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Related Concept Videos

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Updated: Jun 24, 2025

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
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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, Irvine, CA 92617, United States.

Briefings in Bioinformatics
|June 6, 2024
PubMed
Summary

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

Keywords:
Ribo-seqmicroproteinsmORF annotationtranslation

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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 dataset quality and analysis tools vary significantly.

Purpose of the Study:

  • To evaluate the impact of Ribo-seq data quality and analysis tool choice on the identification of translated smORFs.
  • To compare the performance of five commonly used Ribo-seq analysis tools for smORF detection.

Main Methods:

  • Comparison of five Ribo-seq analysis tools (RibORFv0.1, RibORFv1.0, RiboCode, ORFquant, Ribo-TISH) on high-resolution Ribo-seq data.
  • Assessment of tool performance based on agreement in smORF identification.
  • Evaluation of tool sensitivity to Ribo-seq data resolution and quality.

Main Results:

  • Low agreement was observed among the five tools for smORF identification, with only ~2% of smORFs detected by all tools.
  • Agreement for larger annotated genes was substantially higher (~74%) compared to smORFs.
  • Some tools showed bias against low-resolution Ribo-seq data, while others were more robust.
  • smORFs identified by multiple tools exhibited higher translation levels and in-frame read fractions.

Conclusions:

  • Employing multiple computational tools is recommended for confident identification of microprotein-coding smORFs.
  • Tool selection should consider Ribo-seq dataset quality and the specific goals of downstream smORF characterization.
  • Standardization of analysis or consensus approaches may be necessary for reliable smORF discovery.