key: cord-0733283-rhjbesdq
authors: Folkerts, Megan L.; Lemmer, Darrin; Pfeiffer, Ashlyn; Vasquez, Danielle; French, Chris; Jones, Amber; Nguyen, Marjorie; Larsen, Brendan; Porter, W. Tanner; Sheridan, Krystal; Bowers, Jolene R.; Engelthaler, David M.
title: Methods for sequencing the pandemic: benefits of rapid or high-throughput processing
date: 2022-02-21
journal: F1000Res
DOI: 10.12688/f1000research.28352.2
sha: 5e57c92f1b31d795719d4d5eedd1515c0ffe4ff2
doc_id: 733283
cord_uid: rhjbesdq

Genomic epidemiology has proven successful for real-time and retrospective monitoring of small and large-scale outbreaks. Here, we report two genomic sequencing and analysis strategies for rapid-turnaround or high-throughput processing of metagenomic samples. The rapid-turnaround method was designed to provide a quick phylogenetic snapshot of samples at the heart of active outbreaks, and has a total turnaround time of <48 hours from raw sample to analyzed data. The high-throughput method, first reported here for SARS-CoV2, was designed for semi-retrospective data analysis, and is both cost effective and highly scalable. Though these methods were developed and utilized for the SARS-CoV-2 pandemic response in Arizona, U.S, we envision their use for infectious disease epidemiology in the 21 (st) Century.

With the advent of rapid and inexpensive next-generation sequencing, genomic epidemiology has proven to be an invaluable resource for the elucidation of disease outbreaks. Extending beyond traditional shoe-leather approaches, rapid-turnaround sequencing methods have allowed researchers to quickly gain insight into the genetic nature of pathogens at the heart of active outbreaks [1] [2] [3] [4] [5] [6] . By monitoring pathogen evolution over the course of an outbreak, large-scale genomics have the potential to allow for transmission mapping for infection control and prevention 7,8 , to distinguish independent cases from those part of active clusters 9 , and to identify epidemiological patterns in time and space on both local and global scales 7,10-12 .

The most recent example of this has been the collaborative genomic efforts mounted in response to the SARS-CoV-2 outbreak. Not long after the initial cases were identified, whole-genome sequencing quickly established the etiologic agent as a novel coronavirus 13 . Following the rapid spread of SARS-CoV-2, current next-generation technology and analysis pipelines allowed viral sequencing to take place on an unprecedented global scale, with collaborative consortia forming world-wide for the specific purpose of tracking and monitoring the pandemic 14-16 .

In most instances, including with the SARS-CoV-2 outbreak, genomic epidemiology has provided a retrospective view of pathogen spread and evolution well after the information is useful in the public health response to the outbreak 1,2 . Genomic epidemiology should guide contemporaneous outbreak control measures, but can only do so if the data are generated and interpreted in real-time quickly enough to inform a response 17 . As technology has advanced, the potential exists to move beyond providing a retrospective genomic snapshot of an outbreak months after its occurrence, to providing actionable data in real-time for current outbreaks within hours after cases are identified 4 . Real-time genomic tracking has already proven valuable in a number of instances, including the recent West-African Ebola outbreak 3,17 . This is not to discredit the value of large-scale, retrospective studies. While rapid-turnaround genomics may prove essential for outbreak containment, retrospective studies will continue to be necessary to track pathogen evolution, gauge success of public health interventions, and to evaluate pathogen/host movement and behavior. With the recent SARS-CoV-2 pandemic, retrospective studies have so-far proven successful in identifying the timing and sources of outbreaks on a local 18 and global scale 5, 19 , in evaluating the effectiveness of early interventions 5 , and in identifying super-spreader events 20 . Thus, in addition to real-time monitoring, high-throughput, cost-effective sequencing and analysis are needed to gain a better understanding of pandemics.

Here, we report two Illumina-based sequencing and analysis strategies for either real-time monitoring or large-scale, highthroughput targeted genomic sequencing of complex samples. The plexWell method is a novel means of library construction that can facilitate high-throughput sample processing. Though these strategies were developed for use with the current SARS-CoV-2 pandemic, we envision their potential use in any situation in which a genomic response is needed.

Remnant nasopharyngeal swab specimens or extracted RNA were obtained from, or received by, the TGen North Laboratory in Flagstaff, AZ. All samples had previously tested positive for SARS-CoV-2 by RT-PCR.

RNA was extracted using the MagMax Viral Pathogen II kit and a Kingfisher Flex automated liquid handler (ThermoFisher Scientific), with a DNase treatment incorporated to maximize viral RNA recovery from low viral-burden samples, defined as having an RT-PCR cycle-threshold (Ct) value above 33.0. These methods allowed for the rapid, scalable processing of a small number to hundreds of samples at once with minimal personnel, and prevented RNA extraction from becoming a bottleneck to overall throughput (https://doi.org/10.17504/protocols.io.bnkhmct6). Remnant RNA was obtained from the TGen North Laboratory, and had been extracted following their FDA-authorized protocol for the diagnosis of COVID-19.

Targeted amplification SARS-CoV2 RNA was amplified for both of the sequencing methods described below following the nCoV-2019 sequencing protocol V.1 21 and using the ARTIC v3 primer set 22 . Adapters were added to the resulting amplicons by one of the following means described below. The full sample processing workflow, starting with raw RNA and ending with deliverable data, is illustrated in Figure 1 for reference.

For samples requiring immediate attention, e.g. those from patients potentially involved in an active outbreak, adapters were added with the DNA Prep kit (Illumina) as previously described 23 . Amplicons were sequenced on the MiSeq platform, using a Nano 500 cycle kit with v2 chemistry (Illumina) (https:// doi.org/10.17504/protocols.io.bnnbmdan). The batch size used for this method was 2-8 samples, as that was the typical sample volume requiring rapid turnaround in our facility. However, the maximum number of clusters obtainable from a Nano 500 cycle kit is 1 million, thus, with a theoretical minimum target number of reads of ~42,000, this method would be suitable for up 24 samples per kit. For samples with low Ct values, (less than 33) this was demonstrated to be sufficient to obtain a complete genome, based on results obtained from the high-throughput sequencing method, which was optimized first. For Ct values less than 33, read counts as low as ~19,000 per sample were sufficient to provide a 99% coverage breadth (Supplementary data).

In instances where retrospective data were needed from large numbers of samples, adapters were added with the plex-Well384 kit (Seqwell). Samples were multiplexed in batches of 1,152 and sequenced on a NextSeq 550 with v2 chemistry and 150 X 150 bp reads (Illumina). When batch sizes were not large enough to fill a NextSeq run, samples were sequenced on a MiSeq, with V3 chemistry (Illumina) (https://doi.org/10.17504/ protocols.io.bnkimcue). At the time of submission of this manuscript, a maximum of 1,152 barcode combinations were available (this has since increased to 2,304) and thus, the targeted read depth was an order of magnitude higher than for rapid sequencing, at nearly 348,000 reads per sample. The increased number of reads was necessary to account for high Ct value (ie, low viral load) samples, which were not separated into distinct sequencing runs, and which were pooled equally by the plexWell system.

Read data were analyzed using the statistics package "RStudio" (version 4.1.1) 24 The "stats" package was used to perform Kruskal-Wallis, and determine standard deviation 25 . Confidence intervals based on z-scores were calculated using the "BSDA" package 26 . Read data are reported in terms of mean, standard deviation (SD) and 95% confidence interval (95%CI).

Virus genome consensus sequences were built using the Amplicon Sequencing Analysis Pipeline (ASAP) 27,28 . First, reads were adapter-trimmed using bbduk 29 , and mapped to the Wuhan-Hu-1 genome 30 with bwa mem 31 using local alignment with soft-clipping. Bam alignment files were then processed to generate the consensus sequence and statistics on the quality of the assembly by the following: 1) Individual basecalls with a quality score below 20 were discarded. 2) Remaining basecalls at each position were tallied. 3) If coverage ≥10X and ≥80% of the read basecalls agreed, a consensus basecall was made. 4) If either of these parameters were not met, an 'N' consensus call was made. 5) Deletions within reads, as called during the alignment, were left out of the assembly, while gaps in coverage (usually the result of a missing amplicon) were denoted by lowercase 'n's. Only consensus genomes covering at least 90% of the reference genome with an average depth of ≥30X were used in subsequent analyses. Consensus genomes generated using these methods include those from Ladner et al. 18 and are similar to those in Peng et al. 32 and Stoddard et al. 33 . Statistics reported for each sample included: total reads, number of reads aligned to reference, percent of reads aligned to reference, coverage breadth, average depth, and any SNPs and INDELs found in ≥10% of the reads at that position.

Rapid phylogenetic inference was used when it was critical to rapidly answer two initial genomic epidemiological questions: i) are the samples in this set closely related, and ii) to what samples in the public database are these most closely related? To answer the former, SNP comparisons were made among a sample set of interest and output in text format directly from the ASAP results, foregoing the computational time of generating phylogenetic trees.

To answer the latter, a database of all SNPs in the GISAID global collection 15 was generated and periodically updated by downloading all the genomes and filtering them for quality and completeness, then aligning to the WuHan-Hu-1 reference to identify any variants, as described 34 . The list of all variants and metadata for each sample were then put into a relational database for fast querying. The format of this database was one table of SNPs, as generated by Chan et al. 34 with columns for GISAID ID, position, reference base, and variant base, and a second table of metadata which contained the exact data fields, as downloaded from GISAID. The two tables were linked by the GISAID ID number. The GISAID database cannot be shared via secondary publications as per the database access agreement, however the database is available for download by the public from GISAID's website 15 . The list of SNPs common to samples in a given set was then compared to this global SNP database by first querying the number of global genomes containing each of the individual SNPs, sorting them from most common to least common, then further querying for number of global genomes containing combinations of SNPs by adding in each successive SNP in turn, to determine how many GISAID genomes shared all or a subset of the SNPs from the set. A stacked Venn diagram to illustrate globally-shared SNPs was constructed using the statistical package "R" (Version 4.0.2) 25 and the "ggplot2" 35 and "ggforce" 36 R-packages. This information was then relayed to public health partners, who used these preliminary analyses to guide contact tracing efforts and track the spread of the virus in real-time.

For subsequent, more sophisticated phylogenetic analyses, phylogenetic trees were constructed in NextStrain 16 , with genomes from GISAID subsampled by uploading our genomes of interest to the UCSC UShER (UShER: Ultrafast Sample placement on Existing tRee) tool 37 , identifying relevant genomes (those most related to our genomes of interest), and further reducing that set of genomes when necessary with genome-sampler 18,38 .

Samples used were remnant, de-identified samples from a clinical diagnostics lab. No ethical approval was required for their inclusion in this study.

For the plexWell workflow, turnaround time from raw sample to sequence data was approximately 72 hours, and to phylogenetic inference approximately 10 hours, for 1,152 samples (Table 1 ). Cost per sample was ~$60, which is comparable to similar sample prep methods (Table 1) 39 . A complete breakdown of run statistics by Ct value can be found in Table 2 . Success rates, defined as the percentage of samples with greater than 90% genome coverage, were similar to other previously described methods 39,40 . Of a random subset of 897 samples analyzed, approximately 83% of samples with a Ct <33 yielded genomes with ≥90% breadth of coverage of the reference genome, i.e., a complete genome, with an average breadth of coverage of the reference genome of 91% (SD 20.10, 95%CI 89.79-92.81). (Figure 2 , Table 2 ). Failure to obtain a complete genome when Ct was <33 was attributed to sample preparation error, sample degradation, or poor sequencing run metrics. Beyond a Ct of 33, success rate dropped drastically. Between Ct values of 33 and 35, complete genome success rate was ~41% (Table 2) , with an average breadth of coverage of 78% (SD 23.00, 95%CI 72.50-83.20). Above a Ct of 35, ~18% of samples yielded a complete genome, and breadth of coverage dropped below 57% (SD 31.65, 95%CI 48.70-59.08) ( * Listed is the number of samples that can be processed within the specified turnaround time, on a single sequencing run. This is not necessarily the upper limit of either processing system. **A detailed breakdown of reagent costs is available in supplementary materials. *** Time from raw sample to analyzed data.

Samples able to be processed* 

Turnaround time for the Illumina DNA prep method was significantly faster than the high-throughput plexWell system. It took less than 48 hours to go from raw sample to deliverable, analyzed data (Table 1) , and this time could potentially be reduced further by reducing cycle numbers per sequencing run. Illumina estimates a cycle time of ~2.5 minutes for the kit type described, meaning a reduction of just 50 cycles could reduce the overall run time by >2 hours 41 .

Both the plexWell and DNA Prep methods use a tagmentation system for adapter addition, rather than a ligation-based approach. This results in adapters being added ~50 bp or more from the end of overlapping amplicons generated during genespecific PCR. A second PCR step amplifies only the tagmented regions, resulting in final libraries of ~300bp. These approaches negate the need for primer trimming prior to alignment.

Generating consensus genomes and a SNP report from the sequence data, which takes approximately 15 minutes for a small (<=24 samples) dataset, quickly shows whether the samples are part of the same outbreak. To quickly find a potential origin of a cluster or sample (assuming genomes in the public domain were collected prior to the sample(s) of interest), a global SNP database can be used. Constructing or updating a global genome SNP database takes several hours, but it can be done prior to sample sequencing (and regularly). Running the commands to query the global SNP database for particular SNPs of interest takes mere seconds.

To rapidly visualize results of the global SNP database query, a stacked Venn diagram (aka, "onion diagram") visually describes the hierarchical nature, i.e., the parsimony, of SNPs found in the SARS-CoV-2 genomes (Figure 3) , and is easily generated using the methods described above or an alternative tool.

Although fewer data are available from the Illumina DNA prep method, as it was primarily used to process five or fewer samples at a time, data suggest that it performed slightly worse than the plexWell system. Kruskal-Wallis revealed no significant difference among the average aligned reads between these groups (P= 0.08).

The recent SARS-CoV-2 outbreak has highlighted the need for real-time sequencing and data analysis capacity in the face of active pandemics, as well as high-throughput sequencing and analysis strategies for comprehensive retrospective analysis and evaluation. We describe two different strategies that can be used in combination with Illumina platforms for the rapid or high-throughput interrogation of samples involved in disease outbreaks. Though the results reported here are specific to the SARS-CoV-2 outbreak, these methods could conceivably be modified and applied to many other situations in which a genomic epidemiological response is needed.

The Illumina DNA prep-SNP-comparison analysis method is effective in providing rapid sequence data and genomic epidemiology information for small numbers of samples (<48 hours from raw sample to rough phylogenetic placement). Though throughput is limited by both the number of available indices for multiplexing and the nature of the protocol itself, this method has the advantage of being scalable to small numbers of samples, and can be performed in the course of several hours for small sample subsets. The Seqwell plexWell system provides a scalable, cost-effective, and high-throughput method of processing thousands of samples with minimal laboratory personnel (Table 1) . Here, we report the first use of plexWell for post-amplification adapter addition for whole genome sequencing of SARS-CoV2. As the plexWell protocol calls for the pooling of samples at an initial step in the adapter tagmentation process, hundreds of samples were able to be taken through the later steps of the protocol by a single individual in an 8-hour timeframe. This, coupled with the availability of thousands of index combinations, allowed for a high-throughput, cost-effective means of processing large numbers of samples on a weekly basis with minimal laboratory personnel and infrastructure. With just two full-time staff members devoted to sequencing, and 2-4 part-time staff responsible for arraying samples prior to the ARTIC/plexWell protocol, 1152 samples were able to be successfully processed and sequenced each week. Thus, the number of genomes completed per full-time (40 hour) employee per week is 384, a novel pace for what is normally a lengthy protocol.

Typical analyses of virus genomes include phylogenetic tree construction to understand transmission patterns. Nextstrain 16 and GISAID 15 have been crucial to the SARS-CoV-2 scientific community for global and local epidemiologic understanding, and tools for smart subsampling (e.g. genome-sampler 38 ) are now necessary with the growth of the public databases. However, reconstructing phylogenies, especially paired with finding relevant subsets, takes time, and is often overkill for initial, time-sensitive public health needs. We employ a simple, rapid analysis method and visualization meant as a quick-look to determine relatedness among a sample set of interest and/or relatedness of a sample or set to the entire public database of genomes. Because of the novelty of SARS-CoV-2 and its low rate of recombination, merely comparing the low number of SNPs (at the time of first publication) across samples without applying a phylogenetic model or program is often enough to answer initial questions about COVID-19 transmission, e.g. whether samples in a given set are closely related, and/or which samples in the global database are most closely related to a given sample set. This information, then, can be used by public health officials to determine which patients might be involved in active spread of disease (requiring additional shoe-leather epidemiology to follow up) and which patients are thought to represent isolated cases without additional spread. This is not meant to replace more thorough phylogenetic analysis, but rather, to provide a quick genomic snapshot that can inform a public health ground response. Our SNP queries followed by generation of a stacked Venn diagram (onion diagram) offer a much faster alternative or antecedent to complete phylogenetic analysis, and have served as a starting point for contact tracing in active outbreaks. The data presented here using the rapid-turnaround method were obtained from coded, de-identified patient samples that were part of an active SARS-CoV2 outbreak, and the resulting phylogenetic placements were successfully used to inform public health efforts to determine the potential origin of the outbreak, and to prevent further spread 42 . Other pathogens or situations where SNP numbers are expected to be very low may also benefit from these rapid analysis methods.

For retrospective studies, time can allow for more robust phylogenetic analyses including smart subsamplers 38 , such as NextStrain 16 and other commonly used phylogenetic tools; however, their employment can significantly add time to a rapid response. The UShER tool 37 , which can rapidly place genomes onto an existing SARS-CoV-2 phylogenetic tree, can greatly speed-up the final analysis. Parsing the output of UShER generates a subset of public genomes that are phylogenetically close to the samples of interest. This reduces the input dataset to subsamplers such as genome-sampler 38 , which significantly reduces the computation time for further subsampling based on geography and time, which in turn significantly reduces the computation time for a NextStrain analysis.

Each of the two methods have their limitations. We observed a reduced success rate of the rapid Illumina DNA Prep method over the high-throughput plexWell system, as evidenced by both decreased overall breadth of coverage and decreased success of obtaining complete genomes at Ct values above 30. It should be noted, however, that rapid prep study was conducted on a limited sample set, using samples from active outbreak clusters that were shipped from long distances through varying ambient temperatures. Samples used to evaluate the plexWell system were obtained locally and processed entirely in-house. This difference in handling, coupled with the sample size difference, may in part account for the differences in results in the two prep methods. Also, at ~$100/sample in reagent costs, the rapid Illumina DNA Prep method is less cost-effective (Table 1 , Supplementary Data). And though the turnaround for the protocol is ~1.5 days, faster sequencing is achievable through other methods, such as through the use of long-read Nanopore sequencing 17,39 . Nanopore technology, however, has the disadvantage of having a higher per-base error rate when compared to short-read sequencing methods 17 , thus its lack of accuracy may outweigh any potential time savings, particularly in situations where relatively few nucleotide variants can radically alter phylogenetic placement such as for SARS-CoV-2. Also, the SNP-comparison analysis is rapid and robust because of the relatively low numbers of SNPs so far documented in the SARS-CoV-2 genome (at the time of submission of this manuscript) due to the virus's novelty, the lack of recombination, and the unmatched robustness of the global SARS-CoV-2 genome database. Though this method could be applied to other types of outbreaks, rapid, precise phylogenetic placement will rely on these same factors.

The high-throughput plexWell prep system also has its drawbacks. The turnaround time of this method, when large sample numbers are processed, is not competitive 39 . The majority of processing time is lost to the ARTIC portion of the protocol, however, and not specifically to the plexWell adapter addition. And though the combinatorial index system allows for thousands of samples to be multiplexed in a single sequencing run, the SARS pandemic has demonstrated that even this may not be sufficient to meet the data challenges presented by expansive disease outbreaks. This paper highlights the potential for rapid turnout of genomic data from epidemiological samples, giving genomics the potential to become invaluable in pandemic response. It is worth noting, however, that sequencing isn't the only potential bottleneck in the process. For data to be delivered in real-time, coordination between testing sites, sample delivery, and sequencing laboratories must be robust, so that preparation for sequencing can begin shortly after a sample has tested positive for the pathogen of interest. Failures anywhere in this chain can lead to significant delays, which negates the utility of a real-time sequencing pipeline for investigation. Thus, in addition to improvements in the described sequencing and analysis pipeline, coordinating the efforts of testing sites, public health partners, and sequencing laboratories is critical for this technology to be most effective.

Despite overwhelming benefits of employing next generation technological advances in real-time during a public health emergency, challenges remain when using genomic epidemiology as a means of pandemic control and monitoring. It has been demonstrated that genomics are not, in and of themselves, sufficient to completely elucidate the mechanisms and transmission of all pathogen-transmitted disease, particularly when asymptomatic and mild infections are known to play a role in transmission 43 but are less likely to be identified and subsequently sequenced. Thus, the integration of genomic and traditional epidemiology is paramount to the success of this 21 st century public health capability. 

This study proposes a reasonable approach to rapid assessment of viral diversity for SARS-CoV-2 taking advantage of the latest in Illumina sequencing kits and associated prep protocols as well as open source data and tools for downstream analyses. There are no statistics involved and therefore users have no idea how reliable any result might be coming out of such an approach. I would personally prefer public health decisions be based on accurate as well as rapid information. This paper speaks to rapid, but does not speak to accurate. Additionally, the paper describes some methodology that is unique to this work, but the authors do not provide any access to these crucial components. Therefore, as presented, the work seems to not be repeatable. This would need to be fixed before indexing. I have detailed these issues below.

One of the biggest issues we have when consulting with individuals or groups interested in SARS-CoV-2 sequencing is deciding on read quantity. The methods discusses 'coverage' for confidence in genome assembly, but there is no discussion at all on the number of reads targeted for sequencing. What is the target number of reads for the SNP analysis versus the whole genome analysis? Can you provide power calculations for identifying SNPs based on read coverage? Quality of assembled genomes based on read coverage? I think such analyses would be particularly helpful to individuals trying to implement this strategy in their own labs.

Is there a link to the 'global SNP database'? Is this an open access resource? If so, you should provide a link to it. If not, then the paper is not particularly useful because the work is not replicable.

Is the 'custom script' for SNP identification available? This should also be available via something like GitHub for reproducibility.

It's too bad the authors didn't go through the effort of consenting the sample donors and including clinical data. The genomic information is highly useful (as outlined in the paper), but the genomic information COUPLED with the EHR and epidemiological data would be infinitely more useful.

There don't seem to be any statistics associated with 'transmission network' assignment. How accurate is this inference and how is a user supposed to know about the accuracy of such an assignment?

Similarly, there are no statistics associated with the phylogenetic placement of genomes. What is the confidence a user would have in such placement?

If any results are presented, are all the source data underlying the results available to ensure full reproducibility? No

Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Partly Response: We agree that providing power calculations for SNPs based on read coverage is critical for phylogenetic analyses. The purpose of this paper is simply to report sequencing and analysis methods for the purpose of generating data that can then be fed into more thorough phylogenetic analyses, as we have done with Ladner et al 2020. This is reflected in the following:

is not able to be shared via secondary publications, as per GISAID's data sharing agreement. The contents of the database are described, with one table generated using the Chan et al.  35 analysis on genomes downloaded from GISAID, and the other table generated directly from GISAID data which is available for public download from GISAID's website, which is linked in the reference section. This is addressed via the following:

Lines 138-143: The format of this database was simply one table of SNPs, as generated by Chan et al. 35 , with columns for GISAID ID, position, reference base, and variant base, and a second table of metadata which contained the exact data fields, as downloaded from GISAID. The two tables were linked by the GISAID ID number. The GISAID database cannot be shared via secondary publications as per the database access agreement, however the database is available for download by the public from GISAID's website 16 .

Comment 7: Is the custom script for SNP identification available?

The custom script consisted of a simple set of commands, described in detail in the following so that they can be reproduced:

Lines 143-147: The list of SNPs common to samples in a given set was then compared to this global SNP database by first querying the number of global genomes containing each of the individual SNPs, sorting them from most common to least common, then further querying for number of global genomes containing combinations of SNPs by adding in each successive SNP in turn.

Comment 8: Genomic information coupled with the epidemiological data would be infinitely more useful

We completely support this comment. However, this was not done here as it exceeded the purpose of this paper, which was to provide methods for rapid and highthroughput sample processing and initial phylogenetic placement of genomes. We agree that obtaining patient metadata can provide for a more robust analysis and meaningful interpretation for a more complete epidemiological picture, and in our publications where that is the goal, we make every effort to arrange this.

Comment 9: There don't seem to be any statistics associated with the "transmission network"

Response: Thank you for this feedback. This language was changed to reflect the scope of the paper. We were not conducting full transmission analyses (as we overstated in the first draft), but rather, were using the obtained genomic data to guide field epidemiology. Current terminology reflects this, as per:

Lines 260-262: Generating consensus genomes and a SNP report from the sequence data, which takes approximately 15 minutes for a small (<=24 samples) dataset, quickly shows whether the samples are part of the same outbreak.

Comment 10: There are no statistics associated with the phylogenetic placement of genomes.

Response: This is correct. We have cited our previous phylogenetic analyses and other published works that have used and described similar methods. The purpose of this manuscript is primarily to report methods of generating data that can be fed into more complex phylogenetic analyses. We agree that statistics associated with placement are critical, and we have detailed complete phylogenetic analysis of SARS-CoV-2 genomes in Ladner et al. 2020, which follows the published methods of Bolyen et al 2020. Detailed phylogenetic analyses methods and results are not included here, as we felt that this was not part of this study. We have indicated this in the following:

Lines 336-340: However, reconstructing phylogenies, especially paired with finding relevant subsets, takes time, and is often overkill for initial, time-sensitive public health needs. We employ a simple, rapid analysis method and visualization meant as a quick-look to determine relatedness among a sample set of interest and/or relatedness of a sample or set to the entire public database of genomes.

Lines 349-351: This is not meant to replace more thorough phylogenetic analysis, but rather, to provide a quick genomic snapshot that can inform a public health ground response expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. This manuscript seeks to provide guidance to labs seeking to undertake rapid and or high throughput sequencing, and it reports the first use of the SeqWell plexWell system for use in conjunction with the commonly used ARTIC primer system. This novel method required few laboratory personnel and minimal equipment, and allowed for the rapid sequencing of thousands of samples per week, a novel pace for such a complex procedure. The manuscript has been reworded to highlight these developments. Changes mentioned here are reflected in the following lines of the manuscript:

Abstract: The high-throughput method, first reported here for SARS-CoV2, Lines 45-46: The plexWell method is a novel means of sequencing adapter addition to facilitate high-throughput sample processing. Lines 315-316: Here, we report the first use of plexWell for post-amplification adapter addition for whole genome sequencing of SARS-CoV2.

Lines: 321-325: With just two full-time staff members devoted to sequencing, and 2-4 parttime staff responsible for arraying samples prior to the ARTIC/plexWell protocol, 1152 samples were able to be successfully processed and sequenced each week. Thus, the number of genomes completed per full-time (40 hour) employee per week is 384, a novel pace for what is normally a lengthy protocol Comment 2: Focus on retrospective sequencing limits the potential value of the larger scale approach.

Response: We agree with this comment; however, sequencing was not merely done retrospectively. All data shown for the rapid-turnaround method were done in real-time, and data were delivered fast enough to inform a public health response. The manuscript has been edited to make this clear via the following (which includes a citation of the relevant MMWR report for this study).

Lines 352-355: The data presented here using the rapid-turnaround method were obtained from patients who were part of an active SARS-CoV2 outbreak, and the resulting phylogenetic placements were successfully used to inform public health efforts to determine the potential origin of the outbreak, and to prevent further spread 45 .

Comment 3: I would like to see the costs described in more detail.

We have now provided a breakdown of sequencing costs in the supplementary methods. This includes the manufacturer's reagent pricing, and represents the relative cost to the user minus any institution-specific discounts or advantages.

Comment 4: I find the title to be overstated

We now recognize that the title overstated the scope of this methods paper. We have changed the title to more closely represent the content of the publication.

Comment 5: I would like to see it acknowledged that currently many of the delays in SARS-CoV2 sequencing are not in the sample preparation and sequencing steps, rather in the processes up and downstream of sequencing.

We agree that delays aren't just in sequencing, and have acknowledged and described the other areas of potential delay in the manuscript, as per:

Lines 398-407: This paper highlights the potential for rapid turnout of genomic data from epidemiological samples, giving genomics the potential to become invaluable in pandemic response. It is worth noting, however, that sequencing isn't the only potential bottleneck in the process. For data to be delivered in real-time, coordination between testing sites, sample delivery, and sequencing laboratories must be robust, so that preparation for sequencing can begin shortly after a sample has tested positive for the pathogen of interest. Failures anywhere in this chain can lead to significant delays, which negates the

Whole-Genome Sequencing of Measles Virus Genotypes H1 and D8 During Outbreaks of Infection Following the 2010 Olympic Winter Games Reveals Viral Transmission Routes

The authors would like to thank Hayley Yaglom for her helpful review of the first draft and the members of the Arizona COVID Genomics Union (ACGU), for their insight and support of this work.

There is no discussion at all on the number of reads targeted for sequencing.Response: Thank you for this feedback. We have added read counts to all sequencing data metrics within the manuscript body. This is reported in the following paragraphs:Lines 217-229: The number of reads targeted per sample for the high-throughput method was approximately 348,000, considering the limitation of the number of index combinations (i.e., samples per run) that were available at the time. In practice, this varied considerably. The average read count for samples using the plexWell method was 411,375, with 393,925[MOU2] being the average number of reads that aligned to the WuHan-Hu-1 reference strain. Below a Ct of 33, average aligned reads was 475,310 (SD 260292, 95%CI 455789-494831) per sample. Between Cts of 33 and 35, the average reads aligned was 204,234 (SD 175947, 95%CI 163308-245160) per sample. Above 35, read counts dropped off sharply, with a mean of 99,388 reads aligning (SD 188499, 95%CI 68493-130283) per sample. Note that all samples were put through an equimolar pooling step by the plexWell system. This suggests this pooling is not entirely effective when Cts vary considerably in a given sample set. This was confirmed through Kruskal Wallis on the total (unaligned) read counts

This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Broad Institute of Harvard and MIT, Cambridge, MA, USA This is a methodologically sound study describing complementary approaches for generating and analyzing SARS-CoV-2 genomic data. The workflows address two common use cases: rapid turnaround of a relatively small number of samples, i.e. for cluster investigation, and larger scale but less urgently time sensitive sequencing.The approach described here is sound and there is sufficient detail to follow the methodology.My main issue with the manuscript is that it is not clear what the novel contribution is, since each of the approaches are previously described, using commercial and published methods. However it is nonetheless helpful to have the workflows mapped out as they are here, and to benchmark the differences between the approaches. This could be particularly helpful to the many labs now attempting viral/SARS-CoV-2 sequencing for the first time.I would also advise that the focus on retrospective sequencing limits the potential value of the larger scale approach. It could equally be relevant for larger scale surveillance sequencing, i.e. for variants of concern, real time (or near real time).I would like to see the costs described in more detail, and in relative terms, since costs of consumables and effort can vary widely across contexts. Please clarify what is included in costs in more detail, whether costs include effort, what the impact of effort is.Personally I find the title to be overstated for the content of the manuscript. I would suggest a title that more clearly conveys that this is a methods paper. In particular I would delete the prefix "Sequencing the pandemic", as this suggests something being done at a global scale, and the "21st century public health" as there is no direct application to public health.Finally, although this study clearly focuses on lab methods for sequencing, given the framing I would like to see it acknowledge that currently many of the delays in SARS-CoV-2 sequencing are not in the sample preparation and sequencing steps, rather in processes up and downstream of sequencing.

If any results are presented, are all the source data underlying the results available to ensure full reproducibility? YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed.Reviewer Expertise: Virology, Genomics, Epidemiology I confirm that I have read this submission and believe that I have an appropriate level of utility of a real-time sequencing pipeline for investigation. Thus, in addition to improvements in the described sequencing and analysis pipeline, coordinating the efforts of testing sites, public health partners, and sequencing laboratories is critical for this technology to be most effective.