ViralMSA: massively scalable reference-guided multiple sequence alignment of viral genomes.

Niema Moshiri,Peter Robinson

doi:10.1093/bioinformatics/btaa743

Abstract

In molecular epidemiology, the identification of clusters of transmissions typically requires the alignment of viral genomic sequence data. However, existing methods of multiple sequence alignment (MSA) scale poorly with respect to the number of sequences. ViralMSA is a user-friendly reference-guided MSA tool that leverages the algorithmic techniques of read mappers to enable the MSA of ultra-large viral genome datasets. It scales linearly with the number of sequences, and it is able to align tens of thousands of full viral genomes in seconds. However, alignments produced by ViralMSA omit insertions with respect to the reference genome. ViralMSA is freely available at https://github.com/niemasd/ViralMSA as an open-source software project. Supplementary data are available at Bioinformatics online.

Highlights

Real-time or near real-time surveillance of the spread of a pathogen can provide actionable information for public health response (Poon et al, 2016)
ViralMSA depends on BioPython (Cock et al, 2009) and whichever read mapper the user chooses, which is Minimap2 by default (Li, 2018)
ViralMSA’s support for read mappers other than Minimap2 is primarily to demonstrate that ViralMSA is flexible, meaning it will be simple to incorporate new read mappers in the future

Summary

Introduction

Real-time or near real-time surveillance of the spread of a pathogen can provide actionable information for public health response (Poon et al, 2016). The standard tools for performing MSA such as MAFFT (Katoh & Standley, 2013), MUSCLE (Edgar, 2004), and Clustal Omega (Sievers & Higgins, 2014) are prohibitively slow for real-time pathogen surveillance as the number of viral genomes grows. We introduce ViralMSA, a user-friendly open-source MSA tool that utilizes read mappers such as Minimap2 (Li, 2018) to enable the reference-guided alignment of ultra-large viral genome datasets.

Results

Conclusion