Sequencing the pandemic: rapid and high-throughput processing and analysis of COVID-19 clinical samples&nbsp;for 21<sup>st</sup>&nbsp;century public health

Megan L. Folkerts; Darrin Lemmer; Ashlyn Pfeiffer; Danielle Vasquez; Chris French; Amber Jones; Marjorie Nguyen; Brendan Larsen; W. Tanner Porter; Krystal Sheridan; Jolene R. Bowers; David M. Engelthaler

doi:10.12688/f1000research.28352.1

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Method Article

Sequencing the pandemic: rapid and high-throughput processing and analysis of COVID-19 clinical samples for 21^st century public health

[version 1; peer review: 1 approved with reservations, 1 not approved]

Megan L. Folkerts ¹, Darrin Lemmer¹, Ashlyn Pfeiffer¹, [...] Danielle Vasquez¹, Chris French¹, Amber Jones¹, Marjorie Nguyen¹, Brendan Larsen², W. Tanner Porter¹, Krystal Sheridan¹, Jolene R. Bowers¹, David M. Engelthaler¹

Megan L. Folkerts ¹, Darrin Lemmer¹, [...] Ashlyn Pfeiffer¹, Danielle Vasquez¹, Chris French¹, Amber Jones¹, Marjorie Nguyen¹, Brendan Larsen², W. Tanner Porter¹, Krystal Sheridan¹, Jolene R. Bowers¹, David M. Engelthaler¹

PUBLISHED 26 Jan 2021

Author details Author details

¹ Pathogen Genomics Division, Translational Genomics Research Institute, Arizona, Flagstaff, AZ, 86005, USA
² Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA

Megan L. Folkerts
Roles: Conceptualization, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Darrin Lemmer
Roles: Data Curation, Formal Analysis, Investigation, Software, Writing – Original Draft Preparation, Writing – Review & Editing

Ashlyn Pfeiffer
Roles: Data Curation, Investigation

Danielle Vasquez
Roles: Investigation

Chris French
Roles: Data Curation, Formal Analysis, Software

Amber Jones
Roles: Investigation

Marjorie Nguyen
Roles: Investigation

Brendan Larsen
Roles: Supervision

W. Tanner Porter
Roles: Formal Analysis, Visualization, Writing – Review & Editing

Krystal Sheridan
Roles: Investigation

Jolene R. Bowers
Roles: Conceptualization, Data Curation, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

David M. Engelthaler
Roles: Conceptualization, Funding Acquisition, Project Administration, Supervision, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

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This article is included in the Cell & Molecular Biology gateway.

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This article is included in the Coronavirus collection.

Abstract

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 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, and we envision their use for infectious disease epidemiology in the 21^st Century.

Keywords

Genomic epidemiology, SARS-CoV2, targeted genomics, sequencing methods, phylogenetics,

Corresponding author: Megan L. Folkerts

Competing interests: No competing interests were disclosed.

Grant information: This work was funded in part by the NARBHA Institute and the Arizona Department of Health Services.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2021 Folkerts ML et al. This is an open access article 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.

How to cite: Folkerts ML, Lemmer D, Pfeiffer A et al. Sequencing the pandemic: rapid and high-throughput processing and analysis of COVID-19 clinical samples for 21^st century public health [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Research 2021, 10:48 (https://doi.org/10.12688/f1000research.28352.1) First published: 26 Jan 2021, 10:48 (https://doi.org/10.12688/f1000research.28352.1) Latest published: 21 Feb 2022, 10:48 (https://doi.org/10.12688/f1000research.28352.2)

Introduction

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^1–6. 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 clusters⁹, 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 coronavirus¹³, the origins of which have since been elucidated¹⁴. 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^15–17.

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 response¹⁸. 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 identified⁴. Real-time genomic tracking has already proven valuable in a number of instances, including the recent West-African Ebola outbreak^3,18.

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 local¹⁹ and global scale^5,20, in evaluating the effectiveness of early interventions⁵, and in identifying super-spreader events²¹. 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, high-throughput targeted genomic sequencing of complex samples. 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.

Methods

Sample information

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

RNA extraction

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 TNCL, and had been extracted following their FDA emergency use authorization 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.1²² and using the ARTIC v3 primer set²³. 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.

Figure 1. General workflow for the processing of complex samples.

Basic workflow for processing of either high-throughput or rapid-response samples.

Rapid-turnaround adapter addition and sequencing

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 described²⁴. 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).

High-throughput adapter addition and sequencing

In instances where retrospective data were needed from large numbers of samples, adapters were added with the plexWell384 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).

Data processing and analysis

Virus genome consensus sequences were built using the Amplicon Sequencing Analysis Pipeline (ASAP)^25,26. First, reads were adapter-trimmed using bbduk²⁷, and mapped to the Wuhan-Hu-1 genome²⁸ with bwa mem²⁹ 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.¹⁹. 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.

Phylogenetic inference

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 collection¹⁶ 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 described³⁰. The list of all variants and metadata for each sample were then put into a relational database for fast querying. The list of SNPs common to samples in a given set was then compared to this global SNP database using a custom script 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)³¹ and the “ggplot2”³² and “ggforce”³³ R-packages.

For subsequent, more sophisticated phylogenetic analyses, phylogenetic trees were constructed in NextStrain¹⁷, with genomes from GISAID subsampled by uploading our genomes of interest to the UCSC UShER (UShER: Ultrafast Sample placement on Existing tRee) tool³⁴, identifying relevant genomes (those most related to our genomes of interest), and further reducing that set of genomes when necessary with genome-sampler³⁵.

Consent

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

Results

High-throughput workflow

For the Seqwell 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 ~$50, which is comparable to similar sample prep methods (Table 1)³⁶. Success rates, defined as the percentage of samples with greater than 90% genome coverage, were similar to other previously described methods^36,37. 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%. (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 3), with an average breadth of coverage of 78%. Above a Ct of 35, ~18% of samples yielded a complete genome, and breadth of coverage dropped below 57% (Table 2).

Table 1. Cost and labor analysis of two Illumina-based prep methods for complex sample processing.

Workflow metrics for Illumina’s DNA prep method, and Seqwell’s plexWell method.

* 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.

** Time from raw sample to analyzed data

Sequencing method	Samples able to be processed^*	Cost per Sample	Personnel Needed	Turnaround time^**
Illumina DNA Prep	24	$93.54	1	48hrs
Seqwell plexWell	1152	$50.55	4-6	82hrs

Figure 2. Sequencing outcomes of SARS-CoV2-positive samples processed using Seqwell’s plexWell method.

A. Nucleocapsid-2 Ct value vs percent genome coverage and B. percent total reads mapped to SARS-CoV-2 for 897 SARS-CoV-2-positive samples sequenced using Seqwell’s plexWell 384 system.

Table 2. Sequence metrics of SARS-CoV2-positive samples processed using Seqwell’s plexWell method.

Average sequencing metrics for various RT-PCR cycle threshold values of samples sequenced using the plexWell 384 system, and either an Illumina MiSeq or an Illumina NextSeq550.

*Ct value reported is that for the nucleocapsid-2 gene

**Uniform depth of coverage was targeted for all samples

Average Cycle Threshold Value (n)^*	Average % aligned	Average % coverage	Average depth^**	% samples > 90% coverage
<20 (194)	96.64	92.36	3371.58	87.63
20-25 (210)	96.91	92.61	2848.11	85.24
25-30 (169)	95.67	91.11	2733.37	84.62
30-33 (110)	88.58	87.18	2113.09	74.55
33-35 (71)	74.68	77.85	1221.05	40.85
35-37 (52)	60.20	56.91	716.43	17.31
37+ (91)	38.28	52.71	576.09	18.68

Table 3. Sequence metrics for SARS-CoV2-positive samples processed using Illumina’s DNA Prep method.

Average sequencing metrics for various RT-PCR cycle threshold values of samples sequenced using the Illumina DNA Prep system and an Illumina MiSeq.

*Ct value reported is that for the nucleocapsid-2 gene

**Uniform depth of coverage was targeted for all samples

Average Cycle Threshold Value (n)^*	Average % aligned	Average % coverage	Average depth^**	% samples > 90% coverage
<25 (6)	96.56	94.53	1503.00	83.33
25-30 (8)	91.92	87.60	1115.25	50%
30-37 (3)	56.46	62.83	851.21	0%

Uniform depth of genome coverage across samples was targeted when pooling samples for sequencing, but depth generally decreased as Ct increased. Average depth of coverage was approximately 2766X through a Ct of 33. Past 33, coverage dropped off sharply, with an average depth of 1221X between Ct values of 33-35. Between Cts of 35 and 37, coverage was 716X, and above a Ct of 37, coverage dropped to 576X (Table 2).

Similarly, percent of total reads aligning to the SARS-CoV-2 reference genome decreased as Ct increased (Table 2, Figure 2). Up to a Ct of 30, most (~96%) of reads mapped to SARS-CoV-2. From Cts of 30 to 35, this noticeably decreased, and continued to drop as Cts rose. Beyond a Ct of 37, average percentage of reads aligning dropped to 38%.

Rapid-Turnaround Workflow

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.

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 gene-specific 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 or transmission network. 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 script 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.

Figure 3. Phylogenetic relationship of SARS-CoV2-positive samples to Wuhan-Hu-1 reference strain.

Stacked Venn “onion” diagram indicating the hierarchical nature of cluster-specific SNPs relative to the reference strain in global collection of SARS-CoV2 samples.

Although fewer data are available from the Illumina DNA prep method, as it was primarily used to process 5 or fewer samples at a time, data suggest that it performed slightly worse than the Seqwell system, with odds of obtaining a complete genome dropping to 0 at Ct values greater than 30 (Table 3).

Discussion

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). 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.

Typical analyses of virus genomes include phylogenetic tree construction to understand transmission patterns. Nextstrain¹⁷ and GISAID¹⁶ have been crucial to the SARS-CoV-2 scientific community for global and local epidemiologic understanding, and tools for smart subsampling (e.g. genome-sampler³⁵) 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 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. Our SNP queries followed by generation of a stacked Venn diagram (onion diagram) offer a much faster alternative or antecedent to complete phylogenetic analysis. 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 subsamplers³⁵, such as NextStrain¹⁷ and other commonly used phylogenetic tools; however their employment can significantly add time to a rapid response. The UShER tool³⁴, 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-sampler³⁵, 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 Seqwell 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 Seqwell 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). 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^18,36. Nanopore technology, however, has the disadvantage of having a higher per-base error rate when compared to short-read sequencing methods¹⁸, 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, 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 SeqWell prep system also has its drawbacks. The pooling strategy of the plexWell system prevents downscaling, thus small numbers of samples cannot be processed effectively with this system. The turnaround time of this method, when large sample numbers are processed, is not competitive³⁶. 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.

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 transmission³⁸ 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.

Data availability

Underlying data

Zenodo: Raw sequencing metrics from two different prep methods for obtaining SARS-CoV2 genomes, http://doi.org/10.5281/zenodo.4309900³⁹.

This project contains the following underlying data:

Sequencing_metrics_IlluminaDNAprep.xlsx- data used for generation of Table 3
Sequencing_metrics_Seqwellprep.xlsx- data used for generation of Figure 2 and Table 2

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgements

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.

Faculty Opinions recommended

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31. R: A language and environment for statistical computing. Version 4.0.2. R Foundation; 2020. Reference Source
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Version 2

VERSION 2 PUBLISHED 26 Jan 2021

Author details Author details

Megan L. Folkerts
Roles: Conceptualization, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Darrin Lemmer
Roles: Data Curation, Formal Analysis, Investigation, Software, Writing – Original Draft Preparation, Writing – Review & Editing

Ashlyn Pfeiffer
Roles: Data Curation, Investigation

Danielle Vasquez
Roles: Investigation

Chris French
Roles: Data Curation, Formal Analysis, Software

Amber Jones
Roles: Investigation

Marjorie Nguyen
Roles: Investigation

Brendan Larsen
Roles: Supervision

W. Tanner Porter
Roles: Formal Analysis, Visualization, Writing – Review & Editing

Krystal Sheridan
Roles: Investigation

Jolene R. Bowers
Roles: Conceptualization, Data Curation, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

David M. Engelthaler
Roles: Conceptualization, Funding Acquisition, Project Administration, Supervision, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This work was funded in part by the NARBHA Institute and the Arizona Department of Health Services.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (2)

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Published: 21 Feb 2022, 10:48

https://doi.org/10.12688/f1000research.28352.2

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Published: 26 Jan 2021, 10:48

https://doi.org/10.12688/f1000research.28352.1

© 2021 Folkerts ML et al. This is an open access article 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.

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Folkerts ML, Lemmer D, Pfeiffer A et al. Sequencing the pandemic: rapid and high-throughput processing and analysis of COVID-19 clinical samples for 21^st century public health [version 1; peer review: 1 approved with reservations, 1 not approved] F1000Research 2021, 10:48 (https://doi.org/10.12688/f1000research.28352.1)

NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.

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ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested

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Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions

Version 1

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Reviewer Report 21 Sep 2021

Keith Crandall, Computational Biology Institute, The George Washington University, Washington, DC, USA; Department of Biostatistics & Bioinformatics, The George Washington University, Washington, DC, USA

Not Approved

https://doi.org/10.5256/f1000research.31361.r92632

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?

Is the rationale for developing the new method (or application) clearly explained?

Yes
Is the description of the method technically sound?

Yes
Are sufficient details provided to allow replication of the method development and its use by others?

Partly
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

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: phylogenetics, bioinformatics, computational biology, infectious disease

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Report a concern

Author Response 21 Feb 2022

Megan Folkerts, Pathogen Genomics Division, Translational Genomics Research Institute, Arizona, Flagstaff, 86005, USA

21 Feb 2022

Author Response

Dr. Crandall,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully ... Continue reading Dr. Crandall,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully considered the comments, and have done our best to address each of them. We hope that the revised version of this manuscript addresses the concerns you voiced, and now meets your standards. The authors welcome further constructive criticism.

Attached is a point-by-point response to both reviews. Changes made to the manuscript are clearly stated, with line numbers included for your quick reference.

Sincerely,

Megan Folkerts, MS

mfolkerts@tgen.org

Translational Genomics Research Institute, North

Response to Reviewer 2 (Dr. Keith Crandall)

Comment 1: There are no statistics involved.

Response: We agree that this was an oversight, even for a methods paper. All sequencing metrics are now reported in terms of mean, standard deviation, and 95% confidence interval.

Comment 2: 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 in each of the above-mentioned groups (p <0.001)

Lines 289-296: A minimum of 42,000 reads were targeted for the Illumina DNA prep method, but as sample sizes were small for the outbreak in question, the average aligned reads per sample was much higher. For samples with Cts below 25, the average number of reads aligned to the reference was 275,258.7 (SD 136,816.2, 95%CI 165,784.9-384732.4). Between Cts of 25 and 30, average reads aligned was 200,810 (SD 86,999.43, 95%CI 140,523.6-261096.4). Below a CT of 30, average aligned reads dropped to 116,888 (SD 275257.7, 95%CI 53,002.2-180,773.8). Kruskal-Wallis revealed no significant difference between average aligned reads between these groups (P= 0.08).

Comment 3: What is the target number of reads for the SNP analysis vs the WGS analysis?

Response: The SNP analysis was run concurrently with the WGS analysis, and the read depth targeted for the SNP analysis is the same as that of the WGS analysis. Targeted read depth, and achieved read depth, has been added to the manuscript (see response to comment 2). A previously published study reported a depth of 10 reads sufficient for SNP calling of SARS-CoV2 genomes (Ladner et al 2020). In all instances, SNPs called using the methods published here far exceed this threshold. Average read depth per sample can be found in the supplementary data.

Comment 4: Can you provide power calculations for identifying SNPs based on read coverage?

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:

Lines 122-123: Consensus genomes generated using these methods include those from Ladner et al. ¹⁹. and are similar to those in Peng et al.⁴³ and Stoddard et al.⁴⁴

Comment 5: What is the quality of assembled genomes based on read coverage?

Response: Average depth of coverage, as it correlates to CT value and percent genome coverage, is summarized in Table 2 and 3. Average depth by sample across the same metrics is available in the Supplementary Methods.

Comment 6: Is there a link to the global SNP database?

Response: The global SNP database, which contains data directly downloaded from GISAID, 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 website¹⁶.

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

Response: 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

Response: 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 high-throughput 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
Dr. Crandall,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully considered the comments, and have done our best to address each of them. We hope that the revised version of this manuscript addresses the concerns you voiced, and now meets your standards. The authors welcome further constructive criticism.

Attached is a point-by-point response to both reviews. Changes made to the manuscript are clearly stated, with line numbers included for your quick reference.

Sincerely,

Megan Folkerts, MS

mfolkerts@tgen.org

Translational Genomics Research Institute, North

Response to Reviewer 2 (Dr. Keith Crandall)

Comment 1: There are no statistics involved.

Response: We agree that this was an oversight, even for a methods paper. All sequencing metrics are now reported in terms of mean, standard deviation, and 95% confidence interval.

Comment 2: 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 in each of the above-mentioned groups (p <0.001)

Lines 289-296: A minimum of 42,000 reads were targeted for the Illumina DNA prep method, but as sample sizes were small for the outbreak in question, the average aligned reads per sample was much higher. For samples with Cts below 25, the average number of reads aligned to the reference was 275,258.7 (SD 136,816.2, 95%CI 165,784.9-384732.4). Between Cts of 25 and 30, average reads aligned was 200,810 (SD 86,999.43, 95%CI 140,523.6-261096.4). Below a CT of 30, average aligned reads dropped to 116,888 (SD 275257.7, 95%CI 53,002.2-180,773.8). Kruskal-Wallis revealed no significant difference between average aligned reads between these groups (P= 0.08).

Comment 3: What is the target number of reads for the SNP analysis vs the WGS analysis?

Response: The SNP analysis was run concurrently with the WGS analysis, and the read depth targeted for the SNP analysis is the same as that of the WGS analysis. Targeted read depth, and achieved read depth, has been added to the manuscript (see response to comment 2). A previously published study reported a depth of 10 reads sufficient for SNP calling of SARS-CoV2 genomes (Ladner et al 2020). In all instances, SNPs called using the methods published here far exceed this threshold. Average read depth per sample can be found in the supplementary data.

Comment 4: Can you provide power calculations for identifying SNPs based on read coverage?

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:

Lines 122-123: Consensus genomes generated using these methods include those from Ladner et al. ¹⁹. and are similar to those in Peng et al.⁴³ and Stoddard et al.⁴⁴

Comment 5: What is the quality of assembled genomes based on read coverage?

Response: Average depth of coverage, as it correlates to CT value and percent genome coverage, is summarized in Table 2 and 3. Average depth by sample across the same metrics is available in the Supplementary Methods.

Comment 6: Is there a link to the global SNP database?

Response: The global SNP database, which contains data directly downloaded from GISAID, 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 website¹⁶.

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

Response: 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

Response: 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 high-throughput 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
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 21 Feb 2022

Megan Folkerts, Pathogen Genomics Division, Translational Genomics Research Institute, Arizona, Flagstaff, 86005, USA

21 Feb 2022

Author Response

Dr. Crandall,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully ... Continue reading Dr. Crandall,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully considered the comments, and have done our best to address each of them. We hope that the revised version of this manuscript addresses the concerns you voiced, and now meets your standards. The authors welcome further constructive criticism.

Attached is a point-by-point response to both reviews. Changes made to the manuscript are clearly stated, with line numbers included for your quick reference.

Sincerely,

Megan Folkerts, MS

mfolkerts@tgen.org

Translational Genomics Research Institute, North

Response to Reviewer 2 (Dr. Keith Crandall)

Comment 1: There are no statistics involved.

Response: We agree that this was an oversight, even for a methods paper. All sequencing metrics are now reported in terms of mean, standard deviation, and 95% confidence interval.

Comment 2: 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 in each of the above-mentioned groups (p <0.001)

Lines 289-296: A minimum of 42,000 reads were targeted for the Illumina DNA prep method, but as sample sizes were small for the outbreak in question, the average aligned reads per sample was much higher. For samples with Cts below 25, the average number of reads aligned to the reference was 275,258.7 (SD 136,816.2, 95%CI 165,784.9-384732.4). Between Cts of 25 and 30, average reads aligned was 200,810 (SD 86,999.43, 95%CI 140,523.6-261096.4). Below a CT of 30, average aligned reads dropped to 116,888 (SD 275257.7, 95%CI 53,002.2-180,773.8). Kruskal-Wallis revealed no significant difference between average aligned reads between these groups (P= 0.08).

Comment 3: What is the target number of reads for the SNP analysis vs the WGS analysis?

Response: The SNP analysis was run concurrently with the WGS analysis, and the read depth targeted for the SNP analysis is the same as that of the WGS analysis. Targeted read depth, and achieved read depth, has been added to the manuscript (see response to comment 2). A previously published study reported a depth of 10 reads sufficient for SNP calling of SARS-CoV2 genomes (Ladner et al 2020). In all instances, SNPs called using the methods published here far exceed this threshold. Average read depth per sample can be found in the supplementary data.

Comment 4: Can you provide power calculations for identifying SNPs based on read coverage?

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:

Lines 122-123: Consensus genomes generated using these methods include those from Ladner et al. ¹⁹. and are similar to those in Peng et al.⁴³ and Stoddard et al.⁴⁴

Comment 5: What is the quality of assembled genomes based on read coverage?

Response: Average depth of coverage, as it correlates to CT value and percent genome coverage, is summarized in Table 2 and 3. Average depth by sample across the same metrics is available in the Supplementary Methods.

Comment 6: Is there a link to the global SNP database?

Response: The global SNP database, which contains data directly downloaded from GISAID, 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 website¹⁶.

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

Response: 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

Response: 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 high-throughput 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
Dr. Crandall,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully considered the comments, and have done our best to address each of them. We hope that the revised version of this manuscript addresses the concerns you voiced, and now meets your standards. The authors welcome further constructive criticism.

Attached is a point-by-point response to both reviews. Changes made to the manuscript are clearly stated, with line numbers included for your quick reference.

Sincerely,

Megan Folkerts, MS

mfolkerts@tgen.org

Translational Genomics Research Institute, North

Response to Reviewer 2 (Dr. Keith Crandall)

Comment 1: There are no statistics involved.

Response: We agree that this was an oversight, even for a methods paper. All sequencing metrics are now reported in terms of mean, standard deviation, and 95% confidence interval.

Comment 2: 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 in each of the above-mentioned groups (p <0.001)

Lines 289-296: A minimum of 42,000 reads were targeted for the Illumina DNA prep method, but as sample sizes were small for the outbreak in question, the average aligned reads per sample was much higher. For samples with Cts below 25, the average number of reads aligned to the reference was 275,258.7 (SD 136,816.2, 95%CI 165,784.9-384732.4). Between Cts of 25 and 30, average reads aligned was 200,810 (SD 86,999.43, 95%CI 140,523.6-261096.4). Below a CT of 30, average aligned reads dropped to 116,888 (SD 275257.7, 95%CI 53,002.2-180,773.8). Kruskal-Wallis revealed no significant difference between average aligned reads between these groups (P= 0.08).

Comment 3: What is the target number of reads for the SNP analysis vs the WGS analysis?

Response: The SNP analysis was run concurrently with the WGS analysis, and the read depth targeted for the SNP analysis is the same as that of the WGS analysis. Targeted read depth, and achieved read depth, has been added to the manuscript (see response to comment 2). A previously published study reported a depth of 10 reads sufficient for SNP calling of SARS-CoV2 genomes (Ladner et al 2020). In all instances, SNPs called using the methods published here far exceed this threshold. Average read depth per sample can be found in the supplementary data.

Comment 4: Can you provide power calculations for identifying SNPs based on read coverage?

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:

Lines 122-123: Consensus genomes generated using these methods include those from Ladner et al. ¹⁹. and are similar to those in Peng et al.⁴³ and Stoddard et al.⁴⁴

Comment 5: What is the quality of assembled genomes based on read coverage?

Response: Average depth of coverage, as it correlates to CT value and percent genome coverage, is summarized in Table 2 and 3. Average depth by sample across the same metrics is available in the Supplementary Methods.

Comment 6: Is there a link to the global SNP database?

Response: The global SNP database, which contains data directly downloaded from GISAID, 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 website¹⁶.

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

Response: 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

Response: 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 high-throughput 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
Competing Interests: No competing interests were disclosed. Close
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Reviewer Report 16 Mar 2021

Bronwyn L. MacInnis, Broad Institute of Harvard and MIT, Cambridge, MA, USA

Approved with Reservations

https://doi.org/10.5256/f1000research.31361.r78623

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.

Is the rationale for developing the new method (or application) clearly explained?

Yes
Is the description of the method technically sound?

Yes
Are sufficient details provided to allow replication of the method development and its use by others?

Yes
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?

Yes
Are 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 expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

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Author Response 21 Feb 2022

Megan Folkerts, Pathogen Genomics Division, Translational Genomics Research Institute, Arizona, Flagstaff, 86005, USA

21 Feb 2022

Author Response

Dr. MacInnis,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully ... Continue reading Dr. MacInnis,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully considered the comments, and have done our best to address each of them. We hope that the revised version of this manuscript addresses the concerns you voiced, and now meets your standards. The authors welcome further constructive criticism.

Attached is a point-by-point response to both reviews. Changes made to the manuscript are clearly stated, with line numbers included for your quick reference.

Sincerely,

Megan Folkerts, MS

mfolkerts@tgen.org

Translational Genomics Research Institute, North

Response to Reviewer 1 (Dr. Bronwyn MacInnis)

Comment 1: It is not clear what the novel contribution is

Response: We appreciate this valuable feedback and hope we have provided some clarity. 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 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

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 spread⁴⁵.

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

Response: 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

Response: 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.

Response: 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 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.
Dr. MacInnis,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully considered the comments, and have done our best to address each of them. We hope that the revised version of this manuscript addresses the concerns you voiced, and now meets your standards. The authors welcome further constructive criticism.

Attached is a point-by-point response to both reviews. Changes made to the manuscript are clearly stated, with line numbers included for your quick reference.

Sincerely,

Megan Folkerts, MS

mfolkerts@tgen.org

Translational Genomics Research Institute, North

Response to Reviewer 1 (Dr. Bronwyn MacInnis)

Comment 1: It is not clear what the novel contribution is

Response: We appreciate this valuable feedback and hope we have provided some clarity. 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 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

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 spread⁴⁵.

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

Response: 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

Response: 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.

Response: 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 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.
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 21 Feb 2022

Megan Folkerts, Pathogen Genomics Division, Translational Genomics Research Institute, Arizona, Flagstaff, 86005, USA

21 Feb 2022

Author Response

Dr. MacInnis,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully ... Continue reading Dr. MacInnis,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully considered the comments, and have done our best to address each of them. We hope that the revised version of this manuscript addresses the concerns you voiced, and now meets your standards. The authors welcome further constructive criticism.

Attached is a point-by-point response to both reviews. Changes made to the manuscript are clearly stated, with line numbers included for your quick reference.

Sincerely,

Megan Folkerts, MS

mfolkerts@tgen.org

Translational Genomics Research Institute, North

Response to Reviewer 1 (Dr. Bronwyn MacInnis)

Comment 1: It is not clear what the novel contribution is

Response: We appreciate this valuable feedback and hope we have provided some clarity. 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 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

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 spread⁴⁵.

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

Response: 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

Response: 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.

Response: 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 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.
Dr. MacInnis,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully considered the comments, and have done our best to address each of them. We hope that the revised version of this manuscript addresses the concerns you voiced, and now meets your standards. The authors welcome further constructive criticism.

Attached is a point-by-point response to both reviews. Changes made to the manuscript are clearly stated, with line numbers included for your quick reference.

Sincerely,

Megan Folkerts, MS

mfolkerts@tgen.org

Translational Genomics Research Institute, North

Response to Reviewer 1 (Dr. Bronwyn MacInnis)

Comment 1: It is not clear what the novel contribution is

Response: We appreciate this valuable feedback and hope we have provided some clarity. 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 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

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 spread⁴⁵.

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

Response: 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

Response: 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.

Response: 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 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.
Competing Interests: No competing interests were disclosed. Close
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Version 2

VERSION 2 PUBLISHED 26 Jan 2021

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Bronwyn L. MacInnis, Broad Institute of Harvard and MIT, Cambridge, USA
Keith Crandall, The George Washington University, Washington, USA; The George Washington University, Washington, USA

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14 Mar 2022 | for Version 2

Bronwyn L. MacInnis, Broad Institute of Harvard and MIT, Cambridge, MA, USA

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Approved

The authors have addressed my comments in this version of the manuscript, congratulations on a good paper.

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 expertise to confirm that it is of an acceptable scientific standard.

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4 Views

07 Mar 2022 | for Version 2

4 Views Cite this report Responses(0)

Approved

The authors have responded well to my previous critiques. I think the paper now provides useful information for sequencing methods (read depth, coverage, etc.) and includes some statistics to justify some of the conclusions in the paper.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

phylogenetics, bioinformatics, computational biology, infectious disease

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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39 Views

21 Sep 2021 | for Version 1

39 Views Cite this report Responses(1)

Not Approved

Is the rationale for developing the new method (or application) clearly explained?

Yes
Is the description of the method technically sound?

Yes
Are sufficient details provided to allow replication of the method development and its use by others?

Partly
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

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

phylogenetics, bioinformatics, computational biology, infectious disease

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Author Response

21 Feb 2022

Megan Folkerts, Pathogen Genomics Division, Translational Genomics Research Institute, Arizona, Flagstaff, 86005, USA

Dr. Crandall,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully considered the comments, and have done our best to address each of them. We hope that the revised version of this manuscript addresses the concerns you voiced, and now meets your standards. The authors welcome further constructive criticism.

Attached is a point-by-point response to both reviews. Changes made to the manuscript are clearly stated, with line numbers included for your quick reference.

Sincerely,

Megan Folkerts, MS

mfolkerts@tgen.org

Translational Genomics Research Institute, North

Response to Reviewer 2 (Dr. Keith Crandall)

Comment 1: There are no statistics involved.

Response: We agree that this was an oversight, even for a methods paper. All sequencing metrics are now reported in terms of mean, standard deviation, and 95% confidence interval.

Comment 2: 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 in each of the above-mentioned groups (p <0.001)

Lines 289-296: A minimum of 42,000 reads were targeted for the Illumina DNA prep method, but as sample sizes were small for the outbreak in question, the average aligned reads per sample was much higher. For samples with Cts below 25, the average number of reads aligned to the reference was 275,258.7 (SD 136,816.2, 95%CI 165,784.9-384732.4). Between Cts of 25 and 30, average reads aligned was 200,810 (SD 86,999.43, 95%CI 140,523.6-261096.4). Below a CT of 30, average aligned reads dropped to 116,888 (SD 275257.7, 95%CI 53,002.2-180,773.8). Kruskal-Wallis revealed no significant difference between average aligned reads between these groups (P= 0.08).

Comment 3: What is the target number of reads for the SNP analysis vs the WGS analysis?

Response: The SNP analysis was run concurrently with the WGS analysis, and the read depth targeted for the SNP analysis is the same as that of the WGS analysis. Targeted read depth, and achieved read depth, has been added to the manuscript (see response to comment 2). A previously published study reported a depth of 10 reads sufficient for SNP calling of SARS-CoV2 genomes (Ladner et al 2020). In all instances, SNPs called using the methods published here far exceed this threshold. Average read depth per sample can be found in the supplementary data.

Comment 4: Can you provide power calculations for identifying SNPs based on read coverage?

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:

Lines 122-123: Consensus genomes generated using these methods include those from Ladner et al. ¹⁹. and are similar to those in Peng et al.⁴³ and Stoddard et al.⁴⁴

Comment 5: What is the quality of assembled genomes based on read coverage?

Response: Average depth of coverage, as it correlates to CT value and percent genome coverage, is summarized in Table 2 and 3. Average depth by sample across the same metrics is available in the Supplementary Methods.

Comment 6: Is there a link to the global SNP database?

Response: The global SNP database, which contains data directly downloaded from GISAID, 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 website¹⁶.

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

Response: 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

Response: 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 high-throughput 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

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

28 Views

16 Mar 2021 | for Version 1

Bronwyn L. MacInnis, Broad Institute of Harvard and MIT, Cambridge, MA, USA

28 Views Cite this report Responses(1)

Approved With Reservations

Is the rationale for developing the new method (or application) clearly explained?

Yes
Is the description of the method technically sound?

Yes
Are sufficient details provided to allow replication of the method development and its use by others?

Yes
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?

Yes
Are 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

Respond to this report

Responses (1)

Author Response

21 Feb 2022

Megan Folkerts, Pathogen Genomics Division, Translational Genomics Research Institute, Arizona, Flagstaff, 86005, USA

Dr. MacInnis,

We want to thank you for taking the time to review our paper and provide valuable insight and comments aimed at strengthening our manuscript. We have carefully considered the comments, and have done our best to address each of them. We hope that the revised version of this manuscript addresses the concerns you voiced, and now meets your standards. The authors welcome further constructive criticism.

Attached is a point-by-point response to both reviews. Changes made to the manuscript are clearly stated, with line numbers included for your quick reference.

Sincerely,

Megan Folkerts, MS

mfolkerts@tgen.org

Translational Genomics Research Institute, North

Response to Reviewer 1 (Dr. Bronwyn MacInnis)

Comment 1: It is not clear what the novel contribution is

Response: We appreciate this valuable feedback and hope we have provided some clarity. 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 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

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 spread⁴⁵.

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

Response: 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

Response: 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.

Response: 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 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.

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Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Sequencing the pandemic: rapid and high-throughput processing and analysis of COVID-19 clinical samples for 21st century public health

Abstract

Keywords

Introduction

Methods

Sample information

RNA extraction

Targeted amplification

Figure 1. General workflow for the processing of complex samples.

Rapid-turnaround adapter addition and sequencing

High-throughput adapter addition and sequencing

Data processing and analysis

Phylogenetic inference

Consent

Results

High-throughput workflow

Table 1. Cost and labor analysis of two Illumina-based prep methods for complex sample processing.

Figure 2. Sequencing outcomes of SARS-CoV2-positive samples processed using Seqwell’s plexWell method.

Table 2. Sequence metrics of SARS-CoV2-positive samples processed using Seqwell’s plexWell method.

Table 3. Sequence metrics for SARS-CoV2-positive samples processed using Illumina’s DNA Prep method.

Rapid-Turnaround Workflow

Figure 3. Phylogenetic relationship of SARS-CoV2-positive samples to Wuhan-Hu-1 reference strain.

Discussion

Data availability

Underlying data

Acknowledgements

References

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Sequencing the pandemic: rapid and high-throughput processing and analysis of COVID-19 clinical samples for 21^st century public health