RibosomeProfilingEngine

Ribo-seq Translational Efficiency Analysis Pipeline

A pure-Python pipeline for ribosome profiling data analysis including translational efficiency and uORF detection.

Features

• Translational efficiency (TE = Ribo-seq RPM / RNA-seq RPM)
• Upstream ORF (uORF) detection and activity scoring
• Codon Adaptation Index (CAI) computation
• Differential translation analysis (anota2seq-style)
• Translational buffering detection

Results

• 50 samples × 3000 genes, paired Ribo-seq + RNA-seq
• DE-TE genes (FDR<0.05, |ΔTE|>0.5): 98
• Translational buffering genes: 88
• uORF-TE correlation: r=-0.483, p=4.39e-175
• Median CAI: 0.860
• Ribo-RNA correlation: r=0.995

Usage

pip install numpy scipy matplotlib
python ribosome_profiling_engine.py

Tags

ribosome-profiling translation codon-usage translational-efficiency uorf ribo-seq