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) . Though there is currently no consensus in the world of molecular epidemiology regarding a formal definition of what exactly constitutes a "transmission cluster" (Novitsky et al., 2017) , all current methods of inferring transmission clusters require a multiple sequence alignment (MSA) of the viral genomes: distance-based methods of transmission clustering require knowledge of homology for accurate distance measurement (Pond et al., 2018) , and phylogenetic methods of transmission clustering require the MSA as a precursor to phylogenetic inference (Balaban et al., 2019; Rose et al., 2017; Ragonnet-Cronin et al., 2013; Prosperi et al., 2011) .
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. For example, during the COVID-19 pandemic, the number of viral genome assemblies available from around the world grew exponentially in the initial months of the pandemic, but MAFFT, the fastest of the aforementioned MSA tools, scales quadratically with respect to the number of sequences.
In the case of closely-related viral sequences for which a highconfidence reference genome exists, MSA can be accelerated by independently comparing each viral genome in the dataset against the reference genome and then using the reference as an anchor to merge the individual alignments into a single MSA.
Here, 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.
VIRULIGN is another reference-guided MSA tool designed for viral genomes (Libin et al., 2019) . While VIRULIGN also aims to support MSA of large sequence datasets, its primary objective is to produce codon-correct MSAs (i.e., avoiding frameshifts), whereas ViralMSA's objective is to produce MSAs for use in real-time applications such as transmission clustering. Thus, while ViralMSA is not guaranteed to yield codon-correct alignments, it is orders of magnitude faster than VIRULIGN, which is critical for rapidly-growing epidemics. Further, because it is codon-correct, VIRULIGN is appropriate for coding regions, whereas ViralMSA is appropriate for whole genomes. Lastly, VIRULIGN requires a thorough annotation of the reference genome, which may be difficult to obtain (especially towards the beginning of a novel outbreak) and does not provide the user to easily utilize different reference genomes for different viral strains. ViralMSA, on the other hand, only requires the reference genome assembly's GenBank accession number and can build any required index files on-the-fly.
ViralMSA is written in Python 3 and is thus cross-platform. ViralMSA depends on BioPython (Cock et al., 2009) and whichever read mapper the user chooses, which is Minimap2 by default (Li, 2018) . In addition to Minimap2, ViralMSA supports STAR (Dobin et al., 2013) , Bowtie 2 (Langmead & Salzberg, 2012), and HISAT2 (Kim et al., 2019) , though the default of Minimap2 is strongly recommended: Minimap2 is much faster than the others (Li, 2018) and is the only mapper that consistently succeeds to align all genome assemblies against an appropriate reference across multiple viruses. 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.
ViralMSA takes the following as input: (1) a FASTA file containing the viral genomes to align, (2) the GenBank accession number of the reference genome, and (3) the mapper to utilize (Minimap2 by default). ViralMSA will pull the reference genome from GenBank and generate an index using the selected mapper, both of which will be cached for future alignments of the same viral strain, and will then execute the mapping. ViralMSA will then process the results and output an MSA in the FASTA format. For commonly-studied viruses, the user can simply provide the name of the virus instead of an accession number, and ViralMSA will select an appropriate reference genome. The user can also choose to provide a local FASTA file containing a reference genome, which may be useful if the desired reference does not exist on GenBank or if the user wishes to conduct the analysis offline.
Because it uses the positions of the reference genome as anchors with which to merge the individual pairwise alignments, ViralMSA only keeps matches, mismatches, and deletions with respect to the reference genome: it discards all insertions with respect to the reference genome. For closely-related viral strains, insertions with respect to the reference genome are typically unique and thus lack usable phylogenetic or transmission clustering information, so their removal results in little to no impact on downstream analyses (Tab. 1). In order to test MSA accuracy, we obtained curated Ebola, HCV, and HIV-1 full genome MSAs from the Los Alamos National Laboratory (LANL) Sequence Databases, which we used as our ground truth. In order to benchmark MSA runtime, we obtained a large collection of SARS-CoV-2 complete genomes from the Global Initiative on Sharing All Influenza Data (GISAID) database. We then used both MAFFT and ViralMSA to estimate MSAs. No other MSA tools were included in the comparison due to being orders of magnitude slower than MAFFT (which would render the full experiment infeasible). Because ViralMSA's objective is to be utilized in real-time applications such as transmission clustering workflows, which typically rely on pairwise distances between samples, we computed pairwise sequence distances between every pair of samples in each MSA under the TN93 model of sequence evolution (Tamura & Nei, 1993) using the pairwise distance calculator implemented in HIV-TRACE (Pond et al., 2018) . Then, for each estimated MSA, we measured alignment accuracy by computing the Mantel correlation test between the curated ("true") and estimated MSAs. In order to measure performance, we subsampled the full SARS-CoV-2 dataset, with 10 replicates for each dataset size. We then computed MSAs of each subsampled alignment replicate.
As can be seen, ViralMSA in its default mode is consistently orders of magnitude faster than MAFFT (Figs. 1, S1 ), yet it produces MSAs with just slightly lower accuracy, even across different viruses (Tab. 1) and different levels of subsampling (Figs. S2-S3). Further, both MAFFT and ViralMSA produce MSAs that tend to underestimate TN93 distance, with ViralMSA underestimating slightly more significantly (Fig. S4) . Note that ViralMSA's speed and accuracy stem from the algorithmic innovations of the selected read mapper (not from ViralMSA itself), meaning ViralMSA can natively improve as read mapping tools evolve.