Nanopore Variant Analysis Pipeline

This pipeline is designed for analyzing amplicon-based nanopore sequencing for CRISPR mediated mutations. It supports read alignment (using minimap2), variant calling (via bcftools), and basic variant summarization, providing insights into reference matches, insertions, deletions, and substitutions. Note, for the summary statistics output file, the reference percentages assume a single variant per read in the target site, and thus if multiple variants are present in a single read that impact the target site, the percent of reference bases will be underestimated. Note, this is a basic pipeline that has not been extensively tested on multiple machines.

Installation Guide

System Requirements

This pipeline has been tested on Ubuntu Linux with the following external tool dependencies:

• Minimap2 (2.26-r1175 tested)
• BCFtools (1.10.2 tested)
• Samtools (1.10 tested)
• Biopython
• pysam

Ensure Minimap2, BCFtools, and Samtools are installed and accessible in your system's PATH. This pipeline has been tested on Ubuntu Linux.

Setting Up Python Environment

Create a virtual environment and install Python dependencies:

python3 -m venv nanopore_env
source nanopore_env/bin/activate  # On Windows use `nanopore_env\Scripts\activate`
pip install biopython pysam

Installing Command-line Tools

• Minimap2, BCFtools, and Samtools: These can be installed via package managers on most Linux distributions. For Ubuntu, use:

sudo apt-get update
sudo apt-get install minimap2 bcftools samtools

Usage

To run the pipeline, use the following command:

python Nanopore_Variant_analysis.py --fastq_path <path_to_fastq> --ref_fasta <path_to_ref_fasta> --output_dir <output_directory> --targets <path_to_targets> --min_read_num <min_read> --min_read_percentage <min_percentage> --min_read_quality <min_quality> --min_alignment_quality <min_align_quality> --max_depth <max_depth>

Parameters

• --fastq_path <path>: Specify the path to the FASTQ file or directory containing the FASTQ files. (The pipeline assumes a folder of fastq files that will first be pooled into a single one)
• --ref_fasta <path>: Path to the reference genome FASTA file. Note, the names of each amplicon must exactly match the names in your targets TSV file
• --output_dir <path>: Directory where output files will be saved.
• --targets <path>: Path to a TSV file containing target sites for variant analysis. This should contain 2 columns with the amplicon name from the reference in the first and the expected target sequence in the second
• --min_read_num <number>: Minimum number of reads supporting a variant (default: 3).
• --min_read_percentage <percentage>: Minimum percentage of reads that support a variant, expressed as a percentage (e.g., for 10%, use 0.1 as the value; default: 0.1).
• --min_read_quality <quality score>: Minimum quality score for reads to be considered in the analysis (default: 18).
• --min_alignment_quality <quality score>: Minimum alignment quality score for alignments to be considered (default: 10).
• --max_depth <depth>: Maximum read depth to consider in variant calling (default: 10000).

Example

python Nanopore_Variant_analysis.py --fastq_path barcode01 --ref_fasta SRSF2_offtargets.fasta --output_dir barcode01_out --targets Targets.txt --min_read_num 3 --min_read_percentage 0.1 --min_read_quality 16 --min_alignment_quality 16 --max_depth 10000

Contributing

Contributions to improve the pipeline or extend its functionality are welcome. Please submit pull requests or report issues via GitHub.