snvoter 0.1.2

A top up tool to enhance SNV calling from Nanopore sequencing data.

SNVoter

Improving SNV detection from low coverage nanopore sequencing data (<30x).

Installation

Using the pypi repository.

pip install snvoter

From source.

git clone https://github.com/vahidAK/SNVoter.git
cd SNVoter
./snvoter.py

Creation of a dedicated conda environment

SNVoter uses several fixed versions of its dependencies. Users are encouraged to use a conda or similar environment to isolate the packages from their default python instance. An environment file is available in the GitHub repository.

git clone https://github.com/vahidAK/SNVoter.git
conda env create -f SNVoter/env/environment.yaml
conda activate snvoter

Tutorial

Variant Calling

You first need to call variants using Clair

You can call variants for each chromosome using the following command and the concatenate all files:

for i in chr{1..22} chrX chrY; do callVarBam --chkpnt_fn <path to model file> --ref_fn <reference_genome.fa> --bam_fn <sorted_indexed.bam> --ctgName $i --sampleName <your sample name> --call_fn $i".vcf" --threshold 0.2 --samtools <path to executable samtools software> --pypy <path to executable pypy > --threads <number of threads>

For the full tutorial please refer to Clair page on GitHub.

Improving Clair's variant calling using SNVoter:

snvoter prediction -i <SNVs_Clair.vcf> -b <sorted_indexed.bam> -mf <path to model file (model.h5)> -r <reference_genome.fa> -t number_of_threads -o output_prefix

It will produce two files.

1- Prediction file that includes each prediction for each 5-mer. The first 10 columns are from vcf file and the last seven columns indicate:

• chrom: the chromosome name
• pos_start: 0-based position of the 5-mer start
• pos_end: 0-based position of the 5-mer end
• pos: 0-based position of the SNV
• 5-mer sequence: sequence of five-mer
• Coverage: this might be different from Clair's coverage as SNVoter uses different mapping quality threshold
• Prediction

2- The second file is the ready vcf file with weighted qualities. You can plot the distribution of weighted quality to obtain optimal threshold for filtering. The plot usually looks like the following plots:

The optimal threshold is the end of the first peak and start of the valley (highlighted regions).

Training a New Model