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viruses
Article
A Comparison of Whole Genome Sequencing of
SARS-CoV-2 Using Amplicon-Based Sequencing,
Random Hexamers, and Bait Capture
Jalees A. Nasir 1, 2, †, Robert A. Kozak 3, †, Patryk Aftanas 3, Amogelang R. Raphenya 1,2 ,
Kendrick M. Smith 4, Finlay Maguire 5, Hassaan Maan 6, Muhannad Alruwaili 7,
Arinjay Banerjee 1,8,9 , Hamza Mbareche 3, 10, Brian P. Alcock 1,2, Natalie C. Knox 11, 12,
Karen Mossman 1,8,9 , Bo Wang 6,13, 14, Julian A. Hiscox 7, Andrew G. McArthur 1 ,2 ,*
and Samira Mubareka 3, 10
1Michael G. DeGroote Institute for Infectious Disease Research, McMaster University,
Hamilton, ON L8S 4K1, Canada; nasirja@mcmaster.ca (J.A.N.); raphenar@mcmaster.ca (A.R.R.);
banera9@mcmaster.ca (A.B.); alcockbp@mcmaster.ca (B.P.A.); mossk@mcmaster.ca (K.M.)
2Department of Biochemistry and Biomedical Sciences, McMaster University,
Hamilton, ON L8S 4K1, Canada
3Division of Microbiology, Department of Laboratory Medicine and Molecular Diagnostics,
Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada; rob.kozak@sunnybrook.ca (R.A.K.);
patryk.aftanas@sri.utoronto.ca (P.A.); hamza.mbareche@sri.utoronto.ca (H.M.);
Samira.Mubareka@sunnybrook.ca (S.M.)
4
Perimeter Institute for Theoretical Physics,
Waterloo, ON N2L 2Y5, Canada
; kmsmith@perimeterinstitute.ca
5Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada;
finlaymaguire@gmail.com
6Peter Munk Cardiac Centre, University Health Network, Toronto, ON M5G 2N2, Canada;
hmaan@uoguelph.ca (H.M.); bowang@vectorinstitute.ai (B.W.)
7Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool L69 3BX, UK;
Muhannad.Alruwaili@liverpool.ac.uk (M.A.); Julian.Hiscox@liverpool.ac.uk (J.A.H.)
8Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4K1, Canada
9McMaster Immunology Research Centre, McMaster University, Hamilton, ON L8S 4K1, Canada
10
Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A1, Canada
11 National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB R3E 3M4, Canada;
natalie.knox@canada.ca
12 Department of Medical Microbiology and Infectious Diseases, University of Manitoba,
Winnipeg, MB R3T 2N2, Canada
13 Department of Medical Biophysics, University of Toronto, Toronto, ON M5S 1A1, Canada
14 Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada
*Correspondence: mcarthua@mcmaster.ca
†These authors contributed equally to this work.
Received: 31 July 2020; Accepted: 12 August 2020; Published: 15 August 2020
Abstract:
Genome sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
is increasingly important to monitor the transmission and adaptive evolution of the virus.
The accessibility of high-throughput methods and polymerase chain reaction (PCR) has facilitated a
growing ecosystem of protocols. Two differing protocols are tiling multiplex PCR and bait capture
enrichment. Each method has advantages and disadvantages but a direct comparison with different
viral RNA concentrations has not been performed to assess the performance of these approaches.
Here we compare Liverpool amplification, ARTIC amplification, and bait capture using clinical
diagnostics samples. All libraries were sequenced using an Illumina MiniSeq with data analyzed
using a standardized bioinformatics workflow (SARS-CoV-2 Illumina GeNome Assembly Line;
SIGNAL). One sample showed poor SARS-CoV-2 genome coverage and consensus, reflective of
low viral RNA concentration. In contrast, the second sample had a higher viral RNA concentration,
Viruses 2020,12, 895; doi:10.3390/v12080895 www.mdpi.com/journal/viruses
Viruses 2020,12, 895 2 of 13
which yielded good genome coverage and consensus. ARTIC amplification showed the highest depth
of coverage results for both samples, suggesting this protocol is effective for low concentrations.
Liverpool amplification provided a more even read coverage of the SARS-CoV-2 genome, but at a
lower depth of coverage. Bait capture enrichment of SARS-CoV-2 cDNA provided results on par with
amplification. While only two clinical samples were examined in this comparative analysis, both the
Liverpool and ARTIC amplification methods showed differing efficacy for high and low concentration
samples. In addition, amplification-free bait capture enriched sequencing of cDNA is a viable method
for generating a SARS-CoV-2 genome sequence and for identification of amplification artifacts.
Keywords: SARS-CoV-2; genome sequencing; bait capture; amplicon sequencing
1. Introduction
The ongoing pandemic of COVID-19 has infected over 20 million people globally, of which over
750,000 have died (as of 13 August 2020) [
1
]. COVID-19 is caused by severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2), a novel coronavirus, which emerged in December 2019 [
2
]. As with any
outbreak of a novel pathogen, diagnostics are critical to assess infection in humans and to monitor
the extent of the spread of the pathogen. Critical components of outbreak analysis and pathogen
identification are second generation high-throughput short-read sequencing and third generation
long-read sequencing [
3
,
4
]. For COVID-19, the rapid development of diagnostic polymerase chain
reaction (PCR) was facilitated by the availability of genome sequences of SARS-CoV-2 isolates [
4
,
5
].
In addition, sequencing enables continuous monitoring of circulating strains of the virus to determine
any adaptive changes that the virus may accumulate, which may affect its detection, transmissibility,
and pathogenicity [
6
]. Sequencing will also serve an important function as antiviral and vaccine trials
roll out, identifying antiviral resistance determinants and vaccine escape mutants, and is essential
for detecting viral recombination. For reliable determination of genomic sequences, it is important
to have high quality starting genetic material, such as RNA from cultured SARS-CoV-2. Patient
samples, such as mid-turbinate swabs, may contain other viruses including seasonal coronaviruses
and are also dominated by host genetic material and resident respiratory flora. It is thus imperative
to evaluate the performance of genomic amplification and sequencing protocols needed to enhance
the derivation of SARS-CoV-2 specific genomic data. Two methods have been widely adopted to
obtain SARS-CoV-2 genome sequences from patient samples: (1) the use of SARS-CoV-2 specific PCR
primers (tiling multiplex PCR) [
7
] and (2) the use of bait capture to enrich the SARS-CoV-2 genomic
material [
8
–
10
]. These processes have their own advantages and disadvantages. Tiling multiplex PCR
allows for the amplification of numerous viral amplicons but can introduce synthetic artifacts with
subsequent cycles. Moreover, divergence from PCR primer sequences can result in suboptimal binding
resulting in lost information on genetic diversity or off-target hybridization. Alternatively, bait capture
enriches viral RNA by reducing the quantity of non-viral nucleotides, subsequently shrinking the total
sequencing volume of the sample. However, the generation of optimal baits requires prior knowledge
of the target virus, which is limited in the response against a novel virus. The primary objective of
this analysis is to compare genome sequencing results from direct amplification of the SARS-CoV-2
genome (i.e., the Liverpool or ARTIC PCR protocols) [
7
] with bait capture enrichment from COVID-19
patient swabs with markedly different viral RNA concentrations. Secondarily, we perform a genomic
analysis for a) genetic relatedness and b) diagnostic PCR primer mismatch.
Viruses 2020,12, 895 3 of 13
2. Methods
2.1. Clinical Isolates
Material from mid-turbinate swabs was collected from patients returning from travel during the
last week of January and the last week of February 2020. One patient was hospitalized [
11
] and the
other was managed as an outpatient with a less severe disease; both recovered. Diagnostic testing [
12
]
was performed at Public Health Ontario and the results were confirmed at the National Microbiology
Laboratory, Winnipeg, Manitoba. This work was approved by the Sunnybrook Institute Research
Ethics Board (amendment to 149–1994, 2 March 2020).
2.2. Genome Sequencing
Total nucleic acid was extracted from each mid-turbinate swab using the QIAamp Viral RNA Mini
kit (Qiagen, Hilden, Germany) without the addition of the carrier RNA. dsDNA for sequencing the
library preparation was synthesized using either the Liverpool SARS-CoV-2 amplification protocol
7
,
ARTIC SARS-CoV-2 amplification protocol (as described in https://artic.network/ncov-2019) [
7
],
or random priming using the Maxima H Minus Double Stranded cDNA Synthesis Kit (Thermo Fisher
Scientific, Waltham, MA, USA) with 2.5
µ
M random hexamers following the manufacturer’s protocol.
For the latter, in a PCR tube 1
µ
L of Random Primer Mix (ProtoScript II First Strand cDNA Synthesis
Kit, NEB, Ipswich, MA, USA) was added to 7
µ
L extracted RNA and denatured on a SimpliAmp
thermal cycler (Thermo Fisher Scientific, Waltham, MA, USA) at 65
◦
C for 5 min and then incubated on
ice. Ten
µ
L 2X ProtoScript II Reaction Mix and 2
µ
L 10X ProtoScript II Enzyme Mix were then added to
the denatured sample and cDNA synthesis was performed using the following conditions: 25
◦
C for
5 min, 48 ◦C for 15 min, and 80 ◦C for 5 min.
For the Liverpool protocol, primer sequences designed to overlap and amplify the entire
SARS-CoV-2 genome in two 15-plex reactions were generously shared by Public Health England. Two
100
µ
M primer pools were prepared by combining primer pairs in an alternating fashion to prevent
amplification of overlapping regions in a single reaction. After cDNA synthesis, in a new PCR tube
2.5
µ
L cDNA was combined with 12.5
µ
L Q5 High-Fidelity 2X Master Mix (NEB, Ipswich, MA, USA),
8.9
µ
L nuclease free water (Thermo Fisher Scientific, Waltham, MA, USA), and 1.1
µ
L of 100
µ
M primer
pool #1 or #2. PCR cycling was then performed as follows: 98
◦
C for 30 sec followed by 40 cycles of
98 ◦C for 15 sec and 65 ◦C for 5 min.
For the ARTIC protocol, 1
µ
L Random Primer Mix (ProtoScript II First Strand cDNA Synthesis
Kit, NEB, Ipswich, MA, USA) and 1
µ
L 10mM dNTP mix (NEB, Ipswich, MA, USA) was added to
8
µ
L extracted RNA and denatured on SimpliAmp thermal cycler (Thermo Fisher Scientific, Waltham,
MA, USA) at 65
◦
C for 5 min and then incubated on ice. 12.5
µ
L 2X ProtoScript II Reaction Mix and
2.5
µ
L 10X ProtoScript II Enzyme Mix were then added to the denatured sample and cDNA synthesis
performed using the following conditions: 25
◦
C for 5 min, 42
◦
C for 50 min and 80
◦
C for 5 min. After
cDNA synthesis, in a new PCR tube 2.5
µ
L cDNA was combined with 12.5
µ
L Q5 High-Fidelity 2X
Master Mix (NEB, Ipswich, MA, USA). To pool #1 mix 5.87
µ
L nuclease free water (Thermo Fisher
Scientific, Waltham, MA, USA), and 4.13
µ
L of 10
µ
M ARTIC version 3 primer pool #1 was added. To
pool #2 mix 5.95
µ
L nuclease free water (Thermo Fisher Scientific, Waltham, MA, USA), and 4.05
µ
L of
10
µ
M ARTIC version 3 primer pool #2 was added. PCR cycling was then performed as follows: 98
◦
C
for 30 sec followed by 35 cycles of 98 ◦C for 15 sec and 65 ◦C for 5 min.
cDNA synthesis (hexamers only) and PCR reactions (Liverpool amplicons) were purified using
RNAClean XP (Beckman Coulter, Brea, CA, USA) at 1.8x bead to amplicon ratio and eluted in 30
µ
L.
Combined ARTIC amplicons were purified at 1.0x bead to amplicon ratio and eluted in 30
µ
L. Two
µ
L
of amplified material was quantified using a Qubit 1X dsDNA HS (Thermo Fisher Scientific, Waltham,
MA, USA) following the manufacturer’s instructions. Illumina sequencing libraries were prepared
using Nextera DNA Flex Library Prep Kit and Nextera DNA CD Indexes (Illumina, San Diego, CA,
USA) according to manufacturer’s instructions. For both Liverpool and random hexamer cDNA
Viruses 2020,12, 895 4 of 13
libraries (but not ARTIC libraries), half of the prepared libraries were enriched for SARS-CoV-2 using
the myBaits Expert Virus SARS-CoV-2 panel (Arbor Biosciences, Ann Arbor, MI, USA) following the
manufacturer’s protocol with a 20 h hybridization time at 65
◦
C and KAPA HiFi HotStart ReadyMix
(Roche, Basel, Switzerland) for post-enrichment library amplification, while the other half of each
library was sequenced without enrichment. Paired-end 150 bp sequencing was performed for each
library on a MiniSeq with the 300-cycle mid-output reagent kit (Illumina, San Diego, CA, USA),
multiplexed with targeted generation of ~40,000 clusters per library. A negative control library with no
input SARS-CoV-2 RNA extract was included using ARTIC amplification.
2.3. Genome Assembly
We developed a complete standardized workflow for the assembly and subsequent analysis
for short-read sequencing, released as the SARS-CoV-2 Illumina GeNome Assembly Line (SIGNAL).
For the Liverpool and ARTIC amplification-based libraries, sequencing reads pools were combined
(as R1 and R2) where needed (i.e., Liverpool amplicons), Illumina adapter sequences were removed
and low quality sequences trimmed or removed using Trimmomatic (version 0.36) [
13
], and then
amplification primer sequences removed where needed (i.e., Liverpool and ARTIC amplicons) using
cutadapt (version 1.18) [
14
]. Final sequence quality and confirmation of adapter/primer trimming
were confirmed by FASTQC (version 0.11.5) [
15
]. The percentage of reads derived from SARS-CoV-2
RNA for each library was determined using Kraken2 (version 2.0.8-beta; using RefSeq complete viral
genomes/proteins) [
16
], all non-SARS-CoV-2 reads removed using parsing of HiSAT2 (version 2.1.0) [
17
]
alignments, coverage normalized (samtools mpileup depth of 100,000), and prediction of genome
sequenced performed by iVar variant detection (version 1.2, consensus minimum depth =10) [
18
].
From these results, assembly statistics were generated by QUAST (version 5.0.2) [
19
] and depths
of coverage were assessed by HiSAT2 (version 2.1.0) alignment of the sequencing reads against the
predicted genome sequence [
17
]. Lastly, sequence variation or coverage gaps in the reads was assessed
by BreSeq (version 0.35.0) analysis relative to GenBank entry MN908947
·
3 (the first genome sequence
reported from the original Wuhan outbreak, China) [
20
]. Separately, sequencing reads were assessed
against GenBank entry MN908947
·
3 using HiSAT2 (version 2.1.0) and visualized using the Integrative
Genomics Viewer [21].
2.4. Assessment of Clinical Diagnostic PCR Primers
Clinical diagnostic amplification PCR primer sequences were designed in house using Geneious
v9.0 (https://www.geneious.com), collated from literature [
22
–
26
] and the World Health Organization
website [
27
], and mapped to the MN908947
·
3 SARS-CoV-2 genome sequence and added as additional
reference for BreSeq analysis of the sequencing reads, highlighting any mismatches at PCR priming sites.
2.5. Molecular Epidemiology Analysis
To confirm the epidemiological origin of both isolates, the best genome sequence of each
was included in a uniform manifold approximation and projection (UMAP) involving the aligned
genomes of 8074 SARS-CoV-2 isolates (obtained from Global Initiative on Sharing All Influenza Data,
GISAID, https://www.gisaid.org) labelled by country of origin [
28
]. For UMAP, the approximate
genomic differences were estimated using DNA distance determined by the Kimura-80 model of DNA
evolution [
29
], after the removal of the first 55 and last 260 bp of the alignment. The same alignment
was used to generate a phylogenetic tree using a RAxML-HPC BlackBox at the CIPRES Science Gateway
with GTRGAMMA +I among site rate variation [
30
]. Both analyses excluded predicted homoplastic
sites within the alignment [31].
2.6. Data & Software Availability
The SIGNAL workflow is available at https://github.com/jaleezyy/covid-19-signal. Custom
software for uniform manifold approximation and projection (UMAP) is available at https://github.
Viruses 2020,12, 895 5 of 13
com/hsmaan/CovidGenotyper [
32
]. FASTQ sequences and assembly FASTA have been deposited in
NCBI Bioproject PRJNA636446, with assembly FASTA sequences additionally submitted to GISAID
(Wuhan-derived: EPI_ISL_413015 as submitted previously, Iran-derived: EPI_ISL_450747). Only
sequencing reads that aligned by HiSAT2 (version 2
·
1
·
0) to the SARS-CoV-2 MN908947
·
3 genome were
included in the deposited sequence files to avoid the release of sequences derived from patient DNA.
3. Result
3.1. Clinical Isolates
Two original clinical diagnostic samples from travelers returning to Canada were used for
genome sequencing; one from Wuhan, China (“Wuhan-derived”) and one from Iran (“Iran-derived”).
Sample RNA, for the generation of cDNA libraries, was extracted from mid-turbinate swabs that were
transported in a universal transport medium. The Wuhan-derived sample had a diagnostic qPCR cycle
threshold (Ct) value of 31.05 for the envelope (E) gene targets. The Iran-derived sample had Ct values
of 18.8 and 20.9 for the RNA-dependent RNA polymerase (RdRp) and E gene targets, respectively.
3.2. Genome Sequencing and Assembly
The number of paired reads and percentages of those reads that were derived from SARS-CoV-2
genetic material varied widely between library preparation protocols (Figure 1). In the Wuhan-derived
sample, the majority of read data was from the patient genome and therefore resulted in poor
SARS-CoV-2 genome coverage and consensus, potentially due to the higher Ct value of the initial
sample (i.e., less abundant or fragmented SARS-CoV-2 RNA). By contrast, sequencing data from the
Iran-derived isolate consisted predominantly of SARS-CoV-2 molecules and produced a high coverage
genome consensus (Table 1). However, ARTIC amplification led to superior results for both the Wuhan-
and Iran-derived samples (Table 1), strongly suggesting that the ARTIC protocol would be best for
samples with lower viral loads. On examining the sequencing results of the Iran-derived sample more
closely, we observed that the Liverpool amplification produced successful results with or without
subsequent bait capture enrichment, while cDNA synthesis using random hexamers led to lower
relative sampling of SARS-CoV-2 molecules in the sequencing library and poor genome coverage.
However, bait capture enriched SARS-CoV-2 cDNA molecules in the sample, producing genome
consensus and coverage on par with the Liverpool amplification approaches. None of the sequencing
protocols resolved the terminal 5
0
and 3
0
nucleotide sequences of the genomes, which was consistent
with other publicly available sequences (Table 2).
Examination of read coverage against the first genome sequence reported from the original Wuhan
(China) outbreak (GenBank MN908947
·
3) revealed that despite the better performance of the ARTIC
protocol for the Wuhan-derived sample, coverage was highly variable across the genome (Figure 2),
with ~10% of locations having less than 100x coverage, ~35% having 100–1000x coverage, and ~53%
having >1000x coverage. Amplification of the Wuhan-derived sample using Liverpool primers was
limited to a few regions of the genome with 0–100x coverage. This is in contrast to the Iran-derived
sample, which had >1000x coverage across >95% of the genome for all methods except the direct
sequencing of cDNA (for which ~99% of the genome still had 101–1000x coverage). On average, bait
capture enriched the Liverpool amplifications by 1.2 fold and the direct cDNA samples by 19.6 fold,
respectively. Although we did not perform secondary enrichment of ARTIC amplification products,
these results illustrate that secondary enrichment is not important for PCR amplicons, but valuable
for direct sequencing of cDNA. Notably, while ARTIC amplification led to the best overall results for
the Iran-derived sample, read alignment revealed several regions with low read coverage (Figure 2),
including a 319 bp coverage gap within the orf1ab gene (Table 2). This region falls within ARTICv3
0
s
amplicon 64 that has been widely reported to generate little to no sequence coverage [
33
]. In contrast,
the Liverpool amplification produced a more even read coverage across the genome (Figure 2).
Viruses 2020,12, 895 6 of 13
Viruses 2020, 12, x FOR PEER REVIEW 5 of 14
3. Result
3.1. Clinical Isolates
Two original clinical diagnostic samples from travelers returning to Canada were used for
genome sequencing; one from Wuhan, China (“Wuhan-derived”) and one from Iran (“Iran-
derived”). Sample RNA, for the generation of cDNA libraries, was extracted from mid-turbinate
swabs that were transported in a universal transport medium. The Wuhan-derived sample had a
diagnostic qPCR cycle threshold (Ct) value of 31.05 for the envelope (E) gene targets. The Iran-derived
sample had Ct values of 18.8 and 20.9 for the RNA-dependent RNA polymerase (RdRp) and E gene
targets, respectively.
3.2. Genome Sequencing and Assembly
The number of paired reads and percentages of those reads that were derived from SARS-CoV-
2 genetic material varied widely between library preparation protocols (Figure 1). In the Wuhan-
derived sample, the majority of read data was from the patient genome and therefore resulted in poor
SARS-CoV-2 genome coverage and consensus, potentially due to the higher Ct value of the initial
sample (i.e., less abundant or fragmented SARS-CoV-2 RNA). By contrast, sequencing data from the
Iran-derived isolate consisted predominantly of SARS-CoV-2 molecules and produced a high
coverage genome consensus (Table 1). However, ARTIC amplification led to superior results for both
the Wuhan- and Iran-derived samples (Table 1), strongly suggesting that the ARTIC protocol would
be best for samples with lower viral loads. On examining the sequencing results of the Iran-derived
sample more closely, we observed that the Liverpool amplification produced successful results with
or without subsequent bait capture enrichment, while cDNA synthesis using random hexamers led
to lower relative sampling of SARS-CoV-2 molecules in the sequencing library and poor genome
coverage. However, bait capture enriched SARS-CoV-2 cDNA molecules in the sample, producing
genome consensus and coverage on par with the Liverpool amplification approaches. None of the
sequencing protocols resolved the terminal 5′ and 3′ nucleotide sequences of the genomes, which was
consistent with other publicly available sequences (Table 2).
Figure 1. Plot showing the percent of sequencing reads mapping to the SARS-CoV-2 reference genome
against the total number of paired reads acquired from each library preparation. Each data point is
additionally labelled with a percent fraction and average read coverage of the SARS-CoV-2 genome.
Figure 1.
Plot showing the percent of sequencing reads mapping to the SARS-CoV-2 reference genome
against the total number of paired reads acquired from each library preparation. Each data point is
additionally labelled with a percent fraction and average read coverage of the SARS-CoV-2 genome.
Viruses 2020, 12, x FOR PEER REVIEW 9 of 14
Viruses 2020, 12, x; doi: FOR PEER REVIEW www.mdpi.com/journal/viruses
Examination of read coverage against the first genome sequence reported from the original
Wuhan (China) outbreak (GenBank MN908947·3) revealed that despite the better performance of the
ARTIC protocol for the Wuhan-derived sample, coverage was highly variable across the genome
(Figure 2), with ~10% of locations having less than 100x coverage, ~35% having 100–1000x coverage,
and ~53% having >1000x coverage. Amplification of the Wuhan-derived sample using Liverpool
primers was limited to a few regions of the genome with 0–100x coverage. This is in contrast to the
Iran-derived sample, which had >1000x coverage across >95% of the genome for all methods except
the direct sequencing of cDNA (for which ~99% of the genome still had 101–1000x coverage). On
average, bait capture enriched the Liverpool amplifications by 1.2 fold and the direct cDNA samples
by 19.6 fold, respectively. Although we did not perform secondary enrichment of ARTIC
amplification products, these results illustrate that secondary enrichment is not important for PCR
amplicons, but valuable for direct sequencing of cDNA. Notably, while ARTIC amplification led to
the best overall results for the Iran-derived sample, read alignment revealed several regions with low
read coverage (Figure 2), including a 319 bp coverage gap within the orf1ab gene (Table 2). This region
falls within ARTICv3′s amplicon 64 that has been widely reported to generate little to no sequence
coverage [33]. In contrast, the Liverpool amplification produced a more even read coverage across
the genome (Figure 2).
Mutation analysis of the well sequenced Iran-derived sample detected one synonymous
substitution and four non-synonymous substitutions for the orf1ab gene, plus one non-synonymous
substitution for the N gene (Table 2). While positions 8653 and 28,688 overlap ARTIC PCR primers
and could reflect the failed removal of primer sequences by the bioinformatics workflow, both were
independently confirmed by the Liverpool amplifications and bait captured cDNA. All five
substitutions were consistently supported by 100% of sequencing reads, except for L3606F in the
orf1ab gene using the Liverpool amplification, the detection of which by BreSeq [20] was obscured by
a deletion predicted by a minority of reads; nonetheless iVar [18] consensus generation supported
L3606F. This location has been flagged for possible homoplastic sequencing artifacts [31]. Sequencing
of cDNA (bait enriched or otherwise) and ARTIC amplification predicted an intergenic nucleotide
substitution at position 29,742 in 100% of sequencing reads, yet this was not observed in sequences
derived from the Liverpool amplification due to missing read coverage (Figure 2). This position is
very close to the polyA tail and while not flagged for exclusion due to poor alignment [31], manual
inspection of the read alignments highlighted imperfect mapping of a minority of reads, so this single
nucleotide polymorphism (SNP) should be viewed with caution. Finally, both Liverpool and ARTIC
amplification methods had minority read support (10.9–22.8% of reads) for a deletion starting at
position 11,074 or 11,082, which was not observed for sequencing of unamplified cDNA, but this
region has been highlighted for Illumina-specific sequencing artifacts [31].
Figure 2.
Mapping and semi-log depth of coverage of trimmed sequencing reads for each library
preparation against the first Wuhan SARS-CoV-2 genome sequence (NCBI accession: MN908947
•
3).
Y-axis dimensions vary among samples (maximum indicated beside label) and colored positions
reflect frequency of SNPs relative to the MN908947
•
3 genome among the reads (green =A, blue =C,
orange =G, red =T). The plus (+) symbol indicates secondary bait capture enrichment. SARS-CoV-2
genome length and organization is highlighted on top.
Viruses 2020,12, 895 7 of 13
Table 1.
Sequencing read and genome assembly statistics including the total raw read pairs obtained and fraction captured from SARS-CoV-2 RNA, the fraction of
29,903 bp MN908947.3 genome sequence covered, depth of coverage, and number of variants detected relative to MN908947.3.
Sample Amplification Enrichment
Number of
Paired
Reads
Reads from
SARS-CoV-2
(%)
SARS-CoV-2
Genome
Fraction (%)
Average
Depth of
Coverage
0–100x
Coverage
(%)
101–1000x
Coverage
(%)
>1000x
Coverage
(%)
# iVar
Variants
Negative ARTIC No 938,693 0.01 0 4.1x 99.2 0.8 0.1 n/a
Wuhan Liverpool No 883,212 0.52 19.587 37.9x 93.88 6.08 0.04 1
Wuhan Liverpool Yes 22,119 58.73 20.811 98.6x 89.6 6.8 3.6 1
Wuhan Hexamers No 585,396 0.01 0 0.3x 99.9 0.1 0.00 n/a
Wuhan Hexamers Yes 1536 1.56 0 n/a n/a n/a n/a n/a
Wuhan ARTIC No 2,271,152 73.86 59.104 15,604.0x 10.6 35.5 53.9 5
Iran Liverpool No 813,975 90.13 98.53 6528.3x 1.2 3.1 95.6 6
Iran Liverpool Yes 901,124 89.76 98.54 8214.4x 0.7 0.2 99.1 6
Iran Hexamers No 1,091,011 2.77 99.89 215.3x 0.43 99.56 0.00 7
Iran Hexamers Yes 619,661 89.17 99.83 4383.9x 0.2 0.3 99.6 7
Iran ARTIC No 1,935,748 88.25 99.31 14,032.7x 0.2 1.7 98.1 7
Viruses 2020,12, 895 8 of 13
Table 2.
Predicted mutations relative to the MN908947.3 SARS-CoV-2 genome for each library for the high titre Iran-derived sample identified by BreSeq analysis of
sequencing reads. Mutations within codons are underlined. All mutations were predicted by 100% of sequencing reads mapping to that position unless otherwise
noted. Mutations in bold existed in the final iVar-called genome sequence, while those in italics exist in the final iVar-called genome sequence but were obscured by
deletion predictions in the minority reads for BreSeq.
Mutation Liverpool Alone Liverpool +
Enrichment Hexamers Alone Hexamers +
Enrichment
ARTIC
Amplification
Clinical Diagnostic
Primer Mismatch
Unresolved 50sequence 259 bp 258 bp 40 bp 0 bp 49 bp
Unresolved 30sequence 200 bp 190 bp 77 bp 139 bp 67 bp
pos. 835 (orf1ab polyprotein) F190F (TTC→TTT) F190F (TTC→TTT) F190F (TTC→TTT) F190F (TTC→TTT) F190F (TTC→TTT) NIID_WH-1_R854
pos. 884 (orf1ab polyprotein) R207C (CGT→TGT) R207C (CGT→TGT) R207C (CGT→TGT) R207C (CGT→TGT) R207C (CGT→TGT) NIID_WH-1_R913
pos. 1397 (orf1ab polyprotein) V378I (GTA→ATA) V378I (GTA→ATA) V378I (GTA→ATA) V378I (GTA→ATA) V378I (GTA→ATA)
pos. 8653 (orf1ab polyprotein) M2796I (ATG→ATT) M2796I (ATG→ATT) M2796I (ATG→ATT) M2796I (ATG→ATT) M2796I (ATG→ATT) Spike_F1
pos. 9502 (orf1ab polyprotein) 5.0% of reads suggest
A3079A (GC
C→
GC
T
)
Spike_F1
pos. 11,074 (orf1ab
polyprotein)
11.8% of reads
suggest a deletion
between positions
10,809 and 13,203
11.8% of reads
suggest a deletion
between positions
10,809 and 13,203
10.9% of reads
suggest a deletion
between positions
10,809 and 13,203
Spike_F1
pos. 11,082 (orf1ab
polyprotein)
18.1% of reads
suggest a deletion
between positions
10,817 and 10,819
22.8% of reads
suggest a deletion
between positions
10,817 and 10,819
Spike_F1
pos. 11,083 (orf1ab
polyprotein) L3606F (TTG→TTT) L3606F (TTG→TTT) L3606F (TTG→TTT) L3606F (TTG→TTT) L3606F (TTG→TTT) Spike_F1
pos. 19,285–19,603 (orf1ab
polyprotein)
319 bp coverage gap
(no aligned reads);
amplicon 64
pos. 27,156 (membrane
glycoprotein)
5.3% of reads suggest
S212C (AGT→TGT)
pos. 28,688 (nucleocapsid
phosphoprotein) L139L (TTG→CTG) L139L (TTG→CTG) L139L (TTG→CTG) L139L (TTG→CTG) L139L (TTG→CTG) 2019-nCoV_N3-F
pos. 29,742 (intergenic) no coverage no coverage G→T G→T G→T
Viruses 2020,12, 895 9 of 13
Mutation analysis of the well sequenced Iran-derived sample detected one synonymous
substitution and four non-synonymous substitutions for the orf1ab gene, plus one non-synonymous
substitution for the Ngene (Table 2). While positions 8653 and 28,688 overlap ARTIC PCR primers
and could reflect the failed removal of primer sequences by the bioinformatics workflow, both
were independently confirmed by the Liverpool amplifications and bait captured cDNA. All five
substitutions were consistently supported by 100% of sequencing reads, except for L3606F in the orf1ab
gene using the Liverpool amplification, the detection of which by BreSeq [
20
] was obscured by a
deletion predicted by a minority of reads; nonetheless iVar [
18
] consensus generation supported L3606F.
This location has been flagged for possible homoplastic sequencing artifacts [
31
]. Sequencing of cDNA
(bait enriched or otherwise) and ARTIC amplification predicted an intergenic nucleotide substitution
at position 29,742 in 100% of sequencing reads, yet this was not observed in sequences derived from
the Liverpool amplification due to missing read coverage (Figure 2). This position is very close to
the polyA tail and while not flagged for exclusion due to poor alignment [
31
], manual inspection of
the read alignments highlighted imperfect mapping of a minority of reads, so this single nucleotide
polymorphism (SNP) should be viewed with caution. Finally, both Liverpool and ARTIC amplification
methods had minority read support (10.9–22.8% of reads) for a deletion starting at position 11,074
or 11,082, which was not observed for sequencing of unamplified cDNA, but this region has been
highlighted for Illumina-specific sequencing artifacts [31].
3.3. Assessment of Clinical Diagnostic PCR Primers
SARS-CoV-2 diagnostic PCRs rely on the efficient binding of primers to their designated targets.
Mutations in these regions will prevent primer annealing and produce false negative results. Thus, given
the critical importance of identifying mutations in diagnostic PCR target sites, our pipeline includes
mapping of diagnostic primer sequences [
22
–
27
] relative to the mutations detected. We identified a
number of these have single nucleotide mismatches in the Iran-derived sample, which was supported
by 100% of sequencing reads, as well as minority read support for loss of priming sites for the spike
protein (Table 2).
3.4. Molecular Epidemiology Analysis
There is very little variation among available SARS-CoV-2 genome sequences, as summarized
at GISAID (www.gisaid.org) and exemplified by our own detection of only 6 SNPs between the
original Wuhan genome and our Iran-derived sample. By utilizing a uniform manifold approximation
and projection (UMAP) [
28
] of genome sequence similarity, we were able to place this isolate in a
small cluster of genomes from Australia (11), China (4), India (4), Kuwait (3), Norway (1), Pakistan
(1), Taiwan (5), Turkey (4), USA (1), United Arab Emirates (3), and United Kingdom (1) (Figure 3).
Cross-referencing with GISAID metadata revealed that within this small cluster, isolates from Australia
(2 isolates), India (4 isolates), and Pakistan (1 isolate) also had travel history associated with the
outbreak in Iran. Unfortunately, GISAID did not contain sequences from Iran, but phylogenetic
analysis confirmed these UMAP results, placing our Iran-derived sample and the nearby UMAP
samples in a well supported clade (Figure S1). The incomplete genome sequence obtained for our
Wuhan-derived isolate precluded its inclusion in UMAP and phylogenetic analyses.
Viruses 2020,12, 895 10 of 13
Viruses 2020, 12, x FOR PEER REVIEW 10 of 14
Figure 2. Mapping and semi-log depth of coverage of trimmed sequencing reads for each library
preparation against the first Wuhan SARS-CoV-2 genome sequence (NCBI accession: MN908947•3).
Y-axis dimensions vary among samples (maximum indicated beside label) and colored positions
reflect frequency of SNPs relative to the MN908947•3 genome among the reads (green = A, blue = C,
orange = G, red = T). The plus (+) symbol indicates secondary bait capture enrichment. SARS-CoV-2
genome length and organization is highlighted on top.
3.3. Assessment of Clinical Diagnostic PCR Primers
SARS-CoV-2 diagnostic PCRs rely on the efficient binding of primers to their designated targets.
Mutations in these regions will prevent primer annealing and produce false negative results. Thus,
given the critical importance of identifying mutations in diagnostic PCR target sites, our pipeline
includes mapping of diagnostic primer sequences [22–27] relative to the mutations detected. We
identified a number of these have single nucleotide mismatches in the Iran-derived sample, which
was supported by 100% of sequencing reads, as well as minority read support for loss of priming
sites for the spike protein (Table 2).
3.4. Molecular Epidemiology Analysis
There is very little variation among available SARS-CoV-2 genome sequences, as summarized
at GISAID (www.gisaid.org) and exemplified by our own detection of only 6 SNPs between the
original Wuhan genome and our Iran-derived sample. By utilizing a uniform manifold
approximation and projection (UMAP) [28] of genome sequence similarity, we were able to place this
isolate in a small cluster of genomes from Australia (11), China (4), India (4), Kuwait (3), Norway (1),
Pakistan (1), Taiwan (5), Turkey (4), USA (1), United Arab Emirates (3), and United Kingdom (1)
(Figure 3). Cross-referencing with GISAID metadata revealed that within this small cluster, isolates
from Australia (2 isolates), India (4 isolates), and Pakistan (1 isolate) also had travel history associated
with the outbreak in Iran. Unfortunately, GISAID did not contain sequences from Iran, but
phylogenetic analysis confirmed these UMAP results, placing our Iran-derived sample and the
nearby UMAP samples in a well supported clade (Figure S1). The incomplete genome sequence
obtained for our Wuhan-derived isolate precluded its inclusion in UMAP and phylogenetic analyses.
Figure 3.
Uniform manifold approximation and projection (UMAP) involving the aligned genomes of
8075 SARS-CoV-2 isolates labelled by country of origin. The Iran-derived sample is indicated by an
arrow. The top inset illustrates the analysis of all 8075 isolates, labelled by region, with the zoomed
region indicated by the hashed box.
4. Discussion
Our results underscore the importance of presumptive viral load, based on qPCR cycle threshold,
for obtaining a complete SARS-CoV-2 genome sequence, reinforcing the findings of others [
34
].
While the Liverpool amplification primers provided a more even read coverage of the SARS-CoV-2
genome, amplification using the ARTIC primers was superior for obtaining a complete genome
sequence to the point where it was the only successful protocol for one of our samples. Yet ARTIC
amplification had regions of low or missing sequence coverage not seen with sequencing of cDNA or
the Liverpool amplification (Figure 2). Additionally, low Liverpool and ARTIC coverage at positions
~11,500 to ~13,000 was associated with minority read support for a deletion in the BreSeq analysis,
which was not supported by bait enriched cDNA sequencing. This region has been associated
with artifacts of Illumina sequencing of amplicons [
31
]. Yet our standardized iVar-based pipeline
(github.com/jaleezyy/covid-19-signal), compatible with and extending the Connor lab ARTIC nextflow
pipeline (github.com/connor-lab/ncov2019-artic-nf), was able to overcome these regions of low coverage,
favoring the majority reads to generate a final genome sequence. ARTIC amplification and sequencing
resulted in a 319 bp gap within the coding region for the orf1ab gene (amplicon 64) so this would
underpredict any SNPs in this region, while the Liverpool amplification was confirmed to miss a
possible intergenic SNP due to missing coverage at the 3
0
terminal region of the SARS-CoV-2 genome.
Considering the low variation observed to date among SARS-CoV-2 genomes, accurate prediction of
every possible SNP using a standardized workflow is of high importance for molecular epidemiological
analyses, phylogenetic tree generation, and molecular diagnostic assays. Additionally, it is important
for prioritizing virus isolates for subsequent analysis of glycosylation sites and other post-translational
modification, as well as cell-culture experiments to investigate
in vitro
phenotypes. Notably, the
Viruses 2020,12, 895 11 of 13
prediction of glycosylation sites using NetOGlyc (http://www.cbs.dtu.dk/services/NetOGlyc/) found no
differences between the original Wuhan genome (MN908947
·
3) and our Iran-derived isolate. However,
our work did detect mismatches for currently used diagnostics PCR primers, specifically in primers
designed by the CDC and the Japanese NID. Clinical laboratories should be aware of this, and
we suggest this should be part of ongoing genomic surveillance efforts. We also note that neither
amplification method (Liverpool or ARTIC) was perfect, but the results indicated that amplification-free,
bait capture enriched sequencing of cDNA is of high utility for the identification of amplification
artifacts and may additionally be useful for direct sequencing of SARS-CoV-2 RNA from cell culture.
Overall, the availability of alternate protocols permits confirmation of novel mutations by excluding
protocol-specific sequencing and analysis artifacts.
Understanding the advantages and limitations of different protocols is essential to population-level
whole genome sequencing of SARS-CoV-2 directly from clinical samples. Although the heterogeneity of
this source of material may be a limitation, particularly for samples with low quantities and/or quality
of RNA, it is the most feasible approach given the constraints of virus isolation. This approach also
produces sequences most closely reflecting those within the host. However, we also acknowledge that
this work is limited to two clinical samples, which give a preliminary outlook onto the efficacy of each
protocol. Additionally, our study only investigated one sample type and evaluation of these protocols
with other sample types (e.g., lower-respiratory tract samples) will be informative. Recently, Xiao and
colleagues performed comparative studies on sputum, throat swabs, anal swabs, and nasopharyngeal
swabs and reported that more viral reads were recovered from nasal swabs than any other sample
type [
35
], although it is not clear if they were using paired samples. This suggests protocol optimization
for other sample types is necessary. Overall, standardization and quality controls are necessary for
informative broad analyses and to enable DNA sequencing protocol implementation at regional sites
of care for enhanced turnaround time to generate actionable data.
Supplementary Materials:
The following are available online at http://www.mdpi.com/1999-4915/12/8/895/s1,
Figure S1: Clade within the larger 8,075 isolate phylogenetic tree containing the Toronto isolate derived from Iran,
with isolates associated by UMAP marked by an asterisk (red indicating travel history associated with the Iran
outbreak). Branch lengths represent evolutionary distance while node labels represent bootstrap support.
Author Contributions:
R.A.K. and S.M. developed the concept, P.A. performed the construction of sequencing
libraries and MiniSeq sequencing, B.P.A. performed biocuration of reference data, J.A.N., A.R.R. and A.G.M.
performed the bioinformatics analyses, A.B. and N.C.K. assisted in the interpretation of genomic data. K.M.S.,
F.M., A.R.R. and J.A.N. developed the SIGNAL workflow. H.M. (Hamza Mbareche) tested the analytical workflow
and helped with the interpretation of genomic data. H.M. (Hassaan Maan) performed the UMAP and phylogenetic
analyses. M.A. and J.A.H. provided the Liverpool amplification reagents and protocols. K.M., B.W., A.G.M. and
S.M. provided funding and supervised the entire project. All authors prepared the manuscript and approved the
final article. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the Canadian Institutes of Health Research grant PJT-156214.
Acknowledgments:
Technical discussion from Jared Simpson (Ontario Institute for Cancer Research) was greatly
appreciated. J.A.N. was supported by funds from the Comprehensive Antibiotic Resistance Database. B.P.A.
and A.R.R. were supported by Canadian Institutes of Health Research (CIHR) funding (PJT-156214 to A.G.M.).
Computer resources were supplied by Hewlett Packard Enterprise, Canada. K.M. is funded by CIHR and Natural
Sciences and Engineering Research Council of Canada (NSERC). A.B. is funded by NSERC. F.M. is supported by a
Donald Hill Family Fellowship in Computer Science. H.M. is supported by a postdoctoral fellowship from Fond
de Recherche du Qu
é
bec Nature et Technologie and is the recipient of the Lab Exchange Visitor Program Award
from the Canadian Society for Virology. S.M. and R.A.K. are supported by the McLaughlin Centre and the Toronto
COVID-19 Action Initiative from the University of Toronto. Methods development of an amplicon system for
SARS-CoV-2 by J.A.H. and M.A. is funded by the US Food and Drug Administration.
Conflicts of Interest:
The authors declare no competing interests. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
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2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
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