vSNP: a SNP pipeline for the generation of transparent SNP matrices and phylogenetic trees from whole genome sequencing data sets

Abstract

Background: Several single nucleotide polymorphism (SNP) pipelines exist, each offering its own advantages. Among them and described here is vSNP that has been developed over the past decade and is specifically tailored to meet the needs of diagnostic laboratories. Laboratories that aim to provide rapid whole genome sequencing results during outbreak investigations face unique challenges. vSNP addresses these challenges by enabling users to verify and validate sequence accuracy with ease- having utility across various pathogens, being fully auditable, and presenting results that are easy to interpret and can be comprehended by individuals with diverse backgrounds.

Results: vSNP has proven effective for real-time phylogenetic analysis of disease outbreaks and eradication efforts, including bovine tuberculosis, brucellosis, virulent Newcastle disease, SARS-CoV-2, African swine fever, and highly pathogenic avian influenza. The pipeline produces easy-to-read SNP matrices, sorted for convenience, as well as corresponding phylogenetic trees, making the output easily understandable. Essential data for verifying SNPs is included in the output, and the process has been divided into two steps for ease of use and faster processing times. vSNP requires minimal computational resources to run and can be run in a wide range of environments. Several utilities have been developed to make analysis more accessible for subject matter experts who may not have computational expertise.

Conclusion: The vSNP pipeline integrates seamlessly into a diagnostic workflow and meets the criteria for quality control accreditation programs, such as 17025 by the International Organization for Standardization. Its versatility and robustness make it suitable for use with a diverse range of organisms, providing detailed, reproducible, and transparent results, making it a valuable tool in various applications, including phylogenetic analysis performed in real time.

Keywords: Bioinformatics; SNPs; Sequencing.