HRIBO: high-throughput analysis of bacterial ribosome profiling data.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Ribosome profiling (Ribo-seq) is a key technique for studying cellular translation.
• Existing Ribo-seq analysis pipelines lack specific optimization for bacterial data and novel open reading frame (ORF) discovery.
• Small proteins are challenging to detect with standard methods.

Purpose of the Study:

• To develop a high-throughput and reproducible workflow for bacterial Ribo-seq data analysis.
• To facilitate the discovery and prioritization of unannotated ORFs in bacteria.
• To enable the detection of small proteins recalcitrant to other methods.

Main Methods:

• Developed HRIBO (High-throughput annotation by Ribo-seq), a comprehensive workflow for bacterial Ribo-seq data.
• Implemented automated pre-processing and quality control steps.
• Integrated two complementary bacteria-focused tools for annotation-independent ORF prediction.

Main Results:

• HRIBO enables reproducible, high-throughput analysis of bacterial Ribo-seq data.
• The workflow facilitates rapid, high-confidence discovery of novel ORFs.
• Integrated feature information and expression values aid in prioritizing novel ORFs for functional studies.

Conclusions:

• HRIBO provides a robust solution for bacterial Ribo-seq data analysis.
• The workflow enhances the discovery of previously unannotated bacterial genes, including small proteins.
• HRIBO supports efficient functional characterization of novel bacterial ORFs.