ribo-seq) data. Using multiple computational tools increases confidence in detecting small open reading frames (smORFs).

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Accurate annotation of microprotein-coding small open reading frames (smORFs) is crucial for understanding cellular functions and diseases.
  • Ribosome profiling (Ribo-seq) is the primary method for empirically identifying translated smORFs, but data quality and analysis tools vary significantly.

Approach:

  • This study compared the performance of five common software tools (RibORFv0.1, RibORFv1.0, RiboCode, ORFquant, and Ribo-TISH) for analyzing Ribo-seq data to identify translated smORFs.
  • The impact of Ribo-seq dataset quality (resolution) on tool performance was also assessed.

Key Points:

  • There was low agreement among the five tools, with only ~2% of smORFs identified by all tools and ~15% by three or more tools on the same dataset.
  • Agreement was higher for larger annotated genes (~72% across all five tools).
  • Some tools showed bias against low-resolution Ribo-seq data, while others were more tolerant. Highly translated smORFs were more likely to be detected by multiple tools.

Conclusions:

  • Employing multiple analysis tools is recommended for robust identification of translated smORFs.
  • Tool selection should consider Ribo-seq data quality and the specific goals of downstream smORF characterization.