Leaderless Translation Efficiency Analysis

Overview

Certain bacterial species use regulatory mechanisms to evade the immune system and cause disease. Traditional genome annotation often overlooks leaderless translation, where mRNAs are translated without a 5' UTR or Shine-Dalgarno sequence. This study quantifies leaderless translation efficiency in bacteria using a Python-based computational approach. By analyzing transcription start sites (TSS) and genome reference sequences, we identify ORFs initiating via this mechanism. Across each ORF, translation efficiency is determined by calculating the ratio of ribosomal footprint signal (Translation) to the RNA-seq data (Transcription).

Features

• Identification of leaderless ORFs based on transcription start sites (TSSs) and a FASTA reference sequence.
• Calculation of translation efficiency by analyzing ribosomal footprint signal relative to RNA-seq data.
• Identification of high-efficiency leaderless transcripts, contributing to the understanding of non-canonical translation mechanisms in bacteria.

Installation

Install Dependencies

Ensure you have the following dependencies installed:

• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Biopython

pip install notebook numpy pandas matplotlib seaborn biopython

To avoid dependency conflicts, it is recommended to create and activate a virtual environment.

Setup

Clone the repository

git clone https://github.com/Champion-Lab/leaderlessTranslation.git

cd leaderlessTranslation

Input the data

The notebook requires the following datasets to run, which should be placed in the provided inputs folder within the repository. Sample data specific to M. tuberculosis can be accessed via the Mtb_inputs folder in the Data drive (DATA/Anna/leaderlessTranslation/Mtb_inputs). Output files will be exported to the provided folders data, plots, and fastas and update each time the notebook is re-run.

• Genome reference sequence (genome_FASTA, .fasta)
• Table containing coordinate/strand of all known TSSs (TSS, .bed)
• Annotated genes: start base, end base, strand & name (AnnotatedGenes, .bed)
• Ribo-seq data: base values and corresponding ribosomal intensity (Ribosome footprint, .bedgraph)
• RNA-seq data: base values and corresponding RNA intensity (RNA-seq, .bedgraph)
• Ribo-RET data: base values and corresponding ribosomal intensity at initiation site (Ribo-RET footprint, .bedgraph)
• Protein reference sequence (protein_FASTA, .fasta)

Run the notebook

Start Jupyter Notebook by running:

jupyter notebook

Open the leaderless_efficiency.ipynb Notebook file.