DVG_ONT_scripts

This repository contains a collection of Python and R scripts used for analyzing Nanopore direct RNA sequencing (DRS) data, particularly focusing on viral reads characterization and defective viral genome (DVG) analysis.

Table of Contents

• Overview
• Requirements
• Installation
• Scripts
  • Python Scripts
  • R Scripts
• Usage

Overview

These scripts facilitate the analysis of Nanopore DRS data for viral genome characterization, including:

• Read length distribution visualization
• Coverage depth visualization
• Classification of spliced and unspliced viral reads
• Recombination junction site identification
• Poly(A) tail length visualization
• Chimeric read visualization

Requirements

R Environment

• R version: 4.5.0 or higher
• Platform: Compatible with macOS, Linux, and Windows

R Packages

# Core packages
library(ggplot2)      # v4.0.1
library(dplyr)        # v1.1.4
library(data.table)   # v1.17.8
library(cowplot)      # v1.2.0

# Additional dependencies (auto-loaded)
# RColorBrewer, tidyselect, scales, etc.

Python Dependencies

• Python 3.x
• pysam

Installation

Install R packages

install.packages(c("ggplot2", "dplyr", "data.table", "cowplot"))

Install Python dependencies

pip install pysam

Scripts

Python Scripts

bam2bed.py

A pysam-based custom Python script that converts BAM alignment files to BED format to facilitate downstream analysis in R.

Usage:

python bam2bed.py --input <input.bam> --output <output.bed> --label virus

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R Scripts

0. Demo data

We provided demo data to run Rscripts,

wget https://github.com/lrslab/DVG_ONT_scripts/releases/download/zenodo_version/demo_data.zip
unzip demo_data.zip

1. 1_Read Length Boxplot.R

Plots the read length distribution of Nanopore DRS reads.

• Output: Read length boxplots (e.g., Figure S1)

2. 2_Coverage Plot.R

Plots the coverage depth of viral reads across the genome.

• Output: Coverage depth visualization (e.g., Figure 1C)

3. 3_Viral Spliced and Unspliced Reads.R

Classifies viral reads into two categories:

• Contiguous reads: Unspliced, continuous alignment
• Noncontiguous reads: Spliced, with gaps in alignment

4. 4_Classification of Spliced Reads.R

Further classifies noncontiguous reads based on:

• Fragment count
• Strand orientation
• Assignment to genomic RNA (gRNA) or subgenomic RNA (sgRNA)

5. Start-End Position Plots

5.1. 5.1_Start-End Plot of Unspliced Reads.R

Plots the start and end positions of contiguous reads.

• Output: Position distribution plot (e.g., Figure S8)

5.2. 5.2_Start-End Plot of Spliced Reads.R

Plots the start and end positions of noncontiguous reads.

• Output: Spliced read position plot (e.g., Figure 1E)

5.3. 5.3_Start-End Plot of Chimeric Reads.R

Plots the start and end positions of virus-host chimeric reads.

• Output: Chimeric read visualization (e.g., Figure 1E)

6. 6_PolyA Length Plot.R

Analyzes and plots the poly(A) tail length distribution for different classes of reads.

• Output: Poly(A) length distribution (e.g., Figure S12)

7. 7_Recombination Boundary.R

Visualizes the junction sites of noncontiguous reads to identify recombination events.

• Output: Recombination junction plot (e.g., Figure 4A)

8. 8_Segment Plot.R

Displays the read distribution along the viral reference genome, showing coverage patterns.

• Output: Genome segment coverage plot (e.g., Figure 6A)

Usage

Basic Workflow

1. Convert BAM to BED format:

   python bam2bed.py aligned_reads.bam output.bed

2. Run R analysis scripts in order:

   source("1_Read Length Boxplot.R")
   source("2_Coverage Plot.R")
   source("3_Viral Spliced and Unspliced Reads.R")
   # ... continue with other scripts as needed

3. Customize parameters within each script according to your data and analysis needs.

Session Information

The scripts were developed and tested in the following environment:

R version 4.5.0 (2025-04-11)
Platform: aarch64-apple-darwin20
Running under: macOS 26.0.1

attached packages:
[1] cowplot_1.2.0     data.table_1.17.8 dplyr_1.1.4       ggplot2_4.0.1

Contact

For questions or issues, please open an issue in this repository or contact Dr. TAN Lu.

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Note: Ensure all input files are properly formatted and paths are correctly specified in the scripts before running.