The effect of variant interference on de novo assembly for viral deep sequencing

Abstract

Background:
Viruses have high mutation rates and generally exist as a mixture of variants in biological samples. Next-generation sequencing (NGS) approaches have surpassed Sanger for generating long viral sequences, yet how variants affect NGS de novo assembly remains largely unexplored.

Results:
Our results from > 15,000 simulated experiments showed that presence of variants can turn an assembly of one genome into tens to thousands of contigs. This "variant interference" (VI) is highly consistent and reproducible by ten commonly-used de novo assemblers, and occurs over a range of genome length, read length, and GC content. The main driver of VI is pairwise identities between viral variants. These findings were further supported by in silico simulations, where selective removal of minor variant reads from clinical datasets allow the "rescue" of full viral genomes from fragmented contigs.

Conclusions:
These results call for careful interpretation of contigs and contig numbers from de novo assembly in viral deep sequencing.

Full text

R E S E A R C H A R T I C L E Open Access
The effect of variant interference on de
novo assembly for viral deep sequencing
Christina J. Castro
1,2
, Rachel L. Marine
1
, Edward Ramos
3
and Terry Fei Fan Ng
1*
Abstract
Background: Viruses have high mutation rates and generally exist as a mixture of variants in biological samples.
Next-generation sequencing (NGS) approaches have surpassed Sanger for generating long viral sequences, yet how
variants affect NGS de novo assembly remains largely unexplored.
Results: Our results from > 15,000 simulated experiments showed that presence of variants can turn an assembly of
one genome into tens to thousands of contigs. This “variant interference”(VI) is highly consistent and reproducible
by ten commonly-used de novo assemblers, and occurs over a range of genome length, read length, and GC
content. The main driver of VI is pairwise identities between viral variants. These findings were further supported by
in silico simulations, where selective removal of minor variant reads from clinical datasets allow the “rescue”of full
viral genomes from fragmented contigs.
Conclusions: These results call for careful interpretation of contigs and contig numbers from de novo assembly in
viral deep sequencing.
Keywords: De novo assembly, Variant, Quasispecies, Virus, Microbe
Background
For many years, Sanger sequencing has been used to
complement classical epidemiological and laboratory
methods for investigating viral infections [1]. As tech-
nologies have evolved, the emergence of next-generation
sequencing (NGS), which drastically reduced the cost
per base to generate sequence data for complete viral ge-
nomes, has allowed scientists to apply viral sequencing
on a grander scale [2–4]. Genomic sequencing is ideal
for elucidating viral transmission pathways, characteriz-
ing emerging viruses, and locating genomic regions
which are functionally important for evading the host
immune system or antivirals [2,5].
Genomic surveillance of viruses is particularly import-
ant in light of their rapid rate of evolution. Viruses have
higher mutation rates than cellular-based taxa, with
RNA viruses having mutation rates as high as 1.5 × 10
−3
mutations per nucleotide, per genomic replication cycle
[6]. Due to this high mutation rate, it is well established
that most RNA viruses exist as a swarm of quasispecies,
[7] with each quasispecies containing unique single nu-
cleotide polymorphisms (SNPs). The presence of these
variants plays a key role in viral adaptation.
Due to viruses’rapid evolution, a single clinical sample
often contains a mixture of many closely related viruses.
Viral quasispecies are mainly derived from intra-host
evolution, with RNA viruses such as poliovirus, human
immunodeficiency virus (HIV), hepatitis C (HCV), influ-
enza, dengue, and West Nile viruses maintaining diverse
quasispecies populations within a host [8–15]. Con-
versely, the term “viral strains”often refers to different
lineages of viruses found in separate hosts, or a co-
infection of viruses in the same host due to multiple
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data made available in this article, unless otherwise stated in a credit line to the data.
* Correspondence: ylz9@cdc.gov
1
Division of Viral Diseases, National Center for Immunization and Respiratory
Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA
Full list of author information is available at the end of the article
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infection events. As a result, sequence divergence is usu-
ally higher when comparing viral strains compared to
quasispecies. In this study, we use the term “variant”to
encompass both quasispecies and strains regardless of
how the variants originated in the biological samples.
Since many sequencing technologies produce reads that
are significantly shorter than the target genome size, a
process to construct contigs, scaffolds, and full-length
genomes is needed. Reference-mapping and de novo as-
sembly are the two primary bioinformatic strategies for
genome assembly. Reference-mapping requires a closely-
related genome as input to align reads, while de novo as-
sembly generates contigs without the use of a reference
genome. Therefore, de novo assembly is the most suitable
strategy for analyzing underexplored taxa [16] or for vi-
ruses with high mutation and/or recombination rates.
The two most common graph algorithms employed by
de novo assembly programs are: overlap graphs for
overlap-layout-consensus (OLC) methods, and k-mer
based graphs for de Bruijn graph (DBG) methods. OLC
methods involve determining overlaps by performing a
series of pair-wise sequence alignments. Such assemblies
may be computationally expensive (especially for large
datasets), and generally work better with longer reads
[17,18]. Conversely, DBG assemblers split reads into
smaller k-mers, with k-mers connected when they share
a common prefix and suffix of length k –1. DBG
methods are usually faster to run than OLC methods,
but this strategy is known to be sensitive to repeats, se-
quencing error, and the presence of variants, which in-
crease the k-mer complexity and ambiguity during
sequence reconstruction [19,20]. These challenges could
lead to fragmented contigs when analyzing viral assem-
blies from clinical or environmental samples [21].
In this study, we first examined how often NGS and
de novo assembly were applied for viral sequences de-
posited in the GenBank nucleotide database (www.ncbi.
nlm.nih.gov/nucleotide/). Then, we investigated how the
presence of variants affected assembly results - simulated
and clinical NGS datasets were analyzed using multiple
assembly programs to explore the effects of genome
variant relatedness, read length, genome GC and
genome length on the resulting contig distribution. As
viruses in different taxa vary in length and GC content,
these experiments demonstrate how assembly of viral
variants is impacted by basic genome structure charac-
teristics, as well as by the nucleotide similarity between
variants and sequencing read length.
Results
The rise of NGS and de novo assembler use in GenBank
viral sequences
GenBank viral entries from 1982 to 2019 were collected
and analyzed, with extensive analyses performed to
evaluate technologies and bioinformatics programs cited
in records deposited between 2011 and 2019. Through
2019, there were over 2.7 million viral entries in
GenBank; however, over 70% (1.9 million) do not specify
a sequencing technology (Supplement Table S1) due to
the looser data requirement in earlier years. When look-
ing at recently deposited records (2014–2019), the
Illumina sequencing platform was the most common
NGS platform used for viral sequencing, with over a 2-
fold increase over the next most popular NGS platform
(Fig. 1d & e). When long sequences (≥2000 nt) are con-
sidered, NGS technologies surpassed Sanger in 2017 as
the dominant strategy for sequencing, comprising 53.8%
(14,653/27,217) of entries compared to 46.2% of entries
(12,564/27,217) for Sanger. This trend held true in 2018
and 2019 as well (Fig. 1f and Supplement Table S2).
Hybrid sequencing approaches, where researchers use
more than one sequencing technology to generate
complete viral sequences, have also become more com-
mon over the past several years. The most common
combination observed was 454 and Sanger (18,124 en-
tries), likely due to the early emergence of the 454 tech-
nology compared to other NGS platforms (Fig. 1c and
Supplement Table S3). However, combining Illumina
with various other sequencing platforms is quite com-
monplace (> 19,000 entries).
De novo assembly programs (ABySS, BWA, Canu,
Cap3, IDBA, MIRA, Newbler, SOAPdenovo, SPAdes,
Trinity, and Velvet) have increased from less than 1% of
viral sequence entries ≥2000 nt in 2012, to 20% of all
viral sequence entries in 2019 (Fig. 1h & i). A similar in-
crease was observed for reference-mapping programs
(i.e., Bowtie and Bowtie2), from 0.03% in 2012 to 12.5%
in 2019. Multifunctional programs that offer both as-
sembly options were the most common programs cited
for the years 2013–2019, but since the exact sequence
assembly strategy used for these records is unknown
(Tables S1-S5), the contributions of de novo assembly
are likely underestimated. An expanded summary of the
sequencing technologies and assembly approaches used
for viral GenBank records is available in the Supplement
text and Supplement Tables S1-S6.
Effect of variant assembly using popular de novo
assemblers
After establishing the growing use of NGS technologies
for viral sequencing, we next focused on understanding
how the presence of viral variants may influence de novo
assembly output. We generated 247 simulated viral NGS
datasets representing a continuum of pairwise identity
(PID) between two viral variants, from 75% PID (one nu-
cleotide difference every 4 nucleotides), to 99.6% PID
(one nucleotide difference every 250 nucleotides) (Fig. 2).
For Experiment 1, these datasets were assembled using
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Fig. 1 Trends and patterns of sequencing technology and assembly methods of viral entries in the GenBank database. aCumulative frequency
histogram of all viral entries in GenBank from Jan. 1, 1982 through Dec. 31, 2019 (total =2,793,810 entries). bCount of all viral entries with at least
one Sequencing Technology documented for the years 1982–2019. For panels (b) and (d), the “Other”category denotes entries with the
Sequencing Technology field omitted or mis-assigned. cRelationship between viral entries listing one or two Sequencing Technologies during
1982–2019. The number inside the circle indicates viral entries with only one Sequencing Technology listed; the number adjacent to the line
indicates entries combining two Sequencing Technologies. The thicker the connection line, the stronger the relationship. dand ePercentage ratio
graph of all viral entries with Sequencing Technology documented for the years 2010–2019, with (d) and without (e) the Other category. The
majority of entries in earlier years include omissions classified under the Other category, which is detailed in Supplement Table S1.fPercentage
ratio graph of viral entries with length greater than 2000 nt that have been documented with one of the seven Sequencing Technologies for the
years 2012–2019. The seven technologies include Sanger (n= 1) and NGS technologies (n= 6). gPercentage ratio graph of viral entries with
length greater than 2000 nt and that have been documented with one of the six NGS as the Sequencing Technology for the years 2012–2019.
Compared to panel (f), Sanger is excluded in this graph. hAssembly method of viral entries greater than 2000 nt, showing percentage ratio
graph of entries with at least one Assembly Method. For (h) and (i), the Other category describes assembly methods outside of the 18 most
popular programs investigated. iReclassification of panel (h) by the nature of the assembly methods. The programs can be grouped into de
novo assembler, reference-mapping, and software that can perform both
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10 of the most used de novo assembly programs (Fig. 2
and Supplement Figure S1a) to evaluate their ability to
assemble the two variants into their own respective con-
tigs as the PID between the variants increases.
One key observation is that the assembly result can
change from two (correct) contigs to many (unresolv-
able) contigs simply by having variant reads; the pres-
ence of viral variants affected the contig assembly output
of all 10 assemblers tested. The output of the SPAdes,
MetaSPAdes, ABySS, Cap3, and IDBA assemblers shared
a few commonalities, demonstrated by a conceptual
model in Fig. 3a. First, below a certain PID, when viral
variants have enough distinct nucleotides to resolve the
two variant contigs, the de novo assemblers produced
two contigs correctly (Fig. 3). We refer to this as “variant
distinction”(VD), with the highest pairwise identity
where this occurs as the VD threshold. Above this
threshold, the assemblers produced tens to thousands of
contigs (Fig. 3), a phenomenon we define as “variant
interference”(VI). As PID between the variants continue
to increase, the de novo assemblers can no longer distin-
guish between the variants and assembled all the reads
into a single contig, a phenomenon we define as “variant
singularity”(VS). (Fig. 3). The lowest pairwise identity
where a single contig is assembled is the VS threshold.
Slight differences in the variant interference patterns
(relative to the canonical variant interference model)
were observed for the 10 assemblers investigated. VD
was observed for SPAdes, MetaSPAdes, and ABySS as-
semblers. While it was not observed with Cap3 and
IDBA with the current simulated data parameters, we
speculate that VD may occur at a lower PID level for
these assemblers than tested in this study. The PID
range where VI was observed was distinct for each de
novo assembler (Fig. 3). During VI, SPAdes produced as
many as 134 contigs and ABySS produced 3076 contigs,
while MetaSPAdes, Cap3, and IDBA produced up to 10.
A different pattern was observed for Mira, Trinity, and
SOAPdenovo2 assemblers. The average number of con-
tigs generated by Mira, Trinity, and SOAPdenovo2 was
5, 36, and 283, respectively across all variant PIDs from
75 to 99.96%. Specifically, Mira and Trinity generated
fewer contigs at low PID, but produced many contigs
when the two variants reach 97.1% PID and 96.0% PID,
respectively. For SOAPdenovo2, a larger number of con-
tigs were produced regardless of the PID. This indicates
that these assemblers generally have major challenges
producing a single genome; this has been observed in
previous studies comparing assembly performance [22].
Finally, Geneious and CLC were the least affected by
VI in the simulated datasets tested, returning only 1–5
contigs for all pairwise identities. CLC’s assembly algo-
rithm primarily returned a single contig over the range
of PIDs tested (218/247 simulations; 88.3%), thus favor-
ing VS. In comparison, Geneious predominantly distin-
guished the two variants (234/247 simulations; 94.7%),
favoring VD.
Effect of GC content and genome length on variant
assembly
For Experiment 2, we focused our study on evaluating
whether VI observed in SPAdes de novo assembly is in-
fluenced by the GC content or genome length of the
pathogen. SPAdes was chosen because it produced a
Fig. 2 Workflow diagram of the investigation of variant simulated NGS reads through de novo assembly. First, in step 1, an artificial reference
genome and corresponding initial variant reads were created with varying constraints such as genome length, GC content, read length, and
assemblers, according to the experiment types as detailed in Supplement Figure S1. In the second step, an artificial mutated variant genome was
created. The process is repeated to generate 247 different mutated variants with controlled mutation parameters—starting with 1 mutation
every 4 nucleotides (75% PID) and ending with 1 mutation in every 250 nucleotides (99.6% PID). Mutated variant reads are also generated for
each of the mutation parameters. In the third and fourth steps, the initial and mutated variants were then combined and used as input for de
novo assembly for the three experiments, as detailed in Supplement Figure S1
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well-defined variant interference that closely resembled
the conceptual model (Fig. 2). It is also one of the lead-
ing assemblers for viral assembly (Fig. 1), possibly due to
its ability to assemble viral variants without variant
interference in most PID. Two datasets were used for
the evaluation: reads generated from four artificial ge-
nomes ranging in length from 2 Kb to 1 Mb, as well as
from genome sequences of poliovirus (NC_002058;
7440 nt in length) and coronavirus (NC_002645; 27,317
nt in length). No discernable correlation was observed
between the GC content of variant genomes and the de-
gree of VI for any of the simulated datasets (Supplemen-
tal Figure S1,p< 0.0001). Therefore, for subsequent
analyses examining the effects of genome length on VI,
the number of contigs at each PID level was obtained by
averaging the 13 GC simulations.
Notably, no matter the genome length, SPAdes pro-
duced vastly more contigs (i.e., VI) in a constant, narrow
range of PID (99–99.21%; Fig. 4a & b). The effect of var-
iants on assembly was characterized by the three distinct
intervals described previously: VD at lower PIDs, VI
(Fig. 4b), and VS at higher PIDs for all genome lengths.
For example, during VS, a single contig was generated
when the two variants shared ≥99.22% PID, but tens to
thousands of contigs were generated at a slightly lower
PID of 99.21%. This PID threshold, 99.21%, marked the
drastic transition from VI to VS, whereas the transition
from VD to VI (i.e., the VD threshold) occurred at
98.99% PID (Fig. 4b). A correlation was observed be-
tween genome length and the number of contigs pro-
duced during VI, where longer genomes returned
proportionally more contigs as expected as total VI oc-
currence should increase with length (r
2
= 0.967; p<
0.0001 Fig. 4b and c).
Effect of read length on variant assembly
The read length of a given NGS dataset will vary de-
pending on the sequencing platform and kits utilized to
generate the data. Since read length is an important fac-
tor for de novo assembly success, [23] we hypothesized
that it may also influence the ability to distinguish viral
variants. For Experiment 3, using SPAdes we investi-
gated assemblies with four typical read lengths: 50, 100,
150, and 250 nt. At longer read lengths, the VD thresh-
old occurred at higher PIDs (Fig. 4d & e). Also, with in-
creasing read length, the width of the PID window
where VI occurs gradually decreased from a 1.52%
spread to a 0.21% spread (Fig. 4e). This indicates that
longer reads are better for distinguishing viral variants
with high PIDs.
Fig. 3 . Variant interference in 10 de novo assemblers. aSchematic diagram depicting concepts of the VD, VI, and VS, and their relationship to
PID. bComparison of output from 10 different assemblers. The number of contigs produced by each de novo assemblers at different variant PID
ranges (75–99.6%) were shown. cClose-up of PID ranges where variant interference is the most apparent. Blue denotes de Bruijn graph
assemblers (DBG); green denotes overlap-layout-consensus assemblers (OLC); orange denotes commercialized proprietary algorithms. Variant
distinction, VD; variant interference, VI; variant singularity, VS. *For SOAPdenovo2, several data points returned zero contigs due to a well-
documented segmentation fault error. The y-axis denotes the number of contigs
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In silico experiments examining variant assembly with
NGS data derived from clinical samples
For clinical samples, assembly of viral genomes is af-
fected by multiple factors other than the presence of var-
iants, including sequencing error rate, host background
reads, depth of genome coverage, and the distribution
(i.e., pattern) of genome coverage. We next utilized viral
NGS data generated from four picornavirus-positive
clinical samples (one coxsackievirus B5, one enterovirus
A71, and two parechovirus A3) to explore VI in datasets
representative of data that may be encountered during
routine NGS. The NGS data for each sample was parti-
tioned into four bins of read data: (1) total reads after
quality control (T); (2) major variants only (M); (3)
major and minor variants only (Mm); and (4) major vari-
ants and background non-viral reads only (MB) (Fig. 5).
These binned datasets were then assembled separately
using three assembly programs: SPAdes, Cap3, and Gen-
eious. These programs were chosen as representatives of
different assembly algorithms: SPAdes is a leading de
Bruijn graph (DBG) assembler, Cap3 is a leading
overlap-layout-consensus (OLC) assembler, and Gen-
eious is a proprietary software. By comparing these ma-
nipulations, we aimed to test the hypothesis that minor
variants directly affect the performance of assembly
through VI in real clinical NGS data.
Even with an adequate depth of coverage for genome
reconstruction, assembly of total reads (T) in 11/12 ex-
periments resulted in unresolved genome construction –
resulting in numerous fragmented viral contigs (Fig. 6).
The only exception was one experiment where one sin-
gle PeV-A3 (S1) genome was assembled using Cap3.
When only reads from the major variant were assembled
(M), full genomes were obtained for all datasets using
SPAdes and Cap3, and for the CV-B5 sample using Gen-
eious. Conversely, assembly of the read bins containing
major and minor variants (Mm) resulted in an increased
number of contigs for 9 of the 12 sample and assembly
software combinations tested (Fig. 6), indicating that VI
due to the addition of the minor variant reads likely
Fig. 4 The effect of genome length and read length on de novo assembly of simulated variants across a range of percentage identities (PID). a&
bComparison of genome lengths. Six different genome lengths were assembled and the final contig counts were tallied across varying PID
thresholds (75–99.6%). For the simulated genome lengths of 2Kb, 10 kb, 100 Kb, and 1 Mb, the average of contig number at each PID was plotted.
Panel (b) shows the close-up view where interference was the most prominent. For all six genome lengths and each of the 13 iterations, VI
consistently occurred in the same range of PID (99.00–99.24%). The assembly makes a transition from VD to VI at the threshold of 99.00%, and it
makes a transition from VI to VS at the threshold of 99.24%. Also, the longer the genome length, the more contigs produced during VI. cThe
relationship between genome length and the total number of contigs produced. Data from panel (a) were plotted on a logarithmic scale. The
total number of contigs produced is significantly dependent on the genome size (r
2
= 0.967; p-value< 0.0001). dand eThe effect of read length
in variant assembly with a genome size of 100 K. Simulated data with four different read lengths were created and assembled, and the final
contig counts were tallied across varying PID thresholds (75–99.6%). Panel (e) shows the close-up view where interference was the most
apparent. When longer read lengths were used, the variant interference PID range was much narrower than when shorter read lengths were
used to build contigs
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adversely affected the assembly. The presence of back-
ground reads with major variant reads (MB) did not ap-
pear to affect viral genome assembly, as the UG
50
%
value, a performance metric which only considers
unique, non-overlapping contigs for target viruses [24],
was similar between M and MB datasets.
Discussion
Our analysis of the GenBank entries quantified the
decade-long expansion of NGS technologies and de novo
assembly for viral sequencing (Fig. 1). As the number of
viral sequences in public databases continues to grow,
an important question that naturally arises is how well
Fig. 5 The effect of variant interference in a real dataset from a clinical sample containing enterovirus A71 (EV-A71) and its variants. Fastq reads
were partitioned into four components: trimmed reads after quality control (T), major variant (M), minor variant (m), and background (B). These
reads were then combined into four different experiments: T, M, Mm, and MB and assembled using SPAdes. The contig representation schematic
showing the abundance and length of the generated contigs reveals the impact of variant interference on de novo assembly. The bar graphs
show the UG
50
% metric and the length of the longest contig. UG
50
% is a percentage-based metric that estimates length of the unique, non-
overlapping contigs as proportional to the length of the reference genome [24]. Unlike N
50
,UG
50
% is suitable for comparisons across different
platforms or samples/viruses. More clinical samples and viruses are analyzed similarly in Fig. 6
Fig. 6 The effect of variant interference on the assembly of four clinical datasets using three assembly programs. Fastq reads were partitioned
into four categories: total reads (T), major variant (M), minor variant (m), and background (B). These reads were then combined into four different
categories: T, M, major and minor variants (Mm), and major variant and background (MB). Datasets were assembled using SPAdes, Cap3, and
Geneious. The bar graphs show the UG
50
% metric and the length of the longest contig. Coxsackievirus B5, CV-B5; Enterovirus A71, EV-A71;
Parechovirus A3 (Sample 1), PeV-A3 (S1); Parechovirus A3 (Sample 2), PeV-A3 (S2)
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current de novo assembly programs perform for datasets
with viral variants. Viral variants are expected in bio-
logical samples, with the number of variants and the ex-
tent of the sequence divergence between variants related
to the mutation rate of the virus and the types of speci-
mens that are being investigated. For example, samples
containing rapidly evolving RNA viruses, such as polio-
virus, HIV, and HCV [9,11,25], environmental samples,
[26] and clinical samples from immunosuppressed indi-
viduals [27,28] usually harbor many variants. The ability
to accurately distinguish variants is imperative to inform
treatments (in the case of HIV and HCV), or determine
whether a subpopulation of a more virulent variant is
present.
Several experiments using simulated and clinical sam-
ple NGS data were performed to evaluate the ability of
genome assembly programs to distinguish genome vari-
ants. All assemblers investigated generated fragmented
assemblies when the data contained reads from two
closely related variants due to “variant interference”(VI).
Changes in pairwise identity (PID) as small as 0.01% be-
tween the two variants triggered an assembler to change
from producing one or two contigs to producing hun-
dreds of contigs. A quintessential example of this
phenomenon was the SPAdes assembly of EV-A71 se-
quences during the in silico experiments with clinical
NGS data. Assembly of major variant reads resulted in
one full length contig (Fig. 5), whereas assembly of data-
sets containing the major and minor variant reads (Mm
and T) were characterized by a number of contigs,
resulting in “cobwebs”of contig fragments when visual-
ized using Bandage (Supplement Figure S2)[29]. Even
though the de novo assembly graph linked the different
contig fragments, the assembly could not differentiate
the multiple routes of possible contig construction. We
speculate this is the main reason why VI occurs in the
context of de Bruijn graph assemblers.
The simulated experiments suggested that genome
length and read length influence VI; A longer genome
length will produce proportionally more contigs during
VI, whereas a longer read length decreases the PID
range where VI occurs (Fig. 4). While longer read length
improves assembly, unfortunately, platforms that pro-
duce long reads such as Oxford Nanopore and PacBio
have higher error rates [30]. Until long reads can be pro-
duced at high fidelity, researchers must continue to rely
on combining long- and short-read NGS datasets, and
genome polishing techniques [30].
The large number of contigs generated due to VI may
be overwhelming for most researchers, and for viral
ecology studies, could lead to over-estimation of species
richness for methods that use contig spectra to infer
richness, such as PHACCS or CatchAll [31–33]. This
phenomenon may also impact studies differently
depending on the overall goal for generating viral se-
quence data. For example, some researchers may only be
concerned with generating a single major consensus
genome, even when variants are detected in the data.
This is common during outbreak responses for patho-
gens such as Ebola virus or Middle East respiratory syn-
drome coronavirus, where detection of SNPs (indicative
of minor variants) is not immediately important. On the
other hand, some investigations could favor distinguish-
ing variants, such as for investigating the presence of
vaccine-derived poliovirus, where a small number of
SNPs may distinguish a vaccine-derived strain from a
normal vaccine strain genome [28].
The effects of VI could potentially be mitigated by
running multiple assembly programs. A previous study
testing bioinformatics strategies for assembling viral
NGS data found that employing sequential use of de
Bruijn graph and overlap-layout-consensus assemblers
produced better assemblies [22]. We speculate that this
“ensemble strategy”[22] may perform better because the
multiple assemblers complement one another by having
different VI PID thresholds. Future assembly approaches
could also consider resolving the VI problem by possibly
discriminating the major and minor variant reads first
(perhaps by coverage or SNP analysis), and then assem-
bling major and minor variant reads separately.
Since we observed VI occurring in simulated data from
2 Kb to 1 Mb genome lengths, we speculate that it may
not only affect viral data but also larger draft contigs of
bacteria and other microorganisms. Even though bacter-
ial mutation rates are much lower than those of most vi-
ruses, bacterial variants are common. For environmental
studies, bacterial metagenomes are known to contain
many related taxa and variants [34–37], and in clinical
investigations, minor bacterial variants can harbor SNPs
that provide resistance against antimicrobials. This
warrants future investigation into how the presence of
variants may impact the assembly of other microbial
datasets.
Conclusion
This study aimed to understand how variants affect as-
sembly. As an initial investigation, many confounding
factors were simplified for experimentation. Simulated
variants studied here only depicted periodic mutations,
set at regular intervals. However, in real viral data, SNPs
are never evenly distributed across the genome, with
zones of divergence and similarity [38,39]. Other im-
portant factors which influence genome assembly in-
clude sequencing error rates, presence of repetitive
regions, and coverage depth. We limited our experi-
ments to keep these factors constant in order to investi-
gate the sole effect of VI. A pilot experiment analyzing
three variants demonstrated the VI theory generally
Castro et al. BMC Genomics (2020) 21:421 Page 8 of 12
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

holds true even with multiple variants with varying PID
(Supplement Figure S3). Introducing a third variant
within the VI threshold (Set B) destabilized assembly
and caused the contig number to inflate. Through this
study, we demonstrated that reads from related genome
variants adversely affect de novo assembly. As NGS and
de novo assembly have become essential for generating
full-length viral genomes, future studies should investi-
gate the combined effects of the number and relative
proportion of minor variants, as well as additional as-
sembly factors (e.g., error rates) to supplement this
work.
Methods
Analyzing NGS and assembler usage in the virus
nucleotide collection in GenBank
Viral sequence entries from the GenBank non-redundant
nucleotide collection were obtained by downloading all se-
quences under the virus taxonomy through the end of
2019. A total of 2,793,810 GenBank entries were
investigated.
The total number of viral sequences submitted annu-
ally in GenBank through December 2019 was calculated
by filtering GenBank submissions by “virus,”followed by
application of the following additional filtering steps:
“genomic DNA/RNA”was selected and a “release date:
Jan 1 through Dec 31”was applied to find the total num-
ber of viruses for a given year. A custom script was used
to filter and count all documented sequencing technolo-
gies and assembly methods used for each GenBank
entry.
Creation of simulated variant genomes and reads
Simulated genomes were generated using custom scripts
that randomly assign each nucleotide over a designated
genome length with a weighted distribution dependent
on the GC content (Supplement Figure S1). The random
genomes were then screened using NCBI BLAST to en-
sure no similarity/identity existed to any classified or-
ganism (i.e., no BLAST hits). These simulated genomes
served as the initial variant genome (variant 1). To gen-
erate the mutated variant genomes (variant 2), a custom
script was used to systematically introduce evenly dis-
tributed random mutations at rates from 1 mutation in
every 4 nucleotides (75% PID) to 1 mutation in every
250 nucleotides (99.6% PID), incrementing by 1
nucleotide.
Following the generation of initial and mutated variant
genomes, high-quality fastq reads were generated using
ART, [40] simulating Illumina MiSeq paired-end runs at
50X coverage with 250 nt reads, DNA/RNA mean frag-
ments size of 500, and quality score of 93. Fastq reads
were combined in equal numbers for the initial and mu-
tated variants and used as input for subsequent de novo
assembly experiments (Supplement Figure S1). The same
process was utilized to generate the artificial genomes,
initial and mutated variant genomes, and reads for each
of the experiments.
Experiment 1: analyzing simulated reads from variants
using different de novo assembly programs
The simulated datasets containing reads from two vari-
ant genomes with nucleotide pairwise identity ranging
from 75 to 99.6% were analyzed using 10 different gen-
ome assembly programs using default parameters. The
de novo assembly algorithms used were either overlap-
layout-consensus (OLC) [Cap [41] and Mira [42,43]], de
Bruijn graph (DBG) [ABySS [44], IDBA [45], MetaS-
PAdes [46], SOAPdenovo2 [47], SPAdes [48], and Trin-
ity [49]], or commercial software packages [CLC
(https://www.qiagenbioinformatics.com/) and Geneious
[50]] whose assembly algorithms are proprietary (Sup-
plement Table S6). The simulation settings for the reads
were paired-end reads, 250 nt read length, and 50X
coverage. A total of 2470 assemblies (247 datasets per
genome X 10 assemblers) were analyzed (Supplement
Figure S1a).
Experiment 2: simulated data by varying genome length
and GC content
Artificial genomes were constructed for four genome
lengths: 2 Kb, 10 Kb, 100 Kb, and 1 Mb, with varying
GC content from 20 to 80%, in 5% increments (Supple-
ment Figure S1b). Datasets derived using one poliovirus
genome (NC_002058) and one coronavirus genome
(NC_002645) were also included in this analysis, repre-
senting the lower and upper genome length range typical
of RNA viruses. The original GC content was kept con-
stant for the poliovirus and coronavirus genomes. For all
of these genomes, simulated reads for initial and mu-
tated variants were generated as above.
A total of 13,338 SPAdes assemblies were generated,
which included 12,844 assemblies for the four artificial
genomes (247 datasets per genome X 4 artificial genome
lengths X 13 GC content proportions X 1 assembler)
and 494 assemblies for the poliovirus and coronavirus
datasets (247 datasets per genome X 2 genomes X 1 as-
sembler) (Supplement Figure S1b). JMP v13.0.0 (www.
sas.com) was used to calculate Pearson’s correlation and
Spearman’sρvalues to compare the association between
percent GC levels and the number of contigs produced
at each PID level. Since there was little statistical differ-
ence when comparing the contig numbers generated at
varying percent GC for each of the four genome length
datasets (Spearman’sρ= 0.8299 to 0.9801, p< 0.001)
(Supplement Excel file), the final contig number was av-
eraged across the 13 GC percentages at a given PID. The
average contig number was used for plotting the contig
Castro et al. BMC Genomics (2020) 21:421 Page 9 of 12
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

assembly results vs percent PID for each simulated gen-
ome length (Fig. 4a-b).
Experiment 3: simulated data by varying read length
Genome variants were generated as described above
(“Creation of simulated variant genomes and reads”)fora
genome of size 100 Kb with 50% GC; this was the starting
initial variant genome. In this simulation, initial and muta-
tion variant reads at four sequencing read lengths (50,
100, 150, and 250 nt) were created using ART. A total of
538 SPAdes assemblies were generated (47, 97, 147, and
247 datasets for the 50, 100, 150 and 250 nt read lengths,
respectively) (Supplement Figure S1c).
Evaluation of NGS datasets from clinical samples
Four datasets derived from clinical samples containing
picornaviruses (one enterovirus A71 [EV-A71], one cox-
sackievirus B5 [CV-B5] and two parechovirus A3 [PeV-
A3]) were analyzed for this experiment, as previous se-
quencing analysis using Geneious indicated the presence
of genome variants. The datasets were analyzed using an
in-house pipeline (VPipe), [25] which performs various
quality control (QC) steps and de novo assembly using
SPAdes. The post-QC reads were considered total reads
(T) and mapped to their respective reference genome in
order to determine the major and minor variants present
in each sample. Total reads which mapped with high
similarity (≥99%) to the major variant were categorized
as reads representing the major variant (M). Unbinned
reads from the major variant reference recruitment were
used to construct the minor variant consensus using a
second round of reference recruitment, and these reads
were categorized as the minor variant (m). Remaining
reads from the previous two steps were considered back-
ground (B) reads.
De novo assembly for each of the four clinical samples
was performed for the following binned NGS datasets:
(1) total reads only (T); (2) major variants only (M); (3)
major and minor variants only (Mm); and (4) major vari-
ants and background reads only (MB). This was repeated
with three assembly programs: SPAdes, Cap3, and Gen-
eious. The length of the longest contig produced from
each assembly and the performance metric UG
50
%[24]
were calculated to compare the results for these 48 as-
semblies (4 experiments X 4 viruses X 3 assemblers).
Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s12864-020-06801-w.
Additional file 1. Supplemental Text: Analysis of viral GenBank records
demonstrated the advent of NGS in viral sequencing in the last two
decades. Supplement Figure S1. Workflow diagrams of simulated data
from data creation through de novo assembly. Supplement Figure S2.
Analysis of the final contig assembly graphs for a clinical sample
containing enterovirus A71 (EV-A71) variants using Bandage.
Supplement Figure S3. Assembly with three simulated variants.
Supplement Table S1. Total counts from NCBI’s GenBank non-
redundant nucleotide database. Supplement Table S2. Total count of
sequencing technologies for sequences >2000 nt in the NCBI GenBank
non-redundant nucleotide database for years 2012–2019. Supplement
Table S3. Total counts from NCBI’s GenBank non-redundant nucleotide
database with multiple sequencing technologies listed per entry. Supple-
ment Table S4. Total counts from NCBI’s GenBank non-redundant nu-
cleotide database of all entries with three and four sequencing
technologies listed. Supplement Table S5. Total count of assembly pro-
grams used to generate sequences >2000 nt in the NCBI GenBank non-
redundant nucleotide database. Supplement Table S6. The 10 de novo
assemblers used for analysis of the simulated data, as categorized by their
underlying assembly algorithms.
Additional file 2. Number of contigs generated by SPades using the
simulated data of two variants differed from 75% PID to 99.6% PID, across
20%-80% GC content.
Abbreviations
CV-B5: Coxsackievirus B5; DBG: de Bruijn graph; EV-A71: Enterovirus A71;
HCV: Hepatitis C; HIV: Human immunodeficiency virus; M: Major variant reads;
MB: Major variant and background non-viral reads; Mm: Major and minor
variants reads; NGS: Next-generation sequencing; nt: Nucleotide;
OLC: Overlap-layout-consensus; PeV-A3: Parechovirus A3; PID: Pairwise
identity; SNP: Single nucleotide polymorphism; T: Total reads after quality
control; VD: Variant distinction; VI: Variant interference; VS: Variant singularity
Acknowledgements
The authors thank Dr. Steve Oberste for thoughtful suggestions on this work.
We also thank the CDC Office of Advanced Molecular Detection Scientific
Computing Team for their computing support.
Authors’contributions
All authors contributed to the conceptualization, data analysis, preparation,
and review of this manuscript. C.J.C, R.L.M., and T.F.F.N. wrote this
manuscript. The authors read and approved the final manuscript.
Funding
C.J.C, R.L.M., and T.F.F.N. were supported by Federal appropriations to the
Centers for Disease Control and Prevention.
Availability of data and materials
Sequencing reads for the experiments conducted using clinical specimens
are available through the NCBI Sequence Read Archive (SRA) –BioProject
accession number PRJNA577924. Reads from simulated datasets
(Experiments 1–3) are available upon request.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Division of Viral Diseases, National Center for Immunization and Respiratory
Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA.
2
Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA.
3
General
Dynamics Information Technology, Inc., contracting agency to the Office of
Informatics, National Center for Immunization and Respiratory Diseases,
Centers for Disease Control and Prevention, Falls Church, VA, USA.
Castro et al. BMC Genomics (2020) 21:421 Page 10 of 12
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Received: 2 March 2020 Accepted: 2 June 2020
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