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\begin{document}

\title{vAMPirus: A versatile amplicon processing and analysis program for
studying viruses}



\author[1]{Alex Veglia}%
\author[2,3]{Ramón E. Rivera-Vicéns}%
\author[4]{Carsten Grupstra}%
\author[1]{Lauren Howe-Kerr}%
\author[1]{Adrienne Correa}%
\affil[1]{Rice University}%
\affil[2]{Inter American University of Puerto Rico - Barranquitas Campus}%
\affil[3]{Ludwig-Maximilians-University of Munich}%
\affil[4]{Boston University}%


\vspace{-1em}



  
  \date{May 22, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Amplicon sequencing is an effective and increasingly applied method for
studying viral communities in the environment. Here, we present
vAMPirus, a user-friendly, comprehensive, and versatile DNA and RNA
virus amplicon sequence analysis program, designed to support
investigators in exploring virus amplicon sequencing data and running
informed, reproducible analyses. vAMPirus intakes raw virus amplicon
libraries and, by default, performs nucleotide- and protein-based
analyses to produce results such as sequence abundance information,
taxonomic classifications, phylogenies, and community diversity metrics.
The vAMPirus pipelines additionally include optional approaches that can
increase the biological signal-to-noise ratio in results by leveraging
tools not yet commonly applied to virus amplicon data analyses. In this
paper, we validate the vAMPirus analytical framework and illustrate its
implementation into the general virus amplicon sequencing workflow by
recapitulating findings from two previously published double-stranded
DNA virus datasets. As a case study, we also apply the program to
explore the diversity and distribution of a coral reef-associated RNA
virus. vAMPirus is incorporated with the Nextflow workflow manager,
offering straightforward scalability, standardization, and communication
of virus lineage-specific analyses. The vAMPirus framework itself is
also designed to be adaptable; community-driven analytical standards
will continue to be incorporated as the field advances. vAMPirus
supports researchers in revealing patterns of virus diversity and
population dynamics in nature, while promoting study reproducibility and
comparability.%
\end{abstract}%



\sloppy


Running head: Viruses, Amplicon sequencing, Bioinformatics

\textbf{vAMPirus: A versatile amplicon processing and analysis program
for studying viruses}

Alex J. Veglia\textsuperscript{1}, Ram\selectlanguage{ngerman}ón E.
Rivera-Vicéns\textsuperscript{2,3}, Carsten G.B.
Grupstra\textsuperscript{1,4}, Lauren I. Howe-Kerr\textsuperscript{1},
and Adrienne M.S. Correa\textsuperscript{1}

\textsuperscript{1}BioSciences Department, Rice University, 6100 Main
St, Houston, TX 77005, USA

\textsuperscript{2}Department of Earth and Environmental Sciences,
Paleontology \& Geobiology, Ludwig-Maximilians-Universität München,
Richard-Wagner-Str. 10, 80333 München, Germany

\textsuperscript{3}Department of Sciences and Technology, Interamerican
University of Puerto Rico at Barranquitas, PO Box 517, Barranquitas, PR
00794, USA

\textsuperscript{4} Department of Biology, Boston University, Boston,
Massachusetts, United States of America

KEYWORDS -- Virus, bioinformatics, diversity, DNA, RNA, amplicon
sequencing, amino acid

\textsuperscript{1}Corresponding Author: \textsuperscript{1} Alex J.
Veglia \emph{alex.veglia@gmail.com}

\textbf{Abstract}

Amplicon sequencing is an effective and increasingly applied method for
studying viral communities in the environment. Here, we present
vAMPirus, a user-friendly, comprehensive, and versatile DNA and RNA
virus amplicon sequence analysis program, designed to support
investigators in exploring virus amplicon sequencing data and running
informed, reproducible analyses. vAMPirus intakes raw virus amplicon
libraries and, by default, performs nucleotide- and protein-based
analyses to produce results such as sequence abundance information,
taxonomic classifications, phylogenies, and community diversity metrics.
The vAMPirus pipelines additionally include optional approaches that can
increase the biological signal-to-noise ratio in results by leveraging
tools not yet commonly applied to virus amplicon data analyses. In this
paper, we validate the vAMPirus analytical framework and illustrate its
implementation into the general virus amplicon sequencing workflow by
recapitulating findings from two previously published double-stranded
DNA virus datasets. As a case study, we also apply the program to
explore the diversity and distribution of a coral reef-associated RNA
virus. vAMPirus is incorporated with the Nextflow workflow manager,
offering straightforward scalability, standardization, and communication
of virus lineage-specific analyses. The vAMPirus framework itself is
also designed to be adaptable; community-driven analytical standards
will continue to be incorporated as the field advances. vAMPirus
supports researchers in revealing patterns of virus diversity and
population dynamics in nature, while promoting study reproducibility and
comparability.

\textbf{Introduction}

From the human gut to sediments in the deep ocean, viruses are abundant,
diverse, and shape the systems they inhabit (Breitbart et al., 2018;
Correa et al., 2021; Suttle, 2007). The advent of high-throughput
sequencing (HTS) techniques like amplicon sequencing has transformed the
field of virology, illuminating the currently unculturable virosphere
(Labadie et al., 2020; Metcalf et al., 1995; Paez-Espino et al., 2017;
Zayed et al., 2022) and helping identify the impacts of viruses on
ecosystem and host function (Braga et al., 2020; Breitbart et al., 2018;
Thurber et al., 2017; Uyaguari-Diaz et al., 2016). Amplicon sequencing
is a targeted, polymerase chain reaction (PCR)-based HTS approach that
allows deep characterization of genetic variants within virus
populations (Short et al. 2010). The targeted nature of amplicon
sequencing reduces the economic and computational investment required
for spatiotemporal investigations of virus communities at ecologically
relevant scales (see Finke \& Suttle, 2019; Frantzen \& Holo, 2019;
Grupstra et al., 2022; Gustavsen \& Suttle, 2021; Howe-Kerr et al.,
2022; Montalvo-Proaño et al., 2017). The number of studies leveraging
virus amplicon sequencing has increased rapidly over the last two
decades (e.g., 16 peer-reviewed publications in 1998 compared to 127 in
2021 based on a Web of Science search of `virus amplicon sequencing',
November 2022).

The general virus amplicon sequencing workflow includes: 1. Extraction
of virus nucleic acid (DNA or RNA), 2. PCR amplification of virus marker
gene or transcript, 3. HTS of virus marker gene amplicons, and 4.
Bioinformatic analysis of sequencing data (Short et al., 2010; Figure
1). The effective analysis and interpretation of amplicon sequencing
data relies on biologically accurate binning of marker gene sequences
into taxonomically or ecologically distinct units. Recognizing viral
taxa or ecotypes, however, can be challenging. For example, non-model
viruses have limited baseline information available to inform the
selection of clustering thresholds. Other viruses, such as RNA viruses,
have error-prone polymerases and produce quasispecies, a population
structure consisting of large numbers of variant genomes (Domingo \&
Perales, 2019) that may not be easily resolved by the same clustering
percentage. Amplicon sequence variants (ASVs) are a promising
non-clustering-based approach for virus amplicon analyses that offers
high precision and biological accuracy as error-derived sequence
variants are removed during ASV generation (Callahan et al., 2017;
Edgar, 2016b). In addition, since the identity of an ASV is not specific
to a given dataset (as identity can be in clustering of marker gene
sequences into \emph{de novo} OTUs based on a percent identity value,
Callahan et al., 2017), ASVs and their unique translations
(`aminotypes', see Grupstra et al., 2022) can be compared directly among
studies (Callahan et al., 2017).

To promote the standardization, reproducibility and cross-comparison of
DNA and RNA virus amplicon sequence analyses, we developed the automated
bioinformatics tool, vAMPirus (github.com/Aveglia/vAMPirus). vAMPirus
intakes raw (unprocessed) virus amplicon libraries, performs all read
processing and diversity analysis steps, and produces reports detailing
results (e.g., relative abundance plots, community diversity metrics)
with interactive figures and tables. vAMPirus supports initial
explorations of viral amplicon sequence datasets via a `DataCheck'
pipeline, which generates an HTML report with information on data
quality and sequence diversity. Results from the exploratory DataCheck
pipeline can then be used to optimize parameters in the read processing
or ASV generation steps within the vAMPirus `Analyze' pipeline; this can
improve the signal-to-noise ratio in downstream analyses. vAMPirus is
integrated with the Nextflow workflow manager, which uses a
configuration file that can be shared among investigators, facilitating
the standardization and dissemination of virus amplicon sequence
analyses across projects and research groups. To that end, we also
created the vAMPirus Analysis Repository
(https://zenodo.org/communities/vampirusrepo/) to act as a central
location for all published vAMPirus analyses. vAMPirus is intended to be
accessible to researchers with a range of bioinformatics experience
levels, and includes substantial help documentation with step-by-step
instructions for running the tool
(https://github.com/Aveglia/vAMPirus/blob/master/docs/). By facilitating
the standardization of viral lineage-specific analyses and increasing
the signal-to-noise ratio in community diversity analyses, vAMPirus will
enhance the effectiveness of virus amplicon studies and lead to a more
developed understanding the global virosphere.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} General workflow of virus amplicon sequencing
projects (bolded text). *cDNA = complementary DNA.

\textbf{Materials and methods}

This section outlines the pipelines and processes therein that comprise
vAMPirus v2.1.0 (Figure 2). For a more detailed explanation of vAMPirus
processes and output, see the manual stored in the vAMPirus GitHub
repository (github.com/Aveglia/vAMPirus/).

2.1 vAMPirus implementation and configuration

\emph{2.1.1 Overview of vAMPirus execution}

vAMPirus is composed of three main components that are recommended to be
deployed sequentially: 1. A startup script to install dependencies and
databases for taxonomy processes, 2. A `DataCheck' pipeline that
provides users with detailed information on data quality and diversity
to inform subsequent analysis, and 3. An `Analyze' pipeline that runs a
comprehensive biology-focused analysis of the data using specified
parameters and program options. vAMPirus is incorporated with Nextflow,
a scientific workflow manager that allows easy configuration and
deployment of the program using Conda, Docker, Singularity or cloud
systems like Amazon Web Services (Di Tommaso et al., 2017). Nextflow
natively communicates with scheduling managers like SLURM, PBS, or
Torque, making it easy to run vAMPirus on high-performance computing
clusters or on a local laptop or workstation. vAMPirus analyses can be
configured using the Nextflow configuration file to promote efficient
utilization of computing resources and reduce run times. Real-time
monitoring and remote launching of vAMPirus analyses can be done using
Nextflow Tower with no alterations to the vAMPirus script.

\emph{2.1.1 vAMPirus startup script}

A startup script written in BASH is provided within the vAMPirus
installation directory that will automatically install dependencies and
prepare the vampirus.config file for use. Users can deploy this script
to download the Nextflow workflow manager and Conda package management
system if these programs are not already installed/accessible on the
computer system. The script can also be directed to download one or more
protein/taxonomy databases to be used in vAMPirus taxonomy processes.
Available databases include: 1. The proteic version of the Reference
Virus DataBase (RVDB, Bigot et al., 2020), 2. NCBI virus protein RefSeq
database (Brister et al., 2015), and 3. Complete NCBI NR protein
database (O'Leary et al., 2016). If directed to do so, the startup
script will also download the NCBI Taxonomy Database (Schoch et al.,
2020) and last common ancestor (LCA) information for sequences curated
within the RVDB (Bigot et al., 2020). The script then edits the vAMPirus
configuration file with the updated paths to any downloaded databases
and to the vAMPirus installation directory. Lastly, text documents that
include general next steps for the user and commands to test the
installation are printed in the vAMPirus directory. If test analyses
complete successfully, the user then updates the configuration file with
project-specific parameters (e.g., project name, database for taxonomy
inference, primer sequence information, number of allocated threads,
working memory, scheduling manager) prior to running vAMPirus on a
dataset.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Generalized flowchart of vAMPirus v2.1.0,
illustrating its configuration (box A), default analyses and programs
used within the read processing (box B) and Analyze (boxes C and D)
pipelines. For simplicity, only selected connections between processes
are highlighted; processes generating the unique amino acid sequences
(`aminotypes') and clustered ASVs (`cASVs'), as well as those specific
to the DataCheck pipeline (see Supplemental Figure S2) are omitted. See
Supplemental Figures S1, S2, and S3 for a more comprehensive
illustration of vAMPirus pipelines and the processes therein.

2.2 Overview of the processes performed within vAMPirus

\emph{2.2.1 Read processing and generation of amplicon sequence
variants}

vAMPirus supports single- and paired-end raw Illumina read libraries as
input. By default, read processing and ASV generation processes are
performed prior to entering the DataCheck or Analyze pipelines (Figure
2, yellow box; Supplemental Figure S1). The read processing pipeline
begins with a check of raw libraries using FastQC (v0.11.9, Andrews
2010), which creates and stores reports for review by the user. As
FastQC is running, the program fastp (v0.20.1, Chen et al., 2018)
automatically detects and removes adapter contamination, and performs
quality/length filtering based on user-set parameters in the
configuration file. fastp also performs over-representation analysis and
(for paired-end input) base error correction during this step. Next,
primers are removed from adapter-less reads using the bbduk.sh program
within the BBTools software package (Bushnell 2014), and then another
FastQC report is generated and stored. Cleaned reads are then merged
using the program VSEARCH (v2.21.1, Rognes et al., 2016) and merged
reads (from all libraries) are then concatenated into a single fastq
file. For accurate ASV generation, it is imperative that the merged
reads be the same length (Edgar, 2016b). To ensure this, merged reads
are globally trimmed to a user-specified maximum read length using
fastp. Merged reads with the set length are then extracted from the
total merged read file using the program bbduk.sh and dereplicated using
the program VSEARCH (v2.21.1, Rognes et al., 2016), producing a unique
read file containing read representation information. Amplicon sequence
variants are then generated from this unique read file with VSEARCH and
the UNOISE3 algorithm (Edgar, 2016b; Rognes et al., 2016). Chimeric ASVs
are detected and removed using VSEARCH and the UCHIME3 algorithm (Edgar,
2016a). Prior to entering downstream pipelines, vAMPirus provides users
the option to filter ASVs with DIAMOND blastx (v2.0.15, Buchfink et al.,
2015) to remove non-target sequences or to focus their analyses on a
subset of ASVs/aminotypes. These steps produce a final ASV fasta file
that is then used as input for the DataCheck and Analyze pipelines.

\emph{2.2.2 Amplicon sequence variants and `aminotypes'}

By default, vAMPirus generates nucleotide-based (ASV) and protein-based
(aminotype) results. ASVs support cross-study comparisons and offer a
statistically supported view of virus sequence diversity, as
biologically inaccurate sequences are removed during denoising (Callahan
et al., 2017; Edgar, 2016b). However, ASV results for virus lineages
with high mutation rates (e.g., RNA viruses with quasispecies
heterogeneity) may still contain high levels of noise that mask
biological patterns. It may be beneficial to group ASVs into distinct
clusters based on genetic or ecological similarities in such use cases.
In vAMPirus, `aminotypes' (unique amino acid sequences, Grupstra et al.
2022) are generated by translating ASVs with VirtualRibosome (v2.0,
Wernersson, 2006) and subsequently dereplicating these translations
using the program CD-HIT (v4.8.1, Fu et al., 2012; Li \& Godzik, 2006).
As direct products of specific ASVs, aminotypes maintain sequence
tractability, reproducibility, and comparability, and therefore differ
from \emph{de novo} OTUs or cASVs (see Section 2.2.3). The `aminotyping'
approach not only reduces noise; it also removes sequences with internal
stop codons (a deleterious mutation) and reveals nonsynonymous mutations
that may indicate differences in virus functionality (e.g., infection
efficiency, host range; DeFilippis \& Villarreal, 2000).

vAMPirus provides two additional (optional) ASV or aminotype
``grouping'' approaches that are alternatives to \emph{de
novo}clustering: Minimum Entropy Decomposition (MED) and phylogeny-based
clustering or `phylogrouping'. MED is a method of sequence clustering
that utilizes Shannon entropy (Shannon, 1948) to partition marker gene
datasets into `MED nodes' (Eren et al., 2015). With this approach, users
identify sequence positions in a set of ASVs or aminotypes that are
information-rich (positions of high variability) or information-poor
(positions of high conservation) and use these positions to assign
ASVs/aminotypes to `MED groups' (sequences with identical bases at
specified positions) (Eren et al., 2015). Users can also specify and
assign sequences to MED groups based on sequence positions of interest
(e.g. positions of a protein sequence known to influence a viral
characteristic such as host cell attachment; see Harvey et al., 2021).
Phylogrouping is performed with the TreeCluster program (v1.0.3, Balaban
et al., 2019). With this approach, ASV or aminotype sequences are
assigned to ``phylogroups'' based on user specified TreeCluster
parameters and the phylogenetic tree produced during analysis (see
Figure 4-V, VI). All grouping methods can be applied at the same time;
coupled with the use of the Nextflow `--resume' feature, adjusting
specific parameters and generating new results to review and compare is
straightforward and does not require re-running the entire DataCheck or
Analyze pipelines.

\emph{2.2.3 Optional} de novo \emph{sequence clustering}

vAMPirus provides the option to perform \emph{de novo} clustering of
ASVs into `clustered ASVs' or `cASVs' based on pairwise nucleotide
(ncASV) and/or protein (pcASV) sequence similarity using the programs
VSEARCH (Rognes et al., 2016) and CD-HIT (Fu et al., 2012; Li \& Godzik,
2006), respectively. cASVs differ from traditional \emph{de novo} OTUs
because for cASVs, denoising of sequences is done prior to clustering.
The \emph{de novo} clustering of ASVs is most useful for more developed
virus systems where the degree of sequence divergence between
taxonomically or ecologically distinct groups is known. Note that, from
a methodological standpoint, representative sequences generated by a
cASV approach exhibit the same issues as \emph{de novo} OTUs (e.g.,
dataset dependence; see Callahan et al., 2017).

\emph{2.2.4 vAMPirus DataCheck pipeline and report}

The vAMPirus DataCheck pipeline can help investigators determine the
optimal parameters for read processing, ASV generation, and other
downstream analyses conducted in the Analyze pipeline. The DataCheck
pipeline is particularly beneficial for investigators working on nascent
virus systems because it facilitates the informed establishment of
gene-, lineage- or system-specific analysis standards. The pipeline
produces an HTML report that displays information such as sequencing
success per sample, read characteristics (e.g., read length, GC
content), and ASV/aminotype sequence properties. The DataCheck pipeline
also provides insight into the ASV sequences by clustering them across a
range of nucleotide and amino acid similarities and plotting the
resultant number of cASVs per similarity value. Briefly,
nucleotide-based \emph{de novo} cASVs are produced by clustering ASV
sequences using 24 different percent identity values (55\%, 65\%, 75\%,
80-100\%) with VSEARCH. To generate \emph{de novo} pcASVs, ASVs are
first translated using the program VirtualRibosome (v2.0, Wernersson,
2006), then clustered into \emph{de novo} pcASVs using the same 24
percent identities with the program CD-HIT (v.4.8.1, Fu et al., 2012; Li
\& Godzik, 2006). For each percent identity value, the number of ncASVs
and pcASVs is quantified and visualized as a scatter plot in the
DataCheck report. This is a common approach used to determine the
clustering percentage (e.g., Gustavsen and Suttle 2021): the percent
similarity at which there is no longer a linear rise in the number of
cASVs (the inflection point) is selected for sequence clustering.
Optionally, users can also apply the program oligotyping (Eren et al.,
2015) to calculate Shannon entropy values per sequence position for both
ASV and aminotypes, which is then displayed in the report. An example
vAMPirus DataCheck report is available at
github.com/Aveglia/vAMPirusExamples.

\emph{2.2.5 vAMPirus Analyze pipeline and report}

The Analyze pipeline includes multiple analyses (e.g., phylogenetics,
taxonomy inference); results are summarized in a final HTML report with
tables and interactive figures (Supplemental Figure S3). The pipeline
also organizes and stores output (e.g., counts tables, similarity
matrices, taxonomy files) from these analyses within a user-specified
results directory. Integration into Nextflow allows different analytical
approaches (e.g., ASV grouping approach) to be run in parallel. Primary
processes and analyses are summarized below; additional processes, such
as percent similarity matrix generation and protein physiochemical
property analyses are reviewed in the supplemental materials (Section
S1)\emph{.}

\emph{2.2.5.1 Counts table generation}

Nucleotide- and amino acid-based counts tables are generated within the
Analyze pipeline. Counts tables for ASVs and ncASVs are produced using
the VSEARCH program and the USEARCH algorithm (Edgar, 2010). Optionally,
users can replace the use of the USEARCH algorithm with the use of the
``--search\_exact'' feature provided by VSEARCH for exact ASV counts
tables (Rognes et al., 2016). The aminotype and pcASV counts tables are
generated by aligning translated merged reads to reference amino acid
sequences with DIAMOND blastx (Buchfink et al., 2015). Sequence
abundance information (in a comma delimited counts table) is then
generated with a custom BASH script, which quantifies the number of
alignments to each reference aminotype or pcASV from the DIAMOND output
file. Users have the option to edit and adjust option parameters for the
VSEARCH and DIAMOND counts table generation processes within the
configuration file. All count files are stored in the results directory
and are processed and visualized within Analyze reports as relative
abundance bar plots.

\emph{2.2.5.2 Taxonomy inference}

Sequence taxonomy is inferred using DIAMOND blastx or blastp via the
user-specified protein database and the option parameters specified in
the vAMPirus configuration file. The taxonomy process produces several
files that are stored within a DIAMOND specific directory: 1. Unmodified
DIAMOND output file, 2. fasta file of taxonomy assignments within the
sequence headers, and 3. Results summary files (phyloseq taxonomy file,
tab-separated summary file, and summary table of the abundance of
specific hits). Taxonomy inference results are visualized in the Analyze
report as a donut plot.

\emph{2.2.5.3 Phylogenetic analysis}

Phylogenetic analyses of ASVs, cASVs and aminotypes are conducted within
the Analyze pipeline and all output files are stored in a dedicated
directory within the results directory. First, sequences are aligned
using the program muscle (v5.1; Edgar, 2021) and then trimmed
automatically using the program trimAl (v1.4.1, Capella-Guti\selectlanguage{ngerman}érrez et
al., 2009) using a heuristics-based approach. By default, substitution
model testing is done with Modeltest-NG (v0.1.7, Darriba et al., 2020).
The program IQTREE (v2.2.0.3; Minh et al., 2020) is used to generate a
maximum-likelihood tree. The substitution model used to generate the
tree can be set by the user, sourced from Modeltest-NG results, or
automatically selected with ModelFinder (Kalyaanamoorthy et al., 2017).
The tree produced is then used for phylogrouping with TreeCluster and is
visualized in the Analyze report. Within the report, the user has the
option to color code nodes based on sequence identity, taxonomy hit, MED
group, or phylogroup assignment.

\emph{2.2.5.4 vAMPirus Analyze reports}

The final process within the Analyze pipeline generates HTML reports
using R Markdown (v2.3; (Xie et al., 2018). By default, an individual
summary report containing community composition/diversity, taxonomy and
phylogeny results is generated for ASVs, aminotypes, and cASVs. Users
provide a metadata file that includes the sample name and category used
to group the samples (i.e., sample treatment, location) for alpha and
beta diversity analyses. vAMPirus summary reports are interactive and
include: 1. Pre- and post-processing read statistics in tables and
plots, 2. Rarefaction curves, 3. Shannon's diversity (\emph{H} ),
Simpson's diversity (reported as 1-\emph{D} ), richness and distance to
centroid box plots with statistical tests (Shapiro-Wilk normality test,
Bartlett test of variance homogeneity, Kruskal-Wallis rank sum test,
Wilcox test, ANOVA and Tukey HSD, as appropriate), 4. Two- and
three-dimensional NMDS plots (if no convergence, then PCoA plots), 5.
Relative abundance bar plots, and 6. Taxonomy and phylogenetic results.
An example vAMPirus Analyze summary report can be downloaded and
reviewed at github.com/Aveglia/vAMPirusExamples.

2.3 vAMPirus Analysis Repository

To encourage and simplify the dissemination of parameters and non-read
files needed to reproduce vAMPirus analyses, we created the `vAMPirus
Analysis Repository' (zenodo.org/communities/vampirusrepo/). The
vAMPirus Analysis Repository is a Zenodo Community intended as a central
location where investigators can deposit vAMPirus configuration files,
metadata files, databases used for taxonomy assignment or ASV filtering,
and any other files required to reproduce an analysis. Instructions and
recommendations for submission are available in the vAMPirus manual
(shorturl.at/uCO28). Once uploaded, submissions to the vAMPirus Analysis
Repository are given a DOI.

\textbf{Validating the vAMPirus workflow with published double-stranded
DNA (dsDNA) virus datasets}

We assessed the functionality and performance of vAMPirus' analytical
workflow using amplicon sequencing datasets from two previously
published dsDNA virus studies (Table 1). Research questions associated
with each study are used as examples in Figure 1A (Finke \& Suttle 2019;
Figure 1A, Q1; Frantzen \& Holo 2019; Figure 1A, Q2). For each dataset,
we ran a vAMPirus analysis that reproduced the analysis from the
associated published paper as closely as possible. For example, if a
study generated \emph{de novo} OTUs based on 97\% nucleotide identity,
the vAMPirus equivalent was ncASVs generated at 97\% nucleotide identity
with similar data quality control constraints. We then compared the
results of the vAMPirus-based analyses to the findings described in each
source manuscript. In brief, vAMPirus identified the same biological
patterns as those published by Finke \& Suttle (2019, Figure 3) and
Frantzen \& Holo (2019, Figure 4) from their respective sequence
datasets, and detected additional (previously unreported) virus
diversity (Table 1). For example, Finke and Suttle (2019) reported
increased cyanophage community alpha diversity in samples collected from
sites with higher salinity (\textgreater{}27.5 practical salinity units,
Figure 3-I, II); this pattern was present in the corresponding vAMPirus
results (Figure 3-III, IV, V, VI), which included 86\% more cyanophage
pcASVs relative to the number of OTUs reported in Finke and Suttle
(2019; Table 1). Similarly, the patterns of lactococcal phage OTU
richness and relative abundances per sample reported by Franzten and
Holo (2019; Figure 4-I) were also present in the vAMPirus results (Table
2; Figure 4-II). vAMPirus reported 43\% more lactococcal phage ncASVs,
relative to the OTUs reported by Frantzen and Holo (2019; Table 1,
Figure 4). In addition, vAMPirus ASV-level analysis (Figure 4-III)
revealed high lactococcal phage nucleotide-level diversity (n=531), yet
aminotyping results (Figure 4-IV) suggest that the mutations underlying
this richness mostly result in synonymous mutations: ASV sequences
translated to only 29 aminotypes. Aminotype phylogrouping (see Section
2.2.2) of these data with TreeCluster highlighted a previously hidden
overlap of lactococcal phage diversity across samples and dairy plants
(Figure 4-VI).

Some variation between results obtained from vAMPirus and previous
publications was expected, as the pipelines used in these comparisons
were not identical. The only striking difference between the original
results (in Finke and Suttle 2019 and Frantzen and Holo 2019) and those
produced by vAMPirus is the higher number of pcASVs and ncASVs
(respectively) identified via the latter analytic pipeline. Taxonomy
results generated with vAMPirus by DIAMOND blastx aligning sequences to
the NCBI virus RefSeq database verified that the pcASVs and ncASVs are
of cyanophage and lactococcal phage origin, respectively (Supplemental
Figures S4 and S5). The higher diversity identified by vAMPirus may be
attributable to differences in reference database used (boutique versus
NCBI-curated), handling of singletons, and other factors.

\textbf{Table 1.} Breakdown of test datasets used during vAMPirus
development, including the methods and results from the original
(published) analysis, as well as results from vAMPirus analysis.
vAMPirus results were generated using \emph{de novo} clustering of ASVs
into `clustered ASVs' (cASVs) based on pairwise nucleotide (ncASV) and
protein (pcASV) sequence similarity. dsDNA = double-stranded DNA.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Study & Target dsDNA virus group & Target gene & Original Methods & \#
published OTUs & \# cASVs (vAMPirus, this study)\tabularnewline
\midrule
\endhead
Finke \& Suttle 2019 & Myoviridae (T4-like cyanophage) & DNA polymerase
& OTU clustering at 97\% protein identity & 606 & 1,128 97\%
pcASVs\tabularnewline
Frantzen \& Holo 2019 & Siphoviridae (lactococcal bacteriophage) &
portal protein & OTU clustering at 99.5\% nucleotide identity & 151 &
216 99.5\% ncASVs\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Comparison of results published by Finke \& Suttle
(2019) to those generated by the vAMPirus workflow for the same virus
amplicon sequence dataset. The significant positive relationship between
salinity and viral diversity (Shannon diversity (\emph{H} ) and
richness) reported by Finke and Suttle (2019, panels I and II) was
reproduced by vAMPirus (panels III-VI). Panels I and II display original
results from Finke and Suttle (2019), based on 97\% amino acid
similarity OTUs. Panels III, IV, V, and VI include vAMPirus 97\% pcASV
results. Figures from Finke and Suttle (2019) and vAMPirus analyses were
slightly modified for readability. For the vAMPirus analysis, which
performs comparisons among categorical sample groups set by the user,
all samples were assigned to either `high' (\textgreater{}27.5) or `low'
(\textless{}27.5) salinity (practical salinity units; psu) groups.
Figures from Finke and Suttle (2019) reprinted with permission from
authors.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} Comparison of results published by Frantzen and Holo
(2019) to those generated by vAMPirus for the lactococcal phage amplicon
sequence dataset. Patterns of lactococcal phage OTU relative abundance
reported by Frantzen and Holo (2019; panel I) were recapitulated in
vAMPirus ncASV results (panel II). Panel I displays original relative
abundances of \emph{de novo} OTUs based on 99.5\% nucleotide similarity
from Frantzen and Holo (2019). Panel II displays vAMPirus-generated
99.5\% similarity ncASV relative abundance results. Panels III-VI
demonstrate additional analyses output by vAMPirus, including ASV
relative abundances, aminotype relative abundances, aminotype phylogeny
with nodes colored by sequence phylogroup assignment, and aminotype
phylogroup relative abundances. X-axis labels for all relative abundance
graphs refer to dairy sample identity listed in Table 2. Colors in
panels I-IV are plot-specific; similar colors across these panels
do\emph{not} indicate same sequences/clusters. However, colors assigned
to phylogroups are consistent across panels V and VI. Figure from
Frantzen and Holo (2019) reprinted with permission from authors.

\textbf{Table 2.} Comparison of Lactococcus phage sequence richness per
sample between Frantzen and Holo (2019) results and vAMPirus (this
study) results.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
& Dairy Sample & S1 & S2 & S3 & S4 & S5 & S6\tabularnewline
\midrule
\endhead
& \textbf{Dairy Plant} & \textbf{1} & \textbf{1} & \textbf{1} &
\textbf{2} & \textbf{2} & \textbf{2}\tabularnewline
Frantzen and Holo 2019 & \# of OTUs with \textgreater{}100 reads & 11 &
46 & 55 & 26 & 54 & 59\tabularnewline
vAMPirus results & \# of ncASVs with \textgreater{}100 reads & 25 & 121
& 117 & 47 & 66 & 63\tabularnewline
\bottomrule
\end{longtable}

\textbf{Applying vAMPirus to study a novel environmental RNA virus
dataset}

\emph{4.1 RNA virus study description}

Dinoflagellate-infecting RNA viruses (dinoRNAVs) are positive-sense,
single-stranded RNA viruses hypothesized to infect the dinoflagellate
symbionts (Family Symbiodiniaceae) that live in the tissues of
reef-building stony corals (Correa et al., 2013; Grupstra et al., 2022;
Veglia et al., 2022). Although dinoRNAVs can be prevalent in coral
colonies (Grupstra et al., 2022; Howe-Kerr et al., 2022; Montalvo-Proa\selectlanguage{ngerman}ño
et al., 2017; Veglia et al., 2022), it is unclear how dinoRNAVs (or
Symbiodiniaceae infected by dinoRNAVs) are transmitted among colonies.
Recent work has shown that corallivorous (coral-eating) fishes disperse
100s of millions of live Symbiodiniaceae cells across reefscapes in
their feces each day (Grupstra et al., 2021). To assess the extent to
which corallivorous fish feces disperse dinoRNAVs in their feces (as in
Grupstra et al., 2022), we characterized the presence and diversity of
dinoRNAVs in various environmental reservoirs using amplicon sequencing
of the dinoRNAV major capsid protein (\emph{mcp} ) gene. Given that
dinoRNAV communities can vary across coral species and colonies
(Grupstra et al., 2022; Howe-Kerr, 2022; Montalvo-Proaño et al., 2017)
and that corallivorous fish actively `sample' corals while feeding
(Grupstra et al., 2021), we hypothesized that corallivorous fish feces
are a reservoir of dinoRNAVs and that fish feces-associated dinoRNAV
communities exhibit higher alpha diversity than coral colony-associated
dinoRNAV communities. We generated 19 dinoRNAV \emph{mcp} amplicon
sequencing libraries from coral colony biopsies
(\emph{Acroporahyacinthus} , n=8; \emph{Pocillopora} species complex,
n=5), as well as the feces of corallivorous fishes (\emph{Chaetodon
reticulatus} , n=4; \emph{Chaetodon ornatissimus,} n=2). All samples
were collected from reefs off the north shore of Moorea, French
Polynesia (South Pacific). Methods for sampling and sample processing to
generate virus amplicon sequencing libraries are described in Grupstra
et al. (2021,2022) and Howe-Kerr et al. (2022). DinoRNAV \emph{mcp}
amplicon libraries were processed and analyzed using vAMPirus (see
doi.org/10.5281/zenodo.7574173).

\emph{4.2 RNA virus study results and discussion}

Amplicon sequencing of the dinoRNAV \emph{mcp} gene produced a total of
7.4 million raw reads across 19 samples representing three potential
reservoirs of dinoRNAV diversity across the reef. The 7.4 million raw
reads were processed and reduced to 2.8 million merged reads at the
expected amplicon length of 420 bases. Merged \emph{mcp} amplicons
dereplicated into 1.1 million unique sequences from which 481 ASVs and
191 aminotypes were identified. The ASV-level results indicated a
potential trend of higher dinoRNAV richness in corallivore feces
relative to coral colonies (Kruskal-Wallis H test: p-value = 0.14,
Figure 5-I). Aminotype results, however, revealed that dinoRNAV richness
is significantly higher in corallivore feces, relative
to\emph{Pocillopora} coral colonies (Figure 5-II; Kruskal-Wallis H test:
p-value = 0.005; Wilcoxon signed-rank test: \emph{Pocillopora} vs.
corallivore, p = 0.01, \emph{Pocillopora} vs. \emph{Acropora} , p =
0.04). We interpret that a biological difference in richness likely does
exist between dinoRNAV communities in corallivore feces versus those in
at least some species of coral holobionts, and this difference may be
more readily detected with aminotype-based analyses (as ASV-based
analyses may contain more ``noise'' due to errors arising during RNA
virus replication). This use case illustrates the potential benefits of
running nucleotide and protein-based amplicon analyses in tandem when
testing hypotheses regarding virus community diversity and dynamics.
Furthermore, both ASV and aminotypes differed significantly in
composition according to dinoRNAV reservoir (anosim with Bray Curtis
distances, R=0.99, p\textless{}0.01; Figure 5-III, IV), although some
overlap (14\%, 26 of 190 aminotypes) among dinoRNAV communities was
observed (Supplemental Figure S6). Overall, this vAMPirus-based analysis
of RNA virus amplicon sequencing data further corroborates that dinoRNAV
communities differ across reef reservoirs (Grupstra et al. 2022,
Montalvo-Proano et al. 2017, Howe-Kerr et al. 2022, Figure 5) and
generates a new hypothesis to be tested in future studies: corallivorous
fishes are environmental hotspots of dinoRNAV diversity on reefscapes.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} vAMPirus-generated dinoRNAV major capsid protein gene
ASV and aminotype alpha (I, II) and beta (III, IV) diversity results
from stony coral colonies \emph{(Acropora} sp.,
\emph{Pocillopora}species complex) and corallivorous (coral-eating) fish
feces\emph{.}Plots include three sample types: 1. \emph{Acropora}
biopsies (blue, triangle), 2. Corallivore feces (gray, x), and 3.
\emph{Pocillopora}biopsies (red, diamond). Letters beneath x axis labels
on richness box plots (I, II) indicate statistically different groups.
ASV and aminotype based NMDS plots (III, IV) were generated with Bray
Curtis distances (stress values of 0.04 and 0.03, respectively).

\textbf{Discussion}

Targeted gene sequencing is increasingly being applied to explore
spatiotemporal patterns of viral diversity (Adriaenssens \& Cowan, 2014;
Finke \& Suttle, 2019; Frantzen \& Holo, 2019; Grupstra et al., 2022;
Gustavsen \& Suttle, 2021; Howe-Kerr et al., 2022; Y. Li et al., 2018;
Montalvo-Proa\selectlanguage{ngerman}ño et al., 2017; Prodinger et al., 2020; Short et al.,
2010; Tong et al., 2016). The field of virology can now greatly benefit
from the development of readily standardizable and reproducible
pipelines for analyzing amplicon sequence datasets. Here, we present
vAMPirus; a freely available, powerful, and flexible bioinformatics tool
that streamlines the processing, analysis, and visualization of virus
gene amplicon data. The availability of diverse bioinformatics
approaches and tools within the vAMPirus program (\emph{e.g.,} ASV
calling, clustering, translation, phylogenetic clustering) empowers the
user to adapt and set informed standards for their study system and
easily share these standards with colleagues. With a user-friendly
design and robust documentation, vAMPirus democratizes comprehensive
virus amplicon sequencing analyses, making it a timely and valuable tool
for virologists.

To inform virus amplicon data analyses, virologists have primarily
relied on pipelines and tutorials geared towards bacterial or
microeukaryote amplicon data (e.g., mothur (Schloss, 2020) and QIIME2
(Bolyen et al., 2019)). Although valuable insights have been made using
these resources, an accessible virus-focused amplicon analysis pipeline
will advance the field by offering via (1) automated pipelines that
standardize approaches for viral amplicon analyses (e.g., ASV and
aminotype calling); (2) non-cluster-based alternatives to partitioning
virus gene sequences (e.g., MED and phylogrouping); and (3)
virus-focused taxonomy databases. Virus amplicon analyses have
traditionally applied \emph{de novo} clustering of marker gene sequences
into \emph{de novo} OTUs based on a percent identity value (i.e., 97\%
nucleotide identity, Callahan et al., 2017). However, clustering virus
amplicons into biologically accurate \emph{de novo} OTUs is challenging
as the optimal clustering percentage is often unknown. vAMPirus provides
users with the opportunity to transition from traditional \emph{de
novo}OTUs in virus amplicon sequencing analyses to using ASVs and
aminotypes. We have illustrated here that ASV and aminotype-based
analyses generally recapitulate findings generated via \emph{de novo}
OTU-based analyses (Figures 3, 4; Supplemental Table S1), while enabling
reproducibility and cross-study comparisons (Callahan et al., 2017).
Running analyses of amino acid and nucleotide sequence data in tandem,
which is possible in vAMPirus, can aid in resolving virus phylogenies
and reveal non-synonymous mutations that indicate virus protein property
variability within a community (DeFilippis \& Villarreal, 2000). This
synergistic approach has been effective in developing dinoRNAVs and
their dinoflagellate hosts (family Symbiodiniaceae, endosymbionts of
stony corals) as a nascent study system. To characterize dinoRNAVs,
studies have used the \emph{mcp} gene, which has a high mutation rate
and is hypothesized to be important in host cell attachment (Tomaru et
al., 2004). vAMPirus aminotyping uncovered non-synonymous mutations in
dinoRNAV \emph{mcp} sequences, which may represent phenotypic
differences that correlate with the distribution of host lineages across
reefs (Grupstra et al., 2022; Howe-Kerr et al., 2022, this study).
Aminotyping also effectively reduced noise from high mutation rates in
ASV results, revealing temperature-driven increases in dinoRNAV
infection productivity and community diversity across time and space
(Grupstra et al. 2022: time only, Howe-Kerr et al., 2022). By making
viral protein sequence analyses readily accessible in an amplicon
sequence analysis workflow, vAMPirus helps reveal biological patterns in
DNA and highly mutable RNA virus lineages by increasing signal-to-noise
ratio in results (through collapse of synonymous nucleotide mutations,
(Wernersson \& Pedersen, 2003).

The increasing application of amplicon sequencing to the study of
microbial diversity and dynamics has spurred efforts to improve the
proficiency of tools that parse marker gene data. Such tools include the
programs TreeCluster (Balaban et al., 2019) and oligotyping (Eren et
al., 2015), which were developed as \emph{de novo} clustering
alternatives for partitioning genetic sequences into distinct units. In
vAMPirus, these programs are utilized to assign ASVs and aminotypes to
phylo- or MED groups based on user-set criteria (see Section 2.2.2).
Assignment of ASV/aminotype sequences to groups rather than use of
cluster representative sequences in analyses (such as, in the case
of\emph{de novo} OTUs and cASVs, Callahan et al., 2017) is done by
vAMPirus to maintain reproducibility and comparability of results, while
still permitting virus sequence classification into phylogenetically or
ecologically distinct groups. These grouping approaches are instrumental
for investigators because they can expose underlying patterns obscured
by high sequence diversity (e.g., lactococcal phage phylogrouping
results, Section 3, Figure 4). Phylogeny-based sequence clustering with
TreeCluster has been applied to assess the diversity of microorganisms
(and barley, Chen et al., 2022) and has been used to resolved virus
transmission dynamics (HIV, Balaban et al., 2019; SARS-CoV-2, Plyusnin
et al., 2022) and phylogenies (Ni et al., 2023). However, TreeCluster's
potential utility for virus amplicon analyses is, for the most part,
untapped. The inclusion of TreeCluster in the vAMPirus pipeline also
opens the door to epidemiological insights, such as virus genetic
linkage, transmission dynamics, and subpopulation mixing, from viral
datasets (Balaban et al., 2019; Bezemer et al., 2015; Eshleman et al.,
2011; Hué et al., 2014). Similarly, the program oligotyping developed by
Eren et al. (2015) has been applied extensively to investigate
microorganism diversity from marker gene data (cited 332 times as of
February 2, 2023, Web of Science). However, only one published study has
applied Minimum Entropy Decomposition sequence clustering with
oligotyping to virus amplicon data (Needham et al., 2017). The MED
grouping with oligotyping option provided by vAMPirus is a powerful
approach for deciphering virus community diversity because it enables
the grouping of sequences based on potential physiologically and/or
ecologically relevant similarities. For example, users can identify gene
sequence positions with non-synonymous mutations via aminotyping and
then specify these positions in MED grouping to partition sequences into
units of similar protein phenotypes (i.e., host cell attachment; see
Harvey et al., 2021). The option to incorporate cutting-edge
bioinformatic approaches, such as phylogrouping and MED grouping, into
analyses of virus amplicon data makes vAMPirus a highly useful
``raw-reads-to-results'' environmental virology workflow.

vAMPirus is an easy-to-use, open-source, and flexible tool that
streamlines and simplifies the process of analyzing viral amplicon data.
vAMPirus is designed to be community-driven; new features and programs
(e.g., built-in lineage specific configuration files or databases, new
bioinformatic tools) can easily be implemented at the request of
investigators or when advances in best practices are made. vAMPirus
advances studies of viral community diversity by facilitating informed
analyses of amplicon sequence data with its DataCheck and Analyze
pipelines in a standardized and reproducible manner.

\textbf{Acknowledgments}

The authors would like to thank Jan F. Finke, Curtis A. Suttle, Julia A.
Gustavsen, Cyril Frantzen, Hedge Holo, Florian Prodinger and Hiroyuki
Ogata (and their respective co-authors) for providing raw virus amplicon
sequence data for vAMPirus testing and for permission to reprint
original figures. We thank Samantha R. Coy for feedback and insight
during early stages of vAMPirus development, and Nikolaos Schizas for
access to computational resources during vAMPirus development. This work
represents a contribution of the Moorea Coral Reef (MCR) LTER Site (NSF
OCE 16--37396). This research was funded by a U.S. National Science
Foundation Grant~OCE 22-24354 (and earlier awards) to the Moorea Coral
Reef LTER as well as a generous gift from the Gordon and Betty Moore
Foundation. Research was completed under permits issued by the
Territorial Government of French Polynesia (De\selectlanguage{english}le\selectlanguage{english}gation~a \selectlanguage{english}la Recherche)
and the Haut-Commissariat de la~Re\selectlanguage{english}publique~en~Polyne\selectlanguage{english}sie~Francaise~(DTRT)
(MCR LTER~Protocole~d'Accueil~2005--2022;~Adrienne Correa\textbf{~}
Protocole~d'Accueil~2013-2019), and we thank the~De\selectlanguage{english}le\selectlanguage{english}gation~a \selectlanguage{english}la
Recherche and DTRT for their continued support. Start-up funds from Rice
University, a NSF CAREER Award (OCE-2145472) and an Early-Career
Research Fellowship (\#2000009651) from the Gulf Research Program of the
National Academies of Sciences to AMSC also contributed to this work.
Additional funding was provided by Lewis and Clark Grants for
Exploration to LHK and CGG, and Wagoner Foreign Study awards and to LHK,
CGG, and AJV. The Kirk W. Dotson Endowed Graduate Fellowship in Ecology
and Evolutionary Biology also helped support AJV as this work was
conducted. RERV acknowledges funding from the European Union's Horizon
2020 research and innovation programme under the Marie Sk\selectlanguage{polish}ł\selectlanguage{english}odowska-Curie
grant agreement No. 764840 (ITN IGNITE).

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\textbf{Conflict of Interest}

The authors declare that they have no financial conflict of interest
with the content of this article.

\textbf{Authors' Contributions}

AJV, CBG, LHK conceived of the program with support from AMSC; CBG and
LHK contributed R code used in the vAMPirus reports; RERV contributed R
code and helped execute vAMPirus incorporation into Nextflow; CBG, LHK
and AMSC processed samples and generated the RNA virus dataset; AJV
designed the pipelines with input from CBG and LHK; AJV wrote bash and R
code used in the program, analyzed data, and wrote the initial draft of
the manuscript, with contributions by all authors.

\textbf{Data availability}Source code, scripts, and help documentation
are available online at github.com/aveglia/vAMPirus. RNA virus
sequencing libraries are available on NCBI SRA associated with the
BioProject PRJNA923642 as well as in the vAMPirus Analysis Repository
(doi.org/10.5281/zenodo.7574173). All non-read files required to
reproduce all analyses and results described in this manuscript can be
found on the vAMPirus Analysis Repository
(doi.org/10.5281/zenodo.7574173).

\textbf{ORCID}AJV - 0000-0003-3118-5127 RERV - 0000-0002-6229-3537 CGG -
0000-0001-5083-4570 LHK - 0000-0002-8086-5869 AMSC - 0000-0003-0137-5042

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