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\title{Ebola virus epidemiology, transmission, and evolution during seven months in Sierra Leone}

\author{Daniel Park}
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\section*{Acknowledgments}

This is the acknowledgements section.  You should replace this with your
own acknowledgements.

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The above \textbf{author list} is currently just the list of Authorea
users who have write access to this document. The \emph{real} author
list is compiled separately.

This article was published in \emph{Cell} on June 18, 2015.

\begin{itemize}
\tightlist
\item
  \href{http://dx.doi.org/10.1016/j.cell.2015.06.007}{Published article
  in Cell}.
\item
  \href{http://www.cell.com/cell/authors}{Instructions for Authors}.
\item
  \href{http://www.cell.com/cell/supplemental-information}{Instructions
  on Supplemental Material}.
\end{itemize}

Supplemental Materials are separate:
https://www.authorea.com/users/10734/articles/40335/\_show\_article

\section{Introduction}\label{introduction}

The 2013-2015 Western African Ebola virus disease (EVD) epidemic, caused
by the Ebola virus (EBOV) Makona variant \hyperref[csl:1]{kuhn_andersen_baize_b{\`{a}}o_bavari_berthet_blinkova_brister_clawson_fair_et al._2014}, is the
largest EVD outbreak to date, with 26,648 cases and 11,017 deaths
documented as of May 8, 2015 \hyperref[csl:2]{who_2015}. The outbreak, first
declared in March 2014 in Guinea and traced back to the end of 2013
\hyperref[csl:3]{baize_pannetier_oestereich_rieger_koivogui_magassouba_soropogui_sow_keïta_clerck_et al._2014}, has also devastated the neighboring countries of
Sierra Leone and Liberia, with additional cases scattered across the
globe. Never before has an EBOV variant been transmitted among humans
for such an sustained period of time.

Published EBOV Makona genomes from clinical samples obtained early in
the outbreak in Guinea (three patients) and Sierra Leone (78 patients)
\hyperref[csl:3]{baize_pannetier_oestereich_rieger_koivogui_magassouba_soropogui_sow_keïta_clerck_et al._2014}; \hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}, demonstrated that near-real-time sequencing could
provide valuable information to researchers involved in the global
outbreak response. Analysis of these genomes revealed that the outbreak
likely originated from a single introduction into the human population
in Guinea at the end of 2013 and was then sustained exclusively by
human-to-human transmissions. Genomic sequencing further allowed the
identification of numerous mutations emerging in the EBOV Makona genome
over time. As a consequence, the evolutionary rate of the Makona variant
over the timespan of the early phase of the outbreak could be estimated,
and predictions made on the potential of this new EBOV variant to escape
current candidate vaccines, therapeutics, and diagnostics
\hyperref[csl:5]{kugelman_sanchez-lockhart_andersen_gire_park_sealfon_lin_wohl_sabeti_kuhn_et al._2015}.

While the insights gleaned from sequencing early in the outbreak
informed public health efforts \hyperref[csl:6]{alizon_lion_murall_abbate_2014}; \hyperref[csl:7]{stadler_kühnert_rasmussen_du plessis_2014}; \hyperref[csl:8]{volz_pond_2014}, the continued
human-to-human spread of the virus raises questions about ongoing
evolution and transmission of EBOV. Our laboratory teams in Sierra
Leone, at Kenema (Kenema Government Hospital, KGH) and at Bo (US Centers
for Disease Control and Prevention, CDC) continued to perform active
diagnosis and surveillance in Sierra Leone following our initial study
\hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}. After a 6-month delay of sample shipment due to
regulatory uncertainty about inactivation protocols, we again began to
determine EBOV genome sequences. We have sequenced samples at high depth
and with technical replicates to characterize genetic diversity of EBOV
both within (intrahost) and between (interhost) individuals. To support
global outbreak termination efforts, we publicly released these genomes
prior to publication as they were generated, starting with a first set
of 45 sequences in December 2014 and continuing with regular releases of
hundreds of sequences through May 2015.

Here we provide an analysis of 232 new, coding-complete EBOV Makona
genomes from Sierra Leone. We compared these genomes to 86 previously
available genomes: 78 unique genomes from Sierra Leone
\hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}, 3 genomes from Guinea \hyperref[csl:3]{baize_pannetier_oestereich_rieger_koivogui_magassouba_soropogui_sow_keïta_clerck_et al._2014}, and 5 from
health-care workers infected in Sierra Leone and treated in Europe. We
use this combined data set obtained from 318 EVD patients during the
height of the epidemic in Sierra Leone and Guinea to better understand
EBOV transmission within Sierra Leone and between countries. In
addition, we use it to understand viral population dynamics within
individual hosts, the impact of natural selection, and the
characteristics of the now hundreds of new mutations that have emerged
over the longer course of the epidemic.

\section{Results}\label{results}

\subsection{232 New Ebola Virus Makona Genomes from Sierra
Leone}\label{new-ebola-virus-makona-genomes-from-sierra-leone}

We performed massively parallel genome sequencing on 673 samples from
two EVD patient cohorts. The first cohort included 575 blood samples
from 484 EVD patients confirmed by laboratory staff at KGH from June 16
through September 28, 2014. The second cohort included blood samples
from 88 EVD patients from throughout Sierra Leone confirmed at Bo by CDC
laboratory staff from August 20, 2014 through January 10, 2015. Samples
from both EVD cohorts were sequenced using previously described methods
(Experimental Procedures, \hyperref[csl:9]{matranga_andersen_winnicki_busby_gladden_tewhey_stremlau_berlin_gire_england_et al._2014}, \hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}).

We implemented a new computational pipeline, viral-ngs:v1.0.0, for viral
genomic \emph{de novo} assembly, intrahost variant calling, and genome
analysis and annotation. This pipeline is available via open-source
software \hyperref[csl:10]{park_jungreis_tomkins-tinch_lin_2015} and utilizes a generalized workflow engine
to run on a wide variety of computer hardware configurations
\hyperref[csl:11]{koster_rahmann_2012}. Through a partnership with DNAnexus, this pipeline
is also available in a secure cloud-compute environment to enable
consistent analyses across laboratories with limited computational
resources (Experimental Procedures).

Using this pipeline, we successfully assembled 232 EBOV Makona
coding-complete genomes (150 from KGH and 82 from the CDC cohort,
spanning 16 Jun to 26 Dec 2014). Each assembled sequence was at least
18.5 kb in length with a maximum of 6\% ambiguous base calls per genome.
The median assembly had 374X coverage, was 18.9 kb long, and had no
ambiguous bases. Despite extensive sequencing, successful full genome
assembly was difficult to obtain from the KGH cohort (73\% failed genome
assemblies; 374X mean coverage; Table S1), compared to a previous cohort
from the same laboratory, described in \hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014} (11\% failed
genome assemblies; 2,000X mean coverage). The high assembly failure rate
of the more recent KGH cohort is likely due to the mandatory in-country
implementation of a new EVD sample deactivation protocol and to long
delays for sample shipments amidst the outbreak response (see
Experimental Procedures). In contrast, only 7\% of samples from the CDC
cohort failed to assemble. However, these samples had been pre-selected
for sequencing based on high EBOV titers as estimated by qPCR. In
addition, the CDC cohort samples were collected more recently, did not
remain in lysis buffer for an extended period, and were subjected to a
different sample deactivation protocol than the KGH cohort samples.

While we are continuing attempts to glean genomic information from
compromised samples of the recent KGH cohort, important information may
have been lost. In particular, samples from many EBOV-infected
health-care workers at KGH, which could provide important insights into
hospital-based transmissions, were compromised.

In combination with the 86 previously published EBOV Makona genomes
\hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}, we analyzed a total of 318 genomes (see Experimental
Procedures), all aligned against the earliest sampled Guinean genome
(GenBank KJ660346.2). In this set, we observed 464 single nucleotide
polymorphisms (SNPs; 125 nonsynonymous, 176 synonymous, and 163
noncoding). We also observed five single-base insertions and two
double-base insertions in noncoding regions. We mapped all of the
variants to primer-binding sites for known sequence-based diagnostics
\hyperref[csl:5]{kugelman_sanchez-lockhart_andersen_gire_park_sealfon_lin_wohl_sabeti_kuhn_et al._2015} and found no mutations in these sites that were
present in more than one Sierra Leonean sample (Table S2).

We constructed a second, independent genome library for each of 150
high-quality samples from the KGH cohort to reliably determine intrahost
single nucleotide variants (iSNVs) at low frequencies
\hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}. We identified 247 iSNVs (25 insertion/deletions that
were excluded from all analyses, 73 nonsynonymous, 71 synonymous, and 78
noncoding), including 21 iSNVs shared by multiple patients.

Very recently, another 175 EBOV Makona genomes were published based on a
cohort from Sierra Leone, mostly sampled from the area of Freetown in
the Fall of 2014 \hyperref[csl:12]{tong_shi_liu_qian_liang_bo_liu_ren_fan_ni_et al._2015}. Although these data were not
included in our analyses, they are unlikely to significantly alter our
primary findings (Figure S1).

\subsection{Limited Ebola Virus Exchange Across the Sierra Leonean
Border}\label{limited-ebola-virus-exchange-across-the-sierra-leonean-border}

A previous study of EBOV Makona sequences elucidated viral transmission
and evolution during the early stages of the outbreak in Sierra Leone
\hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}, from late May to early June, 2014. The first
reported EVD cases in Sierra Leone stemmed from two genetically distinct
EBOV Makona lineages, believed to have been introduced from Guinea. One
of these lineages (SL1) was more closely related to the then-available
three Guinean genomes (2-5 mutations) than the second lineage (SL2),
which was characterized by 4 additional mutations. This finding
suggested that SL2 had evolved from SL1 some months before it was
observed in Sierra Leone. A third lineage (SL3), derived from SL2,
emerged in mid-June 2014. SL3 differs from SL2 by a single mutation at
position 10,218, first found as an intrahost variant (polymorphism
within one individual) at a low frequency. SL3 became the most prevalent
lineage in Sierra Leone during the first three weeks of the outbreak
there, with SL1 disappearing soon after the appearance of SL3. The
SL3-defining mutation is epidemiologically important, as it is the first
commonly circulating mutation observed to arise within Sierra Leone's
borders.

As the epidemic developed within Sierra Leone, the SL3 lineage continued
to dominate the viral population within the country, with no evidence
for additional imported EBOV lineages. In our data set, 97\% of the
genomes belong to SL3, and the remainder to SL2 (Figure 1A). These
results link all Sierra Leonean EVD cases to the initial introduction of
EBOV into Sierra Leone, and they provide further evidence that all EVD
cases during this outbreak arose from human-to-human transmission rather
than from further zoonotic introductions from the unknown EBOV
reservoir. This means that no newly imported viral diversity was
detected after the initial introduction \hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}; all newly
sampled viruses likely descended from those sequenced in the initial
weeks of the outbreak. The genetic similarity of these viruses suggests
importation from other countries was minimal, although we cannot
definitively rule out a re-introduction from elsewhere for the SL2
viruses (3\%) in our data set.

Similarly, publicly available EBOV genomes from this outbreak can shed
light on exportation of EBOV from Sierra Leone into other countries. All
published genomes from elsewhere, including 26 from Liberia and 4 from
Mali, lack the Sierra Leone-defining SL3 mutation (Figure 1B;
Experimental Procedures). Given that 97\% of Sierra Leonean EBOV
sequences are SL3, extensive exportation would result in the spread of
SL3 EBOV genomes, a spread that is not seen in the limited samples
available to date. At least in Sierra Leone, and with the exception of
events at the onset of the epidemic, transmission has likely been
primarily within national borders (Figure S2, Experimental Procedures),
rather than by free interchange with neighboring countries.

\subsection{Human-to-human Transmission of Multiple EBOV
Genomes}\label{human-to-human-transmission-of-multiple-ebov-genomes}

Intrahost variants (iSNVs) that appear during the course of the epidemic
may provide valuable information about human-to-human transmission. In
particular, shared iSNVs have been used to estimate the relative size of
the transmission bottleneck \hyperref[csl:13]{khiabanian_emmett_lee_rabadan_2015} and to identify
human-to-human transmission chains \hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}. In the current
data set, which includes 85 samples with at least one iSNV (Figure S3A),
several iSNVs are shared among two or more patients, often spanning
several months of the EVD epidemic (Figure 2A). The existence of shared
iSNVs could be explained by patient infection from multiple sources
(superinfection), sample contamination, recurring mutations (with or
without balancing selection to reinforce mutations), or co-transmission
of slightly diverged viruses that arose by mutation earlier in the
tranmission chain.

We can rule out superinfection and contamination as primary explanations
for the iSNVs in our data because none of the iSNVs are located at
common SNP positions. For example, a SNP at position 14,019 is at
intermediate frequency in the population (found in approximately 40\% of
samples we sequenced), and defines the SL4 lineage (see Figure 1A). If
superinfection were common among EVD patients, we would expect to
sometimes see both SL3 virus and SL4 virus in the same patient, which
would appear as an iSNV at that position. Contamination would result in
a similar pattern, with intermediate-frequency SNPs appearing as iSNVs
in contaminated samples. Additionally, contamination would be most
visible in low-coverage, low-RNA-content samples, because contaminants
would make up more of the RNA available for sequencing, whereas samples
with extremely high coverage would be the most visible contaminants
(Figure S3B). The highest coverage sample (G4960.1) contains genomes
belonging to lineage SL3 only and lacks the SL4 SNP, so if there were
widespread contamination we should see a low frequency iSNV at position
14,019 in SL4 samples with iSNVs. Since SL3 and SL4 samples were
processed together (8 of 9 sequencing batches contained multiple samples
from both lineages), and we saw no instances of an iSNV at that
position, we conclude that superinfection and contamination are not
important contributors to iSNVs.

The remaining possible sources for persistently shared iSNVs are
co-transmission and recurrent mutation. In either case, the iSNV could
be maintained by balancing selection, or could be evolving neutrally.
Figure 2A suggests that selection is not the primary cause of
persistence, since synonymous and nonsynonymous variants are equally
common among the shared iSNVs, and selective pressures are likely to be
different for the two classes of variant. All shared iSNVs are unlikely
to be simply the product of recurring mutation: if they were, they
should have a frequency spectrum heavily weighted toward low frequency,
characteristic of new mutations. However, that is not the case. For
example, the variant at position 18,911 is found at \textgreater{}15\%
frequency in eight different samples (Figure S3C), a much higher
frequency than expected if the change represented a \emph{de novo}
mutation in each sample.

In summary, we conclude that a combination of human-to-human
transmission and recurrent mutations is likely responsible for the iSNV
pattern observed in Figure 2A. This hypothesis is supported by the iSNV
at position 18,911: samples containing this variant often cluster on the
phylogenetic tree (Figure 2B), although more isolated samples may
represent separate mutation events. More generally, pairs of samples
that share an iSNV are typically located near one another
phylogenetically; these pairs are separated by an average of 0.16 years
of evolution, whereas random pairs are separated by an average of 0.30
years (\emph{p} \textless{} 10-4, randomization test). These results
suggests transmission of iSNVs in at least some cases, and therefore
suggest that the transmission bottleneck is wide enough to facilitate
the transmission of low or intermediate-frequency variants between
hosts.

\subsection{Viral Evolution During a Prolonged EVD
Epidemic}\label{viral-evolution-during-a-prolonged-evd-epidemic}

We previously reported that new mutations accumulated more rapidly in
the viral population early in the outbreak than over the long-term in
the reservoir \hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}. We hypothesized then that the higher
rate early in the outbreak resulted from incomplete purifying selection,
that is, that we were detecting transient nonsynonymous variants that
would later be removed by purifying selection \hyperref[csl:14]{pybus_rambaut_belshaw_freckleton_drummond_holmes_2006}; \hyperref[csl:15]{bedford_cobey_pascual_2011}. The
observed evolutionary rate is thus not an estimate of the underlying
mutation rate, since some deleterious mutations are purged by selection
before they can be detected. But neither is it an estimate of the
long-term substitution rate, since other deleterious mutations have not
been eliminated by selection at the time of analysis. We hypothesized
that the EBOV Makona evolutionary rate would decline following the
addition of genomes covering a longer evolutionary time scale. Such a
decline is well-characterized in other species \hyperref[csl:16]{duchene_holmes_ho_2014}; \hyperref[csl:17]{ho_phillips_cooper_drummond_2005}. With
the present data set we were able to examine the evolution of the virus
over a longer time period. We found that the most probable estimated
evolutionary rate of EBOV Makona is indeed markedly lower (mean
posterior rate = 1.25 \selectlanguage{ngerman}× 10-3 substitutions per site per year), and
closer to the long-term rate than to the rate estimated early in the
outbreak (Figure 3A, S4).

How purifying selection acts at different time scales can also be seen
in the distribution of mutations in the EBOV Makona genealogy.
Deleterious mutations are more likely to result in transmission-impaired
viruses and dead-end infections, and may therefore only be present in
individual patients. Mutations unique to individual patients are those
that occur on the external branches of the phylogenetic tree, whereas
internal branch mutations are those present in multiple samples in our
data set. Thus, in the model of incomplete purifying selection, we
expect external branches to be characterized by a higher rate of
nonsynonymous substitution than internal branches; in the latter,
selection has had more opportunity to filter out deleterious mutants.
Internal branches, by definition, have produced multiple descendent
lineages and are thus less likely to include mutations with fitness
costs. To test this hypothesis, we estimated the numbers of
nonsynonymous and synonymous changes on the virus genealogy and
recovered their accumulation rates (Figure 3B). Nonsynonymous mutations
indeed occurred at lower frequency on internal than on external
branches, suggesting that most are removed by purifying selection
because of their fitness costs and hence represent evolutionary dead
ends. Synonymous mutations, which likely have less impact on fitness,
occurred at more comparable frequencies on internal and external
branches.

The relationship between the effectiveness of purifying selection and
its duration is also apparent in the overall pattern of nonsynomymous
mutations in our data set. Selection filters the accumulation of coding
variants in the EBOV genome (Figure 3C, 4A). Nonsynonymous mutations,
which are more likely to be deleterious, occur as a decreasing fraction
of coding mutations as we analyze longer timescales: intrahost variants
\textgreater{} individual patients (external branches) \textgreater{}
multiple patients (internal branches) \textgreater{} between outbreaks.
The last, the fraction seen between outbreaks, represent the effect of
long periods of evolution in the unknown EBOV reservoir. As selection
acts to remove deleterious alleles over time, fewer nonsynonymous
mutations can be detected. This pattern holds true across the EBOV
Makona genome (Figure 4A).

\subsection{Possible Host Effects on the Viral
Genome}\label{possible-host-effects-on-the-viral-genome}

Although we observe less constraint on nonsynonymous changes during the
2013-2015 epidemic than between outbreaks, one anomaly is the genomic
sequence encoding the mucin-like domain of the EBOV glycoprotein (GP),
for which we observe more nonsynonymous substitutions than expected
under neutrality, both within and between EVD outbreaks. Selective
pressure acting on a region can be estimated with the standard statistic
\emph{d}N/\emph{d}S, which has an expected value of 1.0 for neutral
evolution and less than 1 for purifying selection; in the mucin-like
domain, the mean posterior \emph{d}N/\emph{d}S within this outbreak is
4.74, and between outbreaks is 1.44 (Figure 4A). GP is the only
surface-exposed viral protein on EBOV virions, and as such it is the
primary target of antibodies \hyperref[csl:18]{murin_fusco_bornholdt_qiu_olinger_zeitlin_kobinger_ward_saphire_2014}. This finding therefore
raises the possibility that antibodies might be driving diversifying
selection and rapid evolution in this region. This observation is based
on a very small number of substitutions (8 nonsynonymous and 4
synonymous within the outbreak), however, and is not statistically
significant (posterior probability that \emph{d}N/\emph{d}S is elevated
within-outbreak = 92.9\%); the situation should be clarified as more
sequencing becomes available. If diversifying selection is occurring
here, then the observed changes are very unlikely to represent
population-level selection for transmission among humans; this would
only occur if previously infected individuals were frequently being
exposed to new infections. Instead, we hypothesize that these changes
represent within-host selection for EBOV to escape a developing humoral
immune response.

To test the hypothesis that antibodies drive diversifying selection of
GP, we looked for enrichment of mutations within B cell epitopes in that
protein. Effective humoral immunity depends on antibody binding to
specific B cell epitopes \hyperref[csl:19]{becquart_mahlak{\~{o}}iv_nkoghe_leroy_2014}; \hyperref[csl:18]{murin_fusco_bornholdt_qiu_olinger_zeitlin_kobinger_ward_saphire_2014}. Using experimentally
determined B cell epitopes, obtained from the Virus Pathogen Database
and Analysis Resource (ViPR, \hyperref[csl:20]{pickett_sadat_zhang_noronha_squires_hunt_liu_kumar_zaremba_gu_et al._2012}), we found that
nonsynonymous mutations in GP do indeed occur more frequently in
epitopes than expected by chance (Figure 4B). This correlation supports
the hypothesis that humoral immunity exerts selective pressure on the
virus, driving immune evasion via accumulation of nonsynonymous
mutations within GP B cell epitopes.

Visual inspection identified a subset of sequences that are more likely
to be B cell escape variants (Figure 4C). In particular, three sequences
(e.g.~G4955.1) had a threonine-to-alanine mutation at GP amino acid
position 485, a conserved threonine which is required for \emph{in vivo}
protection by the 14G7 antibody \hyperref[csl:21]{olal_kuehne_bale_halfmann_hashiguchi_fusco_lee_king_kawaoka_dye_et al._2012}. Additionally, two
sequences had short stretches of T-to-C mutations in GP (4 or more
T-to-C mutations within a 200 nucleotide region, Figure 4C), both of
which occur within B cell epitopes.

Similar patterns of excess T-to-C mutations within short regions were
also observed by Tong et al. \hyperref[csl:12]{tong_shi_liu_qian_liang_bo_liu_ren_fan_ni_et al._2015}. In our dataset of 318
genomes, 5 possessed obvious stretches of T-to-C mutations within short
regions. We also tested more broadly whether excessive T-to-C mutation
occurred in all sequences. We found a significant enrichment of T-to-C
transitions relative to all other types of transitions (Figure 4D). To
determine if viral sequence divergence is related to T-to-C transition
enrichment, we compared relative T-to-C transition rates in sequences
with stretches of T-to-C mutations (n=5) to the top 5\% of remaining
sequences by sequence divergence (n=15), and to the bottom 95\% of
sequences (n=298) (Figure 4E). While the sequences with T-to-C stretches
showed the strongest T-to-C enrichment, we found moderate enrichment of
T-to-C transitions in the 5\% most divergent sequences.

\section{Discussion}\label{discussion}

Our findings from 232 EBOV Makona genomes sampled in Sierra Leone over 7
months during the 2013-2015 EVD outbreak in Western Africa demonstrate
the value of continued sequencing throughout an epidemic. We tracked the
movement of EBOV throughout Sierra Leone and determined the frequency of
EBOV movement into and out of that country. Although it is not unlikely
that the virus continued to cross the national borders of Sierra Leone
throughout the epidemic, these observations suggest that, at least in
late 2014, cross-border introductions were not an important factor in
the development of the epidemic. We were unable, however, to draw any
conclusions about export to Guinea, since few EBOV sequences from there
are currently available.

The sequence data display EBOV Makona evolution in the context of
prolonged human-to-human transmission, and provide an updated view of
genomic diversity. Based on the rates of nonsynonymous and synonymous
changes that are shared or are unique to an individual host, we
concluded that purifying selection becomes increasingly effective over
time, as it has more opportunity to remove deleterious mutants.

While the effects of purifying selection in this extended EVD outbreak
are clear, these evolutionary changes do not imply that positive
selection or adaptation to humans are occurring. Rather, the data
suggest that evolutionary changes over time through natural selection is
sufficient to remove newly arisen alleles that are less fit in the human
environment. To date, no published study has found experimental evidence
of selection for alleles beneficial to the virus within the current
outbreak.

It is important to recognize, however, that the long-term human-to-human
transmission observed during the 2013-2015 EVD outbreak is historically
unique for EBOV. At the beginning of each EVD outbreak, EBOV enters the
human population with little or no genetic diversity. In the case of the
current EVD outbreak, EBOV has now maintained fitness while expanding
across a much larger space of genetic diversity than in previous EVD
outbreaks, the largest of which comprised only 318 human infections.
This degree of diversity will undoubtedly affect researchers' ongoing
efforts to developing or improve improving candidate diagnostics,
vaccines, and therapeutics for EVD, many of which are targeting EBOV
sequences directly (PCR, nucleic-acid based therapeutics) or indirectly
(antibody cocktails).

The mucin-like domain of the EBOV glycoprotein, in contrast to the rest
of the EBOV genome, appeared to be under diversifying selection, based
on a high ratio of nonsyonymous to synonymous mutations. While not
statistically significant because of the small numbers of SNPs in the
region, our observation is in agreement with many previous studies
\hyperref[csl:22]{sanchez_trappier_ströher_nichol_bowen_feldmann_1998}; \hyperref[csl:23]{wertheim_worobey_2009}. As the EBOV GP, especially the mucin-like domain, is
the target of many antibodies, a plausible hypothesis is that the
humoral immune response exerts selective pressure on GP, resulting in an
accumulation of nonsynonymous mutations. In support of this hypothesis,
regions of GP corresponding to experimentally-determined B cell epitopes
are significantly enriched in nonsynonymous, but not in synonymous,
variants. There are two important caveats to this analysis: (1) these
epitopes are determined \emph{in vitro} and therefore may not be
epitopes \emph{in vivo} if they are not immunodominant, and (2) there is
no experimental evidence to suggest that the majority of observed
variants disrupt antibody binding to these epitopes.

While further experimental testing is required to validate an immune
evasion hypothesis, we have highlighted a few prime candidates to
consider. Genomes from three samples share a threonine-to-alanine
mutation at GP amino acid position 485, a position that is conserved
across all species of the Ebolavirus genus. This position is
indispensable for binding of the protective antibody 14G7
\hyperref[csl:21]{olal_kuehne_bale_halfmann_hashiguchi_fusco_lee_king_kawaoka_dye_et al._2012}; the observed variant at this site may therefore be
the result of escape from antibody-mediated selection. Additionally, two
samples each possess multiple mutations within a single experimental B
cell epitope in GP, which are likely to evade antibody recognition if
those regions are relevant epitopes \emph{in vivo}.

Intriguingly, the two samples with multiple mutations within a single B
cell epitope each possess a distinct short stretch littered with T-to-C
transitions, a phenomenon also observed in Tong et al
\hyperref[csl:12]{tong_shi_liu_qian_liang_bo_liu_ren_fan_ni_et al._2015}. Excessive T-to-C and A-to-G mutation of virus
genomes has been observed previously as a result of adenosine deaminases
acting on RNA (ADARs, \hyperref[csl:24]{gelinas_clerzius_shaw_gatignol_2011}; \hyperref[csl:25]{zahn_schelp_utermohlen_von laer_2006}; \hyperref[csl:26]{carpenter_keegan_wilfert_o{\textquotesingle}connell_jiggins_2009}). When acting on viral genomic
RNA, ADARs cause a pattern of excess A-to-G transitions that are
represented by T-to-C transitions in our data set. These transitions are
known to occur either promiscuously within 200 nucleotide stretches, or
in a sequence-specific manner; therefore, we investigated both
possibilities. While only five of the 318 sequences in our dataset
contained obvious T-to-C stretches, we showed that the top 5\% of
sequences by sequence divergence, excluding the 5 sequences with T-to-C
stretches, were also moderately enriched for T-to-C transitions across
the genome. The remaining 95\% of sequences appeared to show no
enrichment. We do not know if this phenomenon is caused by ADAR acting
upon genomic RNA, as we cannot exclude the possibility of bias by the
EBOV RNA polymerase or other effects. Additionally, it is yet unclear if
these T-to-C mutations have an anti-viral or other effect on viral
fitness. These questions open avenues of research into molecular
mechanisms shaping EBOV evolution.

The results of some of the specific genome analysis methods that we
introduced here, while promising, will require denser EBOV genome
sampling to yield sufficient information to influence the EVD outbreak
response. Among these methods is transmission analysis, which could
prove valuable for improved understanding of hospital-based
transmissions and therefore for improved infection control. Inference of
the ancestral genetic state is often straightforward, with clear
patterns of new variations layering on previously existing variations;
viruses that appear to be descended from others in the same data set are
separated only by new mutations that are seen nowhere else in the data
set. This kind of genetic relationship does not guarantee a transmission
relationship between two patients, since many viruses can share
identical genomes. However, since viruses with identical genomes are
often epidemiologically related \hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}, we can infer that
viruses that appear to descend from other viruses in our data set are
either in or epidemiologically close to the same transmission chain.

Unfortunately, long delays of shipping samples from the field and
required changes to the EBOV inactivation protocol caused severe
degradation of many samples, which prevented identification of variants
and transmission analysis. This loss should serve as a reminder that
standardized and optimized protocols for sample collection, virus
deactivation, and shipment are crucial for a rapid worldwide response to
any new infectious disease outbreak. An important future research effort
will be aimed at understanding which certified EVD sample deactivation
protocols are best suited for high-quality genomic sequencing.
Complications with sample shipment also emphasize the need for
establishing in-country sequencing capabilities either before or at the
onset of future EVD outbreaks \hyperref[csl:27]{folarin_happi_happi_2014}. .

Beyond coordinated field and experimental responses, a culture of rapid
data sharing is critical for teams around the world to have the best
current information about a circulating virus or ongoing disease
\hyperref[csl:28]{yozwiak_schaffner_sabeti_2015}. In light of this need, we released all data
discussed in this paper publicly as they were generated, beginning in
December 2014, well in advance of our own analysis. We have previously
described our high-depth sequencing protocols \hyperref[csl:9]{matranga_andersen_winnicki_busby_gladden_tewhey_stremlau_berlin_gire_england_et al._2014} and we
are also now making available our computational analysis pipeline, in
the hope that they will assist the many laboratories engaged in viral
genomic research. As more EBOV genomic data become available, in
particular for poorly covered Liberia and Guinea, the scientific
community can together obtain a broader picture of transmission and
evolution of EBOV Makona during the EVD epidemic.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/within.newD.oldM.dated.mcc-dpark.tree-legend/Fig1}
\caption{{\textbf{Figure 1 - Within and Between Country Genomic Relationships of
Ebola Virus Makona.} \textbf{(A)} Phylogenetic and temporal placement of
recently sequenced Ebola virus (EBOV) within Sierra Leone. New EBOV
genomes (232 genomes, dark blue), sampled from Jun 16 through Dec 26,
2014, provide a high-resolution view of the accumulated genetic
diversity and fill in the missing ancestry between EBOV Makona genome
data sets. The maximum clade credibility (MCC) tree was inferred using
Bayesian evolutionary analysis by sampling trees (BEAST), with tips
anchored to sampling date. Tips are labeled for EBOV from five Western
health-care workers infected in Sierra Leone and treated in Europe
(sequenced by other groups, light green). Previously described nested
EBOV Makona lineages SL1, SL2, and SL3 \protect\hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}, as well as a
new lineage SL4, are labeled at their most-recent common ancestor (MRCA)
nodes. \textbf{(B)} Lack of EBOV Makona SL3 spread to Liberia or Mali.
Shown is a median-joining haplotype network constructed from a
coding-complete EBOV genome alignment including 340 EBOV Makona
sequences. Each colored vertex represents a sampled viral haplotype,
with colors indicating countries of origin. Colors are as in (A), with
the exception that the distinction is no longer made between older
(Gire) and newer (Park) Sierra Leonean datasets (both are now dark blue)
and two additional countries are shown (Liberia in yellow, Mali in red).
The size of the each vertex is relative to the number of sampled
isolates. Hatch marks indicate the number of mutations along each edge.
See also Figures S1 and S2.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig4/Fig2}
\caption{{\textbf{Figure 2 - Evidence for Host-to-host Transmission of Multiple
Ebola Virus Makona Genomes.} \textbf{(A)} Certain intrahost variants
(iSNVs) appear in samples throughout the 2013-2015 EVD epidemic,
suggesting that iSNVs can be transmitted between patients. Variants
shared between two or more samples are shown as rows of connected
points; each row is a genomic position (ordered by position along the
genome, top to bottom) and each point indicates the presence of the iSNV
in a patient. \textbf{(B)} Phylogenetic placement of derived alleles at
genomic position 18,911 implies both repeated transmission within clades
as well as some amount of recurrent mutation. Colored tips are sized
according to frequency of iSNV at position 18,911. Tips with small black
points are those with iSNV calls at any position; other tips represent
samples with no iSNV calls. This figure shows only the portion of the
tree relevant for this analysis; large branches with no SNPs or iSNVs at
position 18,911 are not shown. See also Figure S3.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/EBOV-rateEstimates/Fig3}
\caption{{\textbf{Figure 3 - Ebola Virus Evolution During a Prolonged EVD
Epidemic.} \textbf{(A)} Estimates of Ebola virus (EBOV) evolutionary
rates at three time scales: decades (yellow: all known EVD outbreaks),
months (blue: Baize + Gire + Park), and weeks (red: Baize + Gire).
\textbf{(B)} Purifying selection. We estimated nonsynonymous (red) and
synonymous (blue) substitution rates on external (unique to an isolate,
potential dead-end) and internal (shared by multiple isolates, evidence
of human-to-human transmission) branches. Nonsynonymous mutations
accumulate faster on external branches than on internal branches. This
is the case for synonymous mutations as well, although the difference
between external and internal branches is less pronounced. \textbf{(C)}
Enrichment for nonsynonymous mutations at shorter time scales. Intrahost
(all variants that appear within a single host at less than 100\%
frequency); unique interhost (minor allele appears only on an external
branch); shared interhost (minor allele on internal branches); shared
between EVD outbreaks (internal branch SNPs on a between-outbreak tree).
See also Figure S4.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/Fig1/Fig4}
\caption{{\textbf{Figure 4 - Evidence for Host Effects on Ebola Virus Makona
Evolution.} \textbf{(A)} Nonsynonymous variants are enriched in the
mucin-like domain of GP. Estimates of log(\selectlanguage{greek}ω\selectlanguage{english}) (a.k.a. log(dN/dS)) per
coding sequence within the Western African EVD outbreak (left) and
between EVD outbreaks (right) demonstrate gene-specific patterns of
natural selection. \textbf{(B)} Nonsynonymous variants are enriched in B
cell epitopes of GP. We calculated the fractions of nonsynonymous (NS)
and synonymous (S) consensus SNPs and intrahost variants (iSNVs) within
experimentally-determined B cell epitopes (data from ViPR,
\protect\hyperref[csl:20]{pickett_sadat_zhang_noronha_squires_hunt_liu_kumar_zaremba_gu_et al._2012}). Dotted line represents the fraction of GP amino
acids in ViPR epitopes. Nonsynonymous SNPs (p = 0.004) and iSNVs (p =
0.037) in GP occur more frequently in epitopes than expected by chance
(2-sided exact binomial test). Numbers indicate fraction of each variant
type within GP epitope regions. Error bars represent binomial sampling
intervals. \textbf{(C)} Local enrichment of T-to-C mutations within GP B
cell epitopes. We observed 5 sequences with short stretches (\textless{}
200 nucleotides) of concentrated T-to-C mutations. Of these 5 sequences,
2 (shown here; samples 20141582 and G5119.1) contain stretches of T-to-C
SNPs (blue points) within GP epitopes (light blue bars). Additionally,
we observe a T-to-C mutation at amino acid position 485 (blue diamond)
in three samples (one shown here; G4955.1) which is otherwise completely
conserved between all Ebolavirus species \protect\hyperref[csl:21]{olal_kuehne_bale_halfmann_hashiguchi_fusco_lee_king_kawaoka_dye_et al._2012}.
\textbf{(D)} Genome-wide increase in T-to-C mutations. We observe more
T-to-C transitions within the 2013-2015 outbreak than any other
transition, after correcting for nucleotide content. Error bars
represent binomial sampling intervals. \textbf{(E-F)} Elevated T-to-C
rates are genome-wide, but are limited to a subset of sequences.
Accumulation of mutation increases linearly with time. However, some
individual samples show more genetic distance than expected based on
sample date. Samples with short stretches of T-to-C mutations (orange)
show a significant enrichment of T-to-C mutations, as expected.
Excluding these samples, the top 5\% of samples by genetic distance
(yellow) lack localized stretches, but still show moderate enrichment of
T-to-C mutations genome-wide. The bottom 95\% of samples (beige) show no
enrichment of T-to-C mutations. Error bars represent binomial sampling
intervals.%
}}
\end{center}
\end{figure}

\section{Experimental Procedures}\label{experimental-procedures}

\subsection{Sample Preparation from Kenema Government
Hospital}\label{sample-preparation-from-kenema-government-hospital}

This study included 575 blood samples from 84 patients with confirmed
EVD from June 16 through September 28, 2014 by KGH laboratory staff.
Clinical samples were inactivated using Qiagen AVL and ethanol in the
KGH laboratory prior to shipping out of the country.

\subsection{Sample Preparation from CDC Bo
Laboratory}\label{sample-preparation-from-cdc-bo-laboratory}

This study included 98 blood samples from 98 patients with confirmed EVD
from August 20, 2014 through January 10, 2015 by CDC laboratory staff
stationed in Bo, Sierra Leone. Clinical specimens from the CDC Bo
laboratory in Sierra Leone were shipped to and stored at the Viral
Special Pathogens Branch BSL-4 laboratory at CDC in Atlanta, GA. Samples
were inactivated, and RNA was extracted using the MagMAX Pathogen
RNA/DNA isolation kit (Invitrogen) and BeadRetriever (Invitrogen).
Non-infectious RNA was treated with DNase I RNase-free (Roche) prior to
shipment to the Broad Institute.

\subsection{High-throughput
Sequencing}\label{high-throughput-sequencing}

Host ribosomal and carrier poly(rA) RNA depletion, randomly-primed cDNA
synthesis, Nextera XT library construction and 101-bp paired-end
Illumina sequencing were performed as described previously
\hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}; \hyperref[csl:9]{matranga_andersen_winnicki_busby_gladden_tewhey_stremlau_berlin_gire_england_et al._2014}.

\subsection{Ebola Virus Makona Genome Assembly and
Analysis}\label{ebola-virus-makona-genome-assembly-and-analysis}

EBOV Makona genomes were assembled from high-throughput sequencing data
using an updated bioinformatics pipeline based on our previously
described methods \hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}; \hyperref[csl:9]{matranga_andersen_winnicki_busby_gladden_tewhey_stremlau_berlin_gire_england_et al._2014}. Of the collected samples, 150 KGH
and 82 CDC samples were used to accumulate sufficient EBOV genome
sequencing coverage for high quality \emph{de novo} genome assembly.
Further description of the pipeline can be found in the Extended
Experimental Procedures.

Our Linux-based software pipeline is publicly available at
https://github.com/broadinstitute/viral-ngs \hyperref[csl:10]{park_jungreis_tomkins-tinch_lin_2015}. This
pipeline includes command-line tools for each of the above steps and
optional Snakemake workflows \hyperref[csl:11]{koster_rahmann_2012} to automate them either
sequentially or in parallel.

The assembly pipeline is also available via the DNAnexus cloud platform.
RNA paired-end reads from either HiSeq or MiSeq instruments (Illumina)
can be securely uploaded in FASTQ or BAM format and processed through
the pipeline using graphical and command-line interfaces. Instructions
for the cloud analysis pipeline are available at
https://github.com/dnanexus/viral-ngs/wiki

\subsection{Genomic Epidemiology of Ebola virus
Makona}\label{genomic-epidemiology-of-ebola-virus-makona}

The following publicly available EBOV Makona genomes from outside of
Sierra Leone do not carry the SL3-derived allele at position 10,218: 26
available genomes from Liberia (25 from \hyperref[csl:29]{kugelman_wiley_mate_ladner_beitzel_fakoli_taweh_prieto_diclaro_minogue_et al._2015}, 1 from
GenBank KP178538.1) and all 4 available genomes from Mali
\hyperref[csl:30]{hoenen_safronetz_groseth_wollenberg_koita_diarra_fall_haidara_diallo_sanogo_et al._2015}. A median-joining haplotype network was constructed
in PopART version 1.7.2 (http://popart.otago.ac.nz). Due to the presence
of missing data, 1,492 sites (7.9\% of total genome) were excluded from
the analysis; these sites included 61 sites with variability among
isolates (10.9\% of all variable sites).

To reconstruct the EBOV Makona transmission history within Sierra Leone,
we grouped samples into sets of one or more genetically identical
viruses based on their consensus sequences. We then identified
relationships between these groups, progressing from the Guinean
reference genome (KJ660346.2) and ending with nine viruses sampled in
Freetown (eight from our KGH and CDC cohorts and one sequenced in
Italy).

\subsection{Intrahost Variant
Analysis}\label{intrahost-variant-analysis}

Full details of the identification and calling of intrahost variants
(iSNVs) are available in Extended Experimental Procedures; iSNV calls
and analyses are available in Data S1. Evolutionary distances between
pairs of phylogeny tips were computed from the posterior sample of trees
produced by Bayesian evolutionary analysis by sampling trees (BEAST)
\hyperref[csl:31]{drummond_suchard_xie_rambaut_2012} analysis. This calculation integrates across
phylogenetic uncertainty and produces a temporal evolutionary distance
between phylogeny tips. We used this distance matrix to calculate the
average distance between pairs of phylogeny tips that share an iSNV and
compared the result to the average distance between random pairs of
tips. We calculated a \emph{p} value for the observed average distance
by conducting a randomization test. In each random replicate, we sampled
the same distribution of iSNV possessing tips as observed in the
empirical data, and calculated the average distance between these pairs
of tips. We calculated a \emph{p} value by comparing the empirical mean
distance to the mean distances observed over 10,000 random replicates.

\subsection{GP B Cell Epitope Analysis}
Data was obtained from the NIAID Virus Pathogen Database and Analysis Resource (ViPR) online through the web site at http://www.viprbrc.org \hyperref[csl:20]{pickett_sadat_zhang_noronha_squires_hunt_liu_kumar_zaremba_gu_et al._2012}. As most of the epitopes in the database are based on the Mayinga reference strain, we mapped all B cell epitopes against the Guinean reference strain (GenBank KJ660346.2), and removed all epitopes which no longer matched perfectly, leaving 40 B cell epitopes. Overlapping epitopes were merged, and the nonsynonymous and synonymous consensus SNPs and iSNVs were scored as within or outside of epitope regions with bedtools. Significance was determined by 2-tailed binomial test with \selectlanguage{greek}α \selectlanguage{english}= 0.05, with the null hypothesis that variants would occur in epitope regions of GP by chance with a probability of 172/676, or, the fraction of residues of GP in a B cell epitope.

\subsection*{Molecular Evolution}

Three data sets were constructed to represent 3 time scales of genetic surveillance of EBOV Makona. For surveillance between EVD outbreaks, 63 publicly available sequences representing the diversity of EBOV sampled over long periods of time, including the first recorded, 1976 EVD outbreak and other EVD outbreaks and excluding one outbreak occurring in the Democratic Republic of the Congo in 2014. We also included EBOV genome fragment sequences from possibly infected great ape carcasses and frugivorous bats. Fourteen sequences from Western Africa were chosen to represent the current 2013-2015 EVD outbreak. For surveillance of the early outbreak, 81 sequences  \hyperref[csl:3]{baize_pannetier_oestereich_rieger_koivogui_magassouba_soropogui_sow_keïta_clerck_et al._2014}; \hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014} were reanalyzed, representing the earliest epidemiologically-relevant and publicly available sequences. For surveillance of the prolonged epidemic, 232 EBOV genomes reported here were combined with 5 sequences from repatriated health-care workers (UK1, UK2, UK3, INMI1, GE1) and the 81 sequences from the early outbreak data set.

Analyses of rates, phylogenies, and evolution were performed on all three data sets in BEAST \hyperref[csl:31]{drummond_suchard_xie_rambaut_2012}. Full details on the models and parameters are available in Extended Experimental Procedures. All BEAST inputs, outputs, and analysis scripts are available in Data S2.

\section{Author Contributions}\label{author-contributions}

The contributions of each author are too extensive to list in detail.
But among the first five and last four authors, A.G. and R.F.G.
collected samples. A.G. and S.L.M.W. processed samples for sequencing.
D.J.P, G.D., S.W., S.L.M.W, and A.R. analyzed sequence data. D.J.P,
G.D., S.W., S.L.M.W., U.S., A.R., and P.C.S. wrote the paper. U.S.,
A.R., R.F.G., and P.C.S. jointly supervised this work.

\section{Acknowledgements}\label{acknowledgements}

We thank KGH staff who died of EVD (including M. Fonnie, A. Moigboi, A.
Kovoma, M. Fullah, and S. H. Khan), the Office of the President of
Sierra Leone (President E. Koroma, M. Jones), the Sierra Leone Ministry
of Health and Sanitation, the Kenema District Health Management team,
and the Kenema Lassa fever program for their immense efforts in the EVD
outbreak response. We thank Public Health England (UK1, UK2, UK3), IRCCS
Lazzaro Spallanzani (INMI1), and the University of Geneva (GE1) for
providing EBOV genome sequences from samples of EVD patients exported
from Sierra Leone. We thank the drivers, pilots, phlebotomists,
non-governmental organizations, district medical officers, and district
surveillance officers for their help with sample collection and
logistics in Sierra Leone. We want to especially thank the M\selectlanguage{ngerman}édecins Sans
Frontières (MSF) operation centers for their continuing support of the
US Centers for Disease Control and Prevention (CDC) laboratory in Bo,
Sierra Leone, and the World Health Organization (WHO) for their support
of the preceding CDC laboratory operation in Kenema, Sierra Leone.

This work was supported by European Union grant FP7/2007-2013
278433-PREDEMICS and European Research Council grant 260864 (A.R.);
Natural Environment Research Council grant D76739X (G.D.); NIH U54
GM111274 (T.B.); NIH grant GM080177 (S.W.); NIH grant 1U01HG007480-01
(C.H.); National Science Foundation Graduate Research Fellowship Grant
No. DGE 1144152 (A.E.L.); the National Health and Medical Research
Council, Australia (E.C.H.); the Defense Threat Reduction Agency
(USAMRIID); NIH/NIAID U19AI110818 (Broad Institute); the Bill and
Melinda Gates Foundation OPP1123407 (Broad Institute); and NIAID
HHSN272200900049C (Harvard/Tulane). This work was funded in part through
Battelle Memorial Institute's prime contract with the US National
Institute of Allergy and Infectious Diseases (NIAID) under Contract No.
HHSN272200700016I. Subcontractors to Battelle Memorial Institute who
performed this work are: J.H.K., an employee of Tunnell Government
Services, Inc. R.F.G. is co-founder of Zalgen Labs.

The Virus Pathogen Database and Analysis Resource (ViPR) has been wholly
funded with federal funds from the National Institute of Allergy and
Infectious Diseases, National Institutes of Health, Department of Health
and Human Services, under Contract No. HHSN272201400028C.

This paper was authored in Authorea and its edit history is available
here: https://www.authorea.com/users/10734/articles/19957.

\emph{The content of this publication does not necessarily reflect the
views or policies of the the US Department of Health and Human Services
(Centers for Disease Control and Prevention, National Institutes of
Health) or the US Army.}

\section{Accession Numbers}\label{accession-numbers}

Genome assemblies, annotations, and raw reads are available at NCBI on
GenBank and SRA using the following BioProject IDs: PRJNA257197 (samples
from Kenema Government Hospital) and PRJNA283385 (samples from CDC Bo
Lab). Note that PRJNA257197 also includes all previously published data
from \hyperref[csl:4]{gire_goba_andersen_sealfon_park_kanneh_jalloh_momoh_fullah_dudas_et al._2014}.

\selectlanguage{english}
\FloatBarrier
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\end{CJK}\end{document}

