iSNV Detection

PySNV is a tool for detecting iSNVs (intra-host variations) in tNGS sequencing data.

Install

git clone https://github.com/bnuLyndon/PySNV.git
cd PySNV
pip install .

Check Installation

pyisnv --help

This will display the usage information:

Usage: pyisnv [OPTIONS] COMMAND [ARGS]...
PySNV is a tool for detecting iSNVs (intra-host variations) in tNGS
sequencing data.

Options:
--version  Show the version and exit.
--help     Show this message and exit.

Commands:
detect-multi-samples  Detect iSNVs of multiple samples using PySNV.
detect-sample         Detect iSNVs using PySNV.

To check the installed version, run:

pyisnv --version

This should output the current version:

pyisnv, version 1.0.0

Usage

Example Files

example.py: Demonstrates basic usage of the PySNV tool.

example_multi_files.py: Illustrates how to process multiple input files simultaneously. Paired samples hould include '_R1' and '_R2' in the file names. Adjust the number of kernels to use parallel processing.

Note: Before running the examples, make sure to set the necessary parameters in the file.

python example.py <path-to-pyiSNV> <path-to-R1-fastq>
python example_multi_files.py <path-to-pyiSNV> <path-to-fastqs-folder>

Bash Usage

Command

pyisnv detect-sample \
    --sample1 <path-to-sample1.fastq> \
    --sample2 <path-to-sample2.fastq> \
    --reference <path-to-reference_genome.fa> \
    --output <output_filename>

pyisnv detect-multi-samples \
    --folder <path-to-samples-folder>  \
    --reference <path-to-reference_genome.fa> \
    --output <output-folder>

Parameter

--sample1: Path to single-end sample or first paired-end sample. Fasta, fastq and gz files are supported.

--sample2 (optional): Path to the second paired-end sample.

--reference (required): Path to the reference genome.

--output: Output file path and name (default is current working directory, using sample name as output filename).

Additional Parameters

--threshold: Detection Threshold (Default: 0.02)

The recommended detection threshold should be lager than sequencing error rate.

--kmer_length: Kmer Length (Default: 21)

This parameter specifies the length of k-mers to be considered during the analysis and must be an odd number smaller than 31. Kmer length should be set to ensure no duplicate kmer exists on the genome.

--downsample: Downsample Factor (Default: 1)

Downsamping of sequencing reads could enhance detection speed and reduce RAM usage, which could be used for high-depth sequencing samples.

--error_rate: Sequencing Error Rate (Default: 0.01)

Used to filter out possible false positive detection.

--indel_limit: Maximum Indel Length (Default: 300)

To mitigate false positive indels, especially in the case of challenging long insertions and potential impacts on estimated sequencing depths due to long deletions, a default maximum indel length of 300 is set. The recommended length threshold is 2*average_read_length.

Cite

If these codes are helpful for you, please cite our paper:

Li, L., Fu, H., Ma, W., & Li, M. (2024). PySNV for complex intra-host variation detection. Bioinformatics, btae116.

https://doi.org/10.1093/bioinformatics/btae116

https://academic.oup.com/bioinformatics/article/40/3/btae116/7616992