Accurate detection of short and long active ORFs using Ribo-seq data.

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Ribosome profiling (Ribo-seq) allows transcriptome-wide translation monitoring in vivo.
• Ribo-seq data enables re-evaluation of coding potential, including short open reading frames (ORFs).
• Detecting translating ORFs, especially short ones, from noisy Ribo-seq data is challenging.

Purpose of the Study:

• To develop a robust method for detecting actively translating ORFs from Ribo-seq data.
• To improve the identification of short ORFs with translational potential.
• To provide a reliable tool for analyzing translation dynamics.

Main Methods:

• Developed ribotricer, a novel computational method.
• Leveraged the three-nucleotide periodicity characteristic of Ribo-seq data.
• Applied the method to multiple published Ribo-seq datasets.

Main Results:

• Ribotricer demonstrates high accuracy and robustness in detecting actively translating ORFs.
• Successfully identified short ORFs with translational activity.
• Validated performance across diverse species including human, mouse, yeast, and zebrafish.

Conclusions:

• Ribotricer offers an improved approach for ORF discovery using Ribo-seq data.
• The method enhances the understanding of the translatome, particularly for short ORFs.
• Ribotricer provides a valuable tool for translational research.