Next Article in Journal
Wildlife Viruses: Impact on Human and Animal Health
Previous Article in Journal
Cytomegalovirus-Specific Hyperimmune Immunoglobulin Administration for Secondary Prevention after First-Trimester Maternal Primary Infection: A 13-Year Single-Center Cohort Study
Previous Article in Special Issue
Genomic and In Vitro Phenotypic Comparisons of Epidemic and Non-Epidemic Getah Virus Strains
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Viruses
Volume 16
Issue 8
10.3390/v16081242
viruses-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Genomics for Arbovirus Surveillance: Considerations for Routine Use in Public Health Laboratories

by
Leandro Patiño
1,2,*,†,
Andrea Denisse Benítez
3,*,†,
Andrés Carrazco-Montalvo
4 and
Mary Regato-Arrata
5
1
Instituto Nacional de Investigación en Salud Pública “Leopoldo Izquieta Pérez”, Dirección Técnica de Investigación, Desarrollo e Innovación, Guayaquil 090150, Ecuador
2
Facultad Ciencias de la Vida, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, Guayaquil 0909020, Ecuador
3
Instituto de Microbiología, Universidad San Francisco de Quito USFQ, Quito 170901, Ecuador
4
Instituto Nacional de Investigación en Salud Pública “Leopoldo Izquieta Pérez”, Centro de Referencia Nacional de Genómica, Secuenciación y Bioinformática, Quito 170403, Ecuador
5
Instituto Nacional de Investigación en Salud Pública “Leopoldo Izquieta Pérez”, Centro de Referencia Nacional de Virus Exantemáticos, Gastroentericos y Transmitidos por Vectores, Guayaquil 090150, Ecuador
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Viruses 2024, 16(8), 1242; https://doi.org/10.3390/v16081242
Submission received: 22 April 2024 / Revised: 13 July 2024 / Accepted: 16 July 2024 / Published: 2 August 2024
(This article belongs to the Collection Emerging Arboviruses, Volume II)
Browse Figure
Versions Notes

Abstract

:
The emergence and re-emergence of arthropod-borne viruses is a public health threat. For routine surveillance in public health laboratories, cost-effective and reproducible methods are essential. In this review, we address the technical considerations of high-throughput sequencing methods (HTS) for arbovirus surveillance in national health laboratories, focusing on pre-sequencing, sequencing, and post-sequencing approaches, underlining the importance of robust wet and dry laboratory workflows for reproducible analysis. We aim to provide insights for researchers and clinicians interested in arbovirus, diagnosis, and surveillance by discussing current advances in sequencing methods and bioinformatics pipelines applied to arboviruses.

1. Introduction

Sensitivity, accuracy, reproducibility, time efficiency, cost-effectiveness, and validation are crucial criteria for choosing techniques for the diagnosis and surveillance of pathogens in public health laboratories [1,2,3]. Arthropod-borne viruses, also known as “arboviruses”, encompass a varied group transmitted by blood-feeding arthropods such as ticks, mosquitoes, sandflies, and biting midges. Dengue virus (DENV), Zika virus (ZIKV) (family Flaviviridae, genus Flavivirus), and chikungunya virus (CHIKV) (family Togaviridae, genus Alphavirus) are among the main arboviruses subject to routine surveillance in tropical and subtropical countries, where arthropod vectors thrive [4,5]. These viruses are involved in recurring large and expanding epidemic outbreaks and are a threat to temperate countries given the establishment of competent Aedes spp. populations associated with urbanization, human mobility, and climate change. DENV is estimated to cause 100–400 million cases and between 40,000 and 70,000 fatalities each year, with half of the world’s population at risk of infection [6]. Arbovirus infections in humans may present with a range of symptoms, such as fever, headache, muscle and joint pain, rash, myalgia, and fatigue. In some cases, the disease manifestations can lead to complications like shock syndrome or severe neurological damage, such as encephalitis, meningitis, paralysis, or even death [7,8].
Arboviruses surveillance is key for the early detection of seasonal outbreaks, new viral introductions, or even local evolution aimed at a rapid response, and the implementation of control measures [6]. National health laboratories (NHLs) play a crucial role in confirming suspected cases, genotyping the viruses, and monitoring changes in viral strains. Routine methods used at these laboratories include Enzyme-Linked Immunosorbent Assay (ELISA), virus isolation, plaque reduction neutralization test (PRNT), and nucleic acid tests (NATs)—particularly RT-qPCR, as most arboviruses are RNA viruses. However, understanding the true burden and evolution of arboviruses is hampered by reduced surveillance capacity using RNA-based tests, the high proportion of asymptomatic infections (e.g., up to 74% for DENV, ZIKV, and YFV), short viral incubation periods in humans (e.g., 2 to 14 days), low viremia in arthropod vectors, low RT-qPCR specificity, and the requirement of BSL-3 for improved viral detection [8,9,10,11,12].
High-throughput sequencing (HTS), also referred to as deep sequencing and next-generation sequencing, has surged as a disruptive technology for epidemic preparedness and outbreak response. Following SARS-CoV-2 surveillance, many public health laboratories have their installed capacity for applying it to arbovirus outbreak monitoring [13,14]. HTS allows for the genome sequencing of individuals from the same or different species simultaneously, for various sample matrices including cultured organisms, or directly from clinical or arthropod vectors. For arbovirus surveillance, HTS is a powerful genotyping method to determine whether the target pathogen is present in the sample. However, to take full advantage of HTS outputs, technical approaches must be carefully addressed before, during, and after sequencing. These include augmenting viral RNA, simultaneously sequencing different arboviruses or other virus families responsible for febrile illness, increasing the depth of coverage, and looking for mutation patterns with evolutionary significance [15,16,17]
This review discusses published information on HTS methods employed for detecting arboviral infection in humans and vector samples, focusing on pre-sequencing, sequencing, and post-sequencing approaches for NHLs.

2. Arbovirus Pre-Sequencing Considerations

2.1. Sample Collection and Storage

Sample collection and the storage of specimens for arbovirus surveillance and research are crucial for monitoring and understanding the epidemiology of these viruses. It might involve collecting entire arthropods such as mosquitos and ticks, vertebrates’ blood, serum, and other tissues. Sample storage will depend on the study objectives and available resources. Common methods for storing arthropods include the use of dry ice, liquid nitrogen, ethanol, and RNA-stabilizing buffer aimed to preserve viral RNA [18] and inactivate dangerous pathogens at the same time. Blood samples are collected preferentially in EDTA or without additives for serum separation by centrifugation. The sample type most used in NHLs is serum samples, as it can be used either for serological or PCR-based analysis [19]. In contrast to serum samples, whole blood has been shown to remain positive for weeks to months and could be a valuable sample type for genomic analysis [20]. Keeping a cold chain for transporting samples from remote locations is a challenge for NHLs [21]. RNA-stabilizing buffers and FTA® (Flinders Technology Associates) classic cards are a useful alternative for tackling this issue. FTA cards are cotton-based cellulose paper containing a particular blend of denaturants and protecting agents. This chemical mixture degrades cells and proteins and inhibits bacterial development while protecting nucleic acids from free radical damage. The matrix traps RNA, maintaining its stability, while undesirable elements are washed away. FTA® cards have been widely used for numerous samples such as blood, tissues, and serum [22].

2.2. Virus Enrichment

Some arboviruses such as ZIKV are known to have a low viral load or, in some cases, the infecting arbovirus could be detected with a low Ct [23] (cycle threshold at which a target nucleic acid is identified by q-PCR). In these circumstances, virus enrichment could be required before sequencing. One of the most common methods for arbovirus enrichment involves growing and propagating these viruses in cell cultures. It is commonly approached using Vero (African green monkey kidney cells) or C6/36 (Aedes albopictus mosquito cells), and various other mammalian cell lines depending on the virus in question [24]. Arbovirus growth in cell culture could take up to two weeks and requires specific aseptic areas and dedicated personnel. In addition, growing some arboviruses (CHIKV, Japanese encephalitis virus—JEV, West Nile virus—WNV) requires BSL-3 facilities, which are not available for all NHLs, especially in low- and middle-income countries, cell culture could also have low specificity for certain arbovirus species [21,25,26].
Ultracentrifugation-based methods could also be used for viral enrichment. Several ultracentrifugation methods described elsewhere include conventional ultracentrifugation, aimed at pellet viruses, separating them from other components in the sample, and the addition of polyethylene glycol (PEG) in a previous centrifugation increases virus precipitation [27,28]. Differential centrifugation involves spinning the sample at different speeds to separate particles based on their size and density. Gradient centrifugation with cesium chloride or tangential flow filtration has been used to separate viruses from other components based on size [3,29,30,31].
Additional methods described for viral enrichment include membrane filtration, using specific pore sizes to physically separate viruses from larger particles in the sample and concentrating the pathogens in the filtered solution. Also, immunomagnetic separation consists of magnetic beads coated with antibodies specific to the virus of interest, where a magnetic device is used to isolate the virus bound to beads from the rest of the sample [32].

2.3. Nucleic Acid Isolation

RNA can be isolated from a viral culture or directly from clinical samples of interest. Commercial kits are available for isolating RNA, and sequencing requires those that isolate highly pure RNA. Common nucleic acid isolation methods for genomic analysis are based on silica columns or magnetic beads using automatized instruments. To ensure the integrity of the RNA, the inactivation of RNases is a primary concern, this can be achieved by treating water with diethyl pyrocarbonate (DEPC) or by employing commercial RNase inhibitors [33]. It is highly recommended to work within a dedicated RNAse-free environment, using RNAse-free filter tips and disposable plasticware to prevent any potential RNA contamination. Maintaining a cold rack for reagent mixtures is recommended to preserve their quality. RNA isolation is usually complemented with the use of DNase enzymes to degrade co-isolated host DNA (from cell culture, host, or vector samples) which could potentially compete for the assignment of reads during sequencing.
Besides viral growth or ultracentrifugation, nucleic acid isolated from clinical or host samples can be enriched by several methods as described below.

2.4. Nucleic Acid Enrichment

2.4.1. Nucleic Acid Capture by Hybridization

Also named targeted sequence capture (TSC), it involves the use of probes or baits to isolate target viral sequences from background nucleic acids. TSC increases viral reads and [34,35] offers advantages including the following: specificity, as the probes are designed to be highly specific to the target regions; flexibility, as the probes can be customized for different viruses; high coverage, due to increased sensitivity for detecting the target genome; and cost-effectiveness. An example is VirCapSeq-VERT, a commercial method that targets vertebrate viral genomes of 34 virus families (both DNA and RNA viruses), utilizing approximately 2 million capture probes [34,35]. The TSC method has a few limitations. An important limitation is that TSC panels may not effectively enrich highly divergent or novel viruses that are not included in the panel or do not share similarities with the covered viruses, which could be the case of the less-explored animal virome. Additionally, differences in viral loads may lead to biases in the representation of viral reads, for instance, viruses with high loads can be overrepresented in pooled samples which results in skewed data [35].

2.4.2. Nucleic Acid Amplification

Nucleic acid amplification may be required to generate sufficient material for sequencing. PCR can provide both target enrichment and amplification in a single step and is relatively cheap, available, and fast compared with other methods. Most of these methods aim to amplify the genome of the organism of interest.
One of the most common methods for nucleic acid enrichment is using a tiling amplicon scheme that involves the use of primer pairs that amplify small, contiguous, or overlapping amplicons of the viral genome of interest. Primers can be designed for specific viruses or virus families and optimized for different sequencing platforms. Tiling amplicons provide high-resolution information by systematically covering every base pair in the target region. This makes it well suited for studying fine-scale genomic features [17].
Spiked primer enrichment is another method that, in combination with random primers, is used for the genome amplification of different virus families at the same time. The term “spiked” refers to the addition of these primers to the sequencing library. This method uses primers designed to target specific genes of the virus of interest. These primers “spike” the target genome, and when it is subjected to PCR, they selectively amplify specific regions of the virus of interest [36].
Multiplex PCR that targets multiple viruses is another strategy. Using a combination of virus or strain-specific primers, multiple viral genomes can be generated in a single PCR reaction [17].
Rolling circle amplification (RCA) is an isothermal multiple displacement amplification method used to obtain several copies of DNA or RNA from a circular template. It is particularly useful for enriching viral genomes in a sample, as many viruses possess circular or single-stranded genomes. RCA involves the use of random hexamers or specific primers, and specific polymerases (such as phi29) that possess strand displacement and proof-reading activity and can generate long synthesis products, which allows for the continuous copying of the template without the need for thermal cycling. RCA offers several advantages, including its sensitivity to detect low-concentration viruses, rapid completion within a few hours, and cost-effectiveness. However, there are some drawbacks to consider, such as the need for careful optimization of the reaction, it lacks high sensitivity and the potential for non-specific amplification. Despite these limitations, RCA remains a valuable method for virus enrichment, providing a sensitive and efficient means to detect viruses in samples with low viral concentrations [2].
Sequence-Independent Single-Primer Amplification (SISPA) is a nucleic acid amplification technique that differs from the others as it does not require previous knowledge of the pathogen sequence. It employs random primers on the 3’ end and a defined tag on the 5’ end, which will be used for subsequent amplification. The advantages of this method include the detection of known and unknown viruses in a wide range of samples and the ability to detect mixed viral infections and new viral species. Given that viral communities lack conserved molecular markers, unlike bacterial (16S) and fungal communities (internal transcriber spacer—ITS), random priming is a good strategy in viral metagenomics. A problem that may be encountered with this method is random priming to bacterial and host reads, which can affect the diversity of sequences found in the library [2,30,34,37].

3. Arbovirus Sequencing Considerations

3.1. Sequencing Methods for Arbovirus Surveillance

The choice of a sequencing method for arbovirus surveillance will depend on laboratory equipment, resources, sequencing instruments, and bioinformatics background. The first method for sequencing the arbovirus whole genome was Sanger sequencing [38], a first-generation sequencing method that involves the use of radioactive or fluorescently labeled dideoxy nucleotides, which lead to chain termination. Sanger sequencing only sequences a single and short DNA fragment at a time; however, it has 99.99% accuracy [39]. A bioinformatics background is not mandatory for processing data resulting from this process, as the methods available for assembling genomes are customized.
HTS for arbovirus sequencing has been reported since 2010, e.g., Bishop-Lilly (2010) and Cruz (2016) [40,41]. A comprehensive revision of the HTS platforms and sequencing technologies has been presented by Slatko et al. and Hu et al. [39,42]. Current HTS includes second- and third-generation Sequencing (SGS or TGS) platforms. SGS platforms currently used for the genome sequencing of arbovirus include the following: SOLiD, based on sequencing by ligation; Illumina, which uses sequencing by synthesis; and Ion Torrent, a semiconductor-based platform. The BGISEQ platforms based on DNA nanoball (DNB) and probe anchor synthesis technology have not been reported yet for arbovirus sequencing. Of the SGS methods tested for arbovirus HTS, Illumina offers a higher level of accuracy, enabling the precise sequencing of genomes from new pathogens. Illumina and Ion Torrent are considered more practical methods due to their reduced cost per base and larger outputs when compared to 454 and SOLID systems, both of which are no longer widely used in the field [29,43,44,45,46]. TGS platforms used for arbovirus sequencing include PacBio and Oxford Nanopore, both based on semiconductor technology. An advantage of TGS is the real-time detection of nucleotide incorporation events during the replication of single DNA molecules by DNA polymerase [44,47]. Oxford Nanopore Technology (ONT) has portable instruments the size of a stapler or a mobile phone, which have been used in the field using a mobile laboratory for Zika surveillance [47,48,49,50].
Illumina and ONT are the most common platforms used up to date for arbovirus sequencing. They use sequence-dependent (targeted) and sequence-independent (untargeted) approaches, proving that high-quality full viral genome sequences may be produced in 1–2 days during viral outbreak events [17,51,52,53,54,55].
Arbovirus genomes can be sequenced following sequence dependent and sequences ondependent approaches. A schematic diagram of different approaches for viral sequencing that could be useful for arboviruses is shown in Figure 1. Table 1 highlights examples of previous research where different sequencing methods were employed, and where the bioinformatics procedures were applied.

3.1.1. Sequence-Dependent Approach

These approaches allow for the targeted amplification of virus species, genera, or families. They include methods based on PCR and methods based on TSC. In relation to PCR-based methods, amplicon tiling is the most common approach. It uses reverse transcription followed by conventional PCR to generate amplicons that overlap by approximately 100 bp. Quick et al. (2017) employed an amplicon tiling strategy to produce comprehensive sequence coverage of ZIKV genomes. The authors developed a web-based primer design tool called primalscheme (latest version v1.4.1), which provides a complete pipeline for the development of efficient multiplex primer schemes for any virus. The software helps to design the primers, the length of the amplicon, and the overlapping section of each amplicon. For the 11 kb viral genome of Zika, scientists designed 35 primer pairs, each pair amplifying products of about 400 nT in length with a 100 nT overlap [17]. Vogels et al. recently proposed a pan-serotype HTS method for DENV [59].
Illumina has developed the commercial kit “Viral Surveillance Panel”, aimed to detect 66 viruses classified as “Important risks to public health” by the World Health Organization including arboviruses such as chikungunya, Zika, and the four dengue serotypes.

3.1.2. Sequence-Independent Approach

Also named agnostic methods [2], these approaches allow for metagenomic sequencing (the direct sequencing of all genetic material present in a sample), allowing for unbiased viral detection. Sequence-independent approaches can be conducted through the spiked primer enrichment method (MSSPE), which involves the use of primers that anneal randomly to the viral RNA during the step of reverse transcription (cDNA synthesis) of the library preparation. MSSPE can be used with different sequencing platforms and has been shown to be efficient for arbovirus sequencing [36]. Several metagenomic methods for arboviruses have been described. For example, Jerome et al. (2019) demonstrated the potential of this method for the diagnosis of viral infections in febrile returning travelers using plasma samples. A total of six human viruses were detected including DENV and CHIKV. The study showed that metagenomics is an effective diagnostic tool, with a higher sensitivity and a faster turnaround time compared to conventional tests [43]. Another example was presented by Batovska et al. (2017), who employed a non-targeted detection approach with unamplified RNA/cDNA hybrids for library preparation and successfully detected and characterized the Ross River virus (RRV) from an infected mosquito [16].
Batovska et al. (2022) also focused on metatranscriptomic sequencing for arbovirus surveillance from field-collected mosquitoes, employing Illumina technology. The authors demonstrated the untargeted nature of metatranscriptomic sequencing, as it enables the detection of both known and novel viruses in mosquitoes, as well as other organisms of interest such as parasites, bacteria, and fungi [60].
Metatranscriptomics—the sequencing of all RNA isolated from a sample—was also used in research developed by [4]. The authors successfully reconstructed the complete genome of the ZIKV, including the 5 and 3 UTR regions, which would not have been possible using standard approaches.

4. Arbovirus, Post-Sequencing Consideration

Bioinformatic Analysis and Workflows for Arbovirus Sequencing Outputs

High-throughput data handling includes quality control, trimming, assembly, annotating, and displaying biological data of large volumes of sequencing data. It requires significant computational analysis and bioinformatics knowledge [15]. Several bioinformatics tools, algorithms, and pipelines have been developed depending on the sequencing platform and the type of analysis required, whether the data belong to a single organism or a mixed sample. A general workflow includes the steps described as follows:
(I) Quality control, the initial step, which includes evaluating statistical metrics and scores of sequences to assure accurate downstream analysis. (II) Precision trimming, cleaning, and improving the sequencing data by eliminating adapter sequences, discarding low-quality reads, filtering out contaminants, and removing low-quality segments and unnecessary elements. This optimizes the data for further analysis and ensures reliable and accurate results [57]. (III) Assembly, the process of reconstructing the complete genome sequence from fragments of shorter sequences, and there are two ways: A. Mapping assembly: sequences alignment with a previously known reference genome. This allows for the identification of variations between the sequenced individual and the reference, including mutations, insertions, or deletions. The resulting alignment data are stored in Binary Alignment Map (BAM) files, which are the compact and indexable binary representation of Sequence Alignment Map (SAM) files. This approach may not be suitable for highly variable genomic regions or genomes that differ significantly from the reference genome [59]. B. De novo assembly: involves genome assembly without the need for prior reference. Overlapping sequences assemble into longer fragments (contigs). This method is particularly useful when a reference genome is not available or when working with non-model species or very large genomes [42]. (IV) Taxonomic profiling: assigning taxonomic identities to sequences, allowing the species present in a the sample to be identified and classified. Additional analysis may include functional annotation to explore information about gene function and biological processes, aimed at a better understanding of the molecular mechanisms behind biological events, directing future experimental studies, and statistical analysis [23,41,42,57,58].
There are several ways to analyze sequencing data in genomic and metagenomic research, providing researchers with varying degrees of control, flexibility, and personalization. The use of locally installed software packages is a typical solution, as researchers can have direct control over the analysis process and adjust it to their individual needs by installing and configuring bioinformatics tools on their own workstations. This requires technical expertise, computational resources, and time for installation, maintenance, and updates (Table A1). Custom scripting is another method in which researchers create their own scripts or workflows using programming languages (such as Python, R, or Perl). This method offers the most flexibility and customization, allowing users to employ specialized analytical processes, though it requires advanced programming skills and a significant amount of time and effort to develop and maintain the scripts [15].
Software bundles that combine tools and processes into one package, like the Bioconductor suite for R, and EPI2ME, offer solutions for enhanced analysis. Researchers use bioinformatics tools and pipelines to efficiently organize the processing, analysis, and interpretation of large amounts of sequencing data. Another alternative is web-based tools that provide end-to-end bioinformatics pipelines. These platforms combine a variety of computational tools, methods, and databases, which allows researchers to perform a variety of analyses, including data quality control, read alignment, variant calling, functional annotation, and visualization (Table A2). However, a disadvantage is that some pre-designed tools often lack updates, which could affect genetic variant assignment, due to the use of outdated databases; ultimately, and they may transition to paid software models, limiting accessibility. Specific pipelines have also been developed for DENGV, and for an example of this, see Mendes et al., 2020 [61].

5. Discussion

Incorporating genomics into public health initiatives for arboviral research and surveillance has enormous potential to reduce their impact. The World Health Organization has acknowledged the value of genomics methods in understanding transmission dynamics, developing global genomic surveillance strategies, and responding to emerging and re-emerging outbreaks. The COVID-19 pandemic was an important turning point in genomic monitoring, exhibiting the exceptional speed of pathogen characterization and data sharing. National health laboratories implemented or strengthened their genomic capacity which could be harnessed to increase the genomic surveillance of pathogens of concern [9,57].
HTS has several advantages for diagnosing clinical cases as reviewed by Lui et al., 2023 [62]. Genome sequencing is a valuable complement to antigen or NAT. Genome analysis allows for viral characterization to the level of genotypes which is crucial to investigate the implication of viral variants causing outbreaks or severe illness. For example, DENV is known to have four serotypes, and each of them is genetically subdivided into genotypes; while RT-qPCR detects serotypes, sequencing leads to genotypes. Serotyping at least 10% of positive DENV cases nationwide is a common algorithm of NHLs, and in comparison with q-PCR data, HTS allows for the detection of genetic variation through the genome. In circumstances where the nucleic acid test fails to detect the pathogens targeted—usually three multiplexed viruses in RT-qPCR—agnostic metagenomic methods would allow for the identification of the suspected pathogens or any other viruses causing the disease. High throughput and the depth of coverage obtained by HTS allow for the detection of minor variants down to 1–5%. These features provide new avenues to study viral populations at the individual (patient) level which would help to better understand the rise of new viral variants or the biological meaning of viral intra-host diversity [63].
For epidemiological studies, HTS has shown to be crucial in identifying early arboviral outbreaks, detecting specific viral strains, tracking their spread to new geographic areas, and identifying the local emergence of novel strains. For example, WGS has been used for studying the ZIKV in Brazil, where it has been detected since 2015 [64]. Genomic analyses identified the Asian lineage as the driver of the epidemic outbreaks in the Americas and allowed for the detection of the African lineages in enzootic cycles between non-human primates and mosquitoes [65]. It also allowed for the identification of the recent introduction to the Americas of the DENV-2 cosmopolitan genotype lineage [66] and a better understanding of the dynamics of circulating lineages/genotypes [67].
Arbovirus genome sequencing could also contribute to strengthening One Health initiatives and early warning systems for arbovirus infection. In relation to the One Health approach, arboviruses often have complex transmission cycles involving wildlife and zoonotic reservoirs [68]. Genome sequencing facilitates studying their diversity in different hosts including vectors. Ongoing initiatives aim to establish early warning systems for arbovirus infection in some countries of the Mediterranean (Serbia) and Black Sea region (Georgia) and Brazil. These efforts seek to integrate the surveillance of human, animal, entomological, and environmental sectors. Incorporating genomics into intersectoral surveillance would leverage these programs providing valuable genetic information [69,70], useful for understanding and predicting potential changes in arbovirus behavior and implementing timely interventions [69,70,71].
Similar to SARS-CoV-2, sharing arbovirus genome sequencing data on a global scale would create opportunities for collaboration among researchers and public health agencies. This collective effort could enhance our ability to respond effectively to arbovirus threats and identify specific viral proteins that could serve as targets for vaccine development or potential targets for antiviral drug development. An example of this is the EpiArbo initiative, launched by GISAID. It provides an online platform for researchers worldwide to share genomic data on arboviruses, helping to improve surveillance, diagnostics, treatments, and vaccine development. With open access to data and a global network of collaborators, EpiArbo strengthens the global scientific community to stay ahead of arbovirus outbreaks and limit their impact.
No single sequencing strategy is ideal for all situations, and a combination of strategies may be required to obtain the best outcomes. It is important to perform a careful standardization of the protocol to take full advantage of genome sequencing [36,72]. Further optimization and standardization of sample preparation methods are needed to ensure reproducibility and comparability across laboratories [73]. This will enable the development of robust protocols applicable in diverse settings and facilitate molecular epidemiology analysis.
The development of user-friendly bioinformatics tools and pipelines specifically designed for arboviral data analysis is necessary. These tools should address the challenges associated with data processing, quality control, variant calling, and data integration, hence facilitating accurate and reproducible analysis across different research groups. Including bioinformatics analysis that looks for significance in mutations in consensus sequences and at the viral population level will have a major contribution to understanding arbovirus evolution [74,75]. In the Americas, important efforts are being made to standardize wet and dry laboratory protocols through PAHO initiatives such as the The Arbovirus Diagnosis Laboratory Network of the Americas and the Arbovirus geomic Surveillance Network, RELDA and Vigenda by its Spanish acronym (The Arbovirus Diagnosis Laboratory Network of the Americas (RELDA): https://www.paho.org/en/topics/dengue/arbovirus-diagnosis-laboratory-network-americas-relda, accessed on 28/7/2024).
From an NHL perspective, HTS does not replace conventional virological methods for arbovirus surveillance. However, it does represent a powerful modern method to complement and enhance these activities. Technical limitations of HTS include workflows of >1 day and the lack of interlaboratory standardization both for wet and dry laboratories. Sequence quality is dependent on viral load, and false-negative results could be obtained from unscreened samples. Laboratory budgets might also restrict HTS implementation as it requires substantial initial investment and high operational costs. Equipment and reagents are costly, with prices increasing according to the sequencing platform and geographical location. Manufacturers are located in high-income countries, and reagents and supply importation to low- and middle-income countries increases costs twice or three times in comparison with countries from the northern hemisphere; also, delivery times are longer. Data analysis also requires hiring personnel specialized in bioinformatics and escalating computing infrastructure for data storage and processing.
Taking limitations into account, improvements in sequencing technologies and the development of cost-effective platforms will facilitate broader access to comprehensive genomic data, particularly in regions with limited resources. This will aid in the surveillance and monitoring of arboviral outbreaks. Currently, all sequencer manufacturers are developing portable instruments, reducing equipment and reagent costs, which could contribute to NGS democratization. Investment in capacity building and training programs in low- and middle-income countries is essential to ensure that researchers, clinicians, and public health professionals have the necessary skills and knowledge to effectively apply molecular methods and interpret sequencing data for arboviral detection and surveillance. Established external quality control for wet and dry laboratories would increase confidence and the reproducibility of the results. Adopting HTS for routine surveillance of arboviruses would enhance the understanding of transmission dynamics, arboviral genetic evolution and improve diagnostic tests, allowing for rapid responses and containment measures to prevent widespread infections.

Author Contributions

Conceptualization, L.P.; writing—original draft preparation, L.P. and A.D.B.; writing—review and editing, L.P., A.D.B., A.C.-M. and M.R.-A.; supervision, L.P. and M.R.-A.; project administration, L.P. and M.R.-A.; funding acquisition, L.P. and M.R.-A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, in the frame of the project: “Investigación de arbovirus y otros agentes infecciosos, analizados en muestras de pacientes febriles colectadas en Ecuador durante 2019–2021”, grant number “INEDITA PIC-21-INE-INSPI-001”.

Acknowledgments

We acknowledge the National Health Institute and Magaly Valencia for administrative and technical support.

Conflicts of Interest

The authors declare no conflicts of interest. Mary Regato retired from INSPI in November 2023 but is still the co-director of the project.

Appendix A

Table A1. Tools employed in genomics and metagenomics analyses.
Table A1. Tools employed in genomics and metagenomics analyses.
CategoryTool NameDescriptionNGS/TGSReference
Pre-processing and Quality ControlFastQCQuality control analysis tool for high throughput sequencing dataNGS[76]
NanoplotQuality control analysis tool for ONT dataTGS[77]
NanopolishSoftware package for signal-level analysis of ONT dataTGS[78]
PorechopAdapter trimmer for ONT readsTGS[79]
FitlongTool for filtering long reads by qualityTGShttps://github.com/rrwick/Filtlong#license (accessed on 24 July 2024)
TrimmomaticRead-trimming tool for Illumina NGSNGS[80]
CutadaptRemove adapter sequencesNGS[81]
De novo genome assemblyFlyeDe novo assembler for single molecule sequencing reads using repeat graphsTGS[82]
CanuA single molecule sequence assembler for large and small genomesTGS[83]
UnicyclerHybrid assembly pipeline for bacterial genomesNGS/TGS[84]
SpadesGenome assembler for genomes of regular and single-cell projectsNGS/TGS[85]
MedakaTool to create consensus sequences and variant calls from nanopore sequencing dataTGShttps://github.com/nanoporetech/medaka (accessed on 24 July 2024)
Wtdbg2De novo assembler for long noisy sequences, based on fuzzy Bruijn graphsTGS[86]
Short-read mappingBWAMaps short reads against reference genomeNGS[87]
STARRNA-seq aligner that can detect and align both spliced and unspliced readsNGS[88]
Long-read mappingMinimap2Pairwise aligner for genomic and spliced nucleotide sequencesTGS[89]
GraphMapA highly sensitive and accurate mapper for long, error-prone readsTGS[90]
Taxonomy and metagenomicsKraken2Taxonomic sequence classification systemTGS[91]
PlasFlowSoftware for prediction of plasmid sequences in metagenomic assembliesTGS[92]
MetaPhIan2Tool to profile structure and composition of microbial communities (bacteria, archaea, eukaryotes, and viruses) from metagenomic shotgun sequencing data with species-level resolutionNGS[93]
DIAMONDTool for rapidly searching large DNA or protein sequence databases for short sequencesNGS[94]
VisualizationKronaInteractively explore metagenomes and more from a web browserNGS/TGS[95]
PhinchData visualization framework aimed at promoting novel explorations of large biological datasetsNGS/TGS[96]
Variant callingMedakaCreate a consensus sequence from ONT dataTGShttps://github.com/nanoporetech/medaka (accessed on 24 July 2024)
Statistical analysisBrackenRelative abundance estimation tool for single-level abundance using Kraken read classification outputTGS[97]
Table A2. Web-based tools for virus analysis.
Table A2. Web-based tools for virus analysis.
Web-Based ToolsBrief DescriptionLinkReference
BV-BRCBacterial and Viral Bioinformatics Resource Center, an information system designed to support research on bacterial and viral infectious diseases. It allows single or multiple assemblers to be invoked to compare resultshttps://www.bv-brc.org/app/Assembly2 (accessed on 24 July 2024)[98]
CASTORProvides an open access, collaborative and reproducible machine learning classifier that enables novel and accurate large-scale virus studies.http://castor.bioinfo.uqam.ca (unable to access, returns an error message)[99]
EDGEWorkflows for metagenomic analysis that are pre-configured and may be accessible via a webpage or a local installation. Supports long read and viral genome analysishttps://edgebioinformatics.org/ (accessed on 24 July 2024)[100]
GalaxyPlatform that provides access to a wide range of tools for analyzing biological data which includes DNA and RNA sequencing, proteomics and metabolomics datahttps://galaxyproject.org/ (accessed on 24 July 2024)[101]
Genome
detective		A web-based platform that enables the identification of organisms responsible for an outbreak and evolutionary information of pathogens crucial in understanding the unique phenotypes such as drug resistance, virulence and disease outcome.http://www.genomedetective.com/app/ (accessed on 24 July 2024)[75]
IMGEnables comparative analysis of
metagenomes registered on Genomes
OnLine Database. Supports analysis
of viral genomes through IMG/VR		https://img.jgi.doe.gov/ (accessed on 24 July 2024)[102]
KaijuFast and sensitive taxonomic classification of high-throughput sequencing reads from metagenomic whole genome sequencing or metatranscriptomics experimentshttps://bioinformatics-centre.github.io/kaiju/ (accessed on 24 July 2024)[103]
Metavir 2Web server dedicated to the analysis of viral metagenomes (viromes)http://metavir-meb.univ-bpclermont.fr/ (unable to access, returns an error message)[104]
MGnifyPlatform for analysis of European Nucleotide Archive (ENA)-based sequence and metadata. Provides pipelines for the detection, annotationa and taxonomic classification of viral contigs in metagenomic and metatranscriptomic assemblies.https://www.ebi.ac.uk/metagenomics/ (accessed on 24 July 2024)[105]
NanoGalaxyNanopore long-read sequencing data analysis in Galaxyhttps://galaxyproject.org/use/nanogalaxy/ (accessed on 24 July 2024)[106]
NextstrainA real-time pathogen evolution tracking platform that provides evolutionary information in the form of interactive visualizations. It has been used to track various arboviral epidemics globally.https://nextstrain.org/ (accessed on 24 July 2024)[107]
VirusDetectUses deep sequencing of small RNAs to identify both known and novel viruses in plants and animals. It performs both reference-guided and de novo assemblies of small RNA sequences and compares them to a curated virus reference database.http://virusdetect.feilab.net/cgi-bin/virusdetect/index.cgi (accessed on 24 July 2024)[108]

References

  1. Gohil, N.; Panchasara, H.; Patel, S.; Singh, V. Molecular Biology Techniques for the Identification and Genotyping of Microorganisms. In Microbial Genomics in Sustainable Agroecosystems; Springer Nature: Singapore, 2019. [Google Scholar]
  2. Fitzpatrick, A.H.; Rupnik, A.; O’Shea, H.; Crispie, F.; Keaveney, S.; Cotter, P. High Throughput Sequencing for the Detection and Characterization of RNA Viruses. Front. Microbiol. 2021, 12, 621719. [Google Scholar] [CrossRef] [PubMed]
  3. Bulcha, B.; Berhane, G.; Merera, O. Review on Viral Metagenomics and Its Future Perspective in Zoonotic and Arboviral Disease Surveillance. J. Biol. Agric. Healthc. 2017, 7, 35–41. [Google Scholar]
  4. Sardi, S.I.; Carvalho, R.H.; Pacheco, L.G.C.; Almeida, J.P.P.D.; Belitardo, E.M.M.D.A.; Pinheiro, C.S.; Campos, G.S.; Aguiar, E.R.G.R. High-Quality Resolution of the Outbreak-Related Zika Virus Genome and Discovery of New Viruses Using Ion Torrent-Based Metatranscriptomics. Viruses 2020, 12, 782. [Google Scholar] [CrossRef] [PubMed]
  5. Miller, B.R. Arboviruses. In Encyclopedia of Virology, 3rd ed.; Mahy, B.W.J., Van Regenmortel, M.H.V., Eds.; Academic Press: Oxford, UK, 2008; pp. 170–176. ISBN 978-0-12-374410-4. [Google Scholar]
  6. LaBeaud, A.D.; Bashir, F.; King, C.H. Measuring the Burden of Arboviral Diseases: The Spectrum of Morbidity and Mortality from Four Prevalent Infections. In Population Health Metrics; Springer: Berlin/Heidelberg, Germany, 2011; Volume 9. [Google Scholar] [CrossRef]
  7. Arredondo, G.J.L.; Mendez Herrera, A.; Medina Cortina, H. Arbovirus En Latinoamérica Arbovirus in Latin America. Acta Pediatr. Mex. 2016, 37, 111–131. [Google Scholar] [CrossRef]
  8. Souza, J.V.C.; Santos, H.d.O.; Leite, A.B.; Giovanetti, M.; Bezerra, R.d.S.; Carvalho, E.d.; Bernardino, J.d.S.T.; Viala, V.L.; Haddad, R.; Ciccozzi, M.; et al. Viral Metagenomics for the Identification of Emerging Infections in Clinical Samples with Inconclusive Dengue, Zika, and Chikungunya Viral Amplification. Viruses 2022, 14, 1933. [Google Scholar] [CrossRef] [PubMed]
  9. Maeda, A.; Maeda, J. Review of Diagnostic Plaque Reduction Neutralization Tests for Flavivirus Infection. Vet. J. 2013, 195, 33–40. [Google Scholar] [CrossRef] [PubMed]
  10. Varghese, J.; De Silva, I.; Millar, D.S. Latest Advances in Arbovirus Diagnostics. Microorganisms 2023, 11, 1159. [Google Scholar] [CrossRef] [PubMed]
  11. Martin, D.A.; Muth, D.A.; Brown, T.; Johnson, A.J.; Karabatsos, N.; Roehrig, J.T. Standardization of Immunoglobulin M Capture Enzyme-Linked Immunosorbent Assays for Routine Diagnosis of Arboviral Infections. J. Clin. Microbiol. 2000, 38, 1823–1826. [Google Scholar] [CrossRef] [PubMed]
  12. Nagarkatti, P.S.; Nagarkatti, M. Comparison of Haemagglutination Inhibition (HI) and Indirect Fluorescent Antibody (IFA) Techniques for the Serological Diagnosis of Certain Flavivirus Infections. J. Trop. Med. Hyg. 1980, 83, 115–117. [Google Scholar]
  13. Wallau, G.L.; Abanda, N.N.; Abbud, A.; Abdella, S.; Abera, A.; Ahuka-Mundeke, S.; Falconi-Agapito, F.; Alagarasu, K.; Ariën, K.K.; Ayres, C.F.J.; et al. Arbovirus Researchers Unite: Expanding Genomic Surveillance for an Urgent Global Need. Lancet Glob. Health 2023, 11, e1501–e1502. [Google Scholar] [CrossRef]
  14. Carter, L.L.; Yu, M.A.; Sacks, J.A.; Barnadas, C.; Pereyaslov, D.; Cognat, S.; Briand, S.; Ryan, M.J.; Samaan, G. Global Genomic Surveillance Strategy for Pathogens with Pandemic and Epidemic Potential 2022–2032. Bull. World Health Organ. 2022, 100, 239. [Google Scholar] [CrossRef] [PubMed]
  15. Maljkovic Berry, I.; Melendrez, M.C.; Bishop-Lilly, K.A.; Rutvisuttinunt, W.; Pollett, S.; Talundzic, E.; Morton, L.; Jarman, R.G. Next Generation Sequencing and Bioinformatics Methodologies for Infectious Disease Research and Public Health: Approaches, Applications, and Considerations for Development of Laboratory Capacity. J. Infect. Dis. 2020, 221, S292–S307. [Google Scholar] [CrossRef] [PubMed]
  16. Batovska, J.; Lynch, S.E.; Rodoni, B.C.; Sawbridge, T.I.; Cogan, N.O. Metagenomic Arbovirus Detection Using MinION Nanopore Sequencing. J. Virol. Methods 2017, 249, 79–84. [Google Scholar] [CrossRef]
  17. Quick, J.; Grubaugh, N.D.; Pullan, S.T.; Claro, I.M.; Smith, A.D.; Gangavarapu, K.; Oliveira, G.; Robles-Sikisaka, R.; Rogers, T.F.; Beutler, N.A.; et al. Multiplex PCR Method for MinION and Illumina Sequencing of Zika and Other Virus Genomes Directly from Clinical Samples. Nat. Protoc. 2017, 12, 1261–1276. [Google Scholar] [CrossRef] [PubMed]
  18. Brinkmann, A.; Nitsche, A.; Kohl, C. Viral Metagenomics on Blood-Feeding Arthropods as a Tool for Human Disease Surveillance. Int. J. Mol. Sci. 2016, 17, 1743. [Google Scholar] [CrossRef] [PubMed]
  19. Pan American Health Organization. Available online: https://www.paho.org/sites/default/files/2023-12/eng-denvalgoritmo-deteccion-por-laboratorio2023eng.pdf (accessed on 13 January 2024).
  20. Waggoner, J.J.; Stittleburg, V.; Natrajan, M.S.; Paniagua-Avila, A.; Bauer, D.; Olson, D.; El Sahly, H.M.; Asturias, E.J.; Anderson, E.J.; Munoz, F.M. Sensitive and Prolonged Detection of Dengue Virus RNA in Whole Blood. Am. J. Trop. Med. Hyg. 2021, 104, 1734–1736. [Google Scholar] [CrossRef]
  21. Ramírez, A.L.; Van Den Hurk, A.F.; Meyer, D.B.; Ritchie, S.A. Searching for the Proverbial Needle in a Haystack: Advances in Mosquito-Borne Arbovirus Surveillance. Parasite Vectors 2018, 11, 1–12. [Google Scholar] [CrossRef]
  22. Krambrich, J.; Bringeland, E.; Hesson, J.C.; Hoffman, T.; Lundkvist, Å.; Lindahl, J.F.; Ling, J. Usage of FTA® Classic Cards for Safe Storage, Shipment, and Detection of Arboviruses. Microorganisms 2022, 10, 1445. [Google Scholar] [CrossRef]
  23. Lanciotti, R.S.; Kosoy, O.L.; Laven, J.J.; Velez, J.O.; Lambert, A.J.; Johnson, A.J.; Stanfield, S.M.; Duffy, M.R. Genetic and Serologic Properties of Zika Virus Associated with an Epidemic, Yap State, Micronesia, 2007. Emerg. Infect. Dis. 2008, 14, 1232–1239. [Google Scholar] [CrossRef]
  24. Lawrie, C.H.; Uzcátegui, N.Y.; Armesto, M.; Bell-Sakyi, L.; Gould, E.A. Susceptibility of Mosquito and Tick Cell Lines to Infection with Various Flaviviruses. Med. Vet. Entomol. 2004, 18, 268–274. [Google Scholar] [CrossRef]
  25. Piantadosi, A.; Kanjilal, S. Diagnostic Approach for Arboviral Infections in the United States. J. Clin. Microbiol. 2020, 58, 10–1128. [Google Scholar] [CrossRef] [PubMed]
  26. Artika, I.M.; Ma’roef, C.N. Laboratory Biosafety for Handling Emerging Viruses. Asian Pac. J. Trop. Biomed. 2017, 7, 483–491. [Google Scholar] [CrossRef] [PubMed]
  27. Tan, C.C.S.; Maurer-Stroh, S.; Wan, Y.; Sessions, O.M.; de Sessions, P.F. A Novel Method for the Capture-Based Purification of Whole Viral Native RNA Genomes. AMB Express 2019, 9, 45. [Google Scholar] [CrossRef] [PubMed]
  28. Dethoff, E.A.; Boerneke, M.A.; Gokhale, N.S.; Muhire, B.M.; Martin, D.P.; Sacco, M.T.; McFadden, M.J.; Weinstein, J.B.; Messer, W.B.; Horner, S.M.; et al. Pervasive Tertiary Structure in the Dengue Virus RNA Genome. Proc. Natl. Acad. Sci. USA 2018, 115, 11513–11518. [Google Scholar] [CrossRef] [PubMed]
  29. Parras-Moltó, M.; López-Bueno, A. Methods for Enrichment and Sequencing of Oral Viral Assemblages: Saliva, Oral Mucosa, and Dental Plaque Viromes. In Methods in Molecular Biology; Springer: Berlin/Heidelberg, Germany, 2018; Volume 1838. [Google Scholar]
  30. Fernandez-Cassi, X.; Rusiñol, M.; Martínez-Puchol, S. Viral Concentration and Amplification from Human Serum Samples Prior to Application of Next-Generation Sequencing Analysis. In Methods in Molecular Biology; Springer: Berlin/Heidelberg, Germany, 2018; Volume 1838, ISBN 978-1-4939-8682-8. [Google Scholar]
  31. Shi, Y.; Wang, G.; Lau, H.C.H.; Yu, J. Metagenomic Sequencing for Microbial DNA in Human Samples: Emerging Technological Advances. Int. J. Mol. Sci. 2022, 23, 2181. [Google Scholar] [CrossRef] [PubMed]
  32. Sahajpal, N.S.; Mondal, A.K.; Njau, A.; Petty, Z.; Chen, J.; Ananth, S.; Ahluwalia, P.; Williams, C.; Ross, T.M.; Chaubey, A.; et al. High-Throughput next-Generation Sequencing Respiratory Viral Panel: A Diagnostic and Epidemiologic Tool for Sars-Cov-2 and Other Viruses. Viruses 2021, 13, 2063. [Google Scholar] [CrossRef] [PubMed]
  33. Green, M.R.; Sambrook, J. Purification of Total RNA from Mammalian Cells and Tissues. Cold Spring Harb. Protoc. 2020, 2020, pdb-prot101659. [Google Scholar] [CrossRef]
  34. Waweru, J.W.; de Laurent, Z.; Kamau, E.; Said, K.; Gicheru, E.; Mutunga, M.; Kibet, C.; Kinyua, J.; Nokes, D.J.; Sande, C.; et al. Enrichment Approach for Unbiased Sequencing of Respiratory Syncytial Virus Directly from Clinical Samples. Wellcome Open Res. 2021, 6, 99. [Google Scholar] [CrossRef] [PubMed]
  35. Schuele, L.; Cassidy, H.; Lizarazo, E.; Strutzberg-Minder, K.; Schuetze, S.; Loebert, S.; Lambrecht, C.; Harlizius, J.; Friedrich, A.W.; Peter, S.; et al. Assessment of Viral Targeted Sequence Capture Using Nanopore Sequencing Directly from Clinical Samples. Viruses 2020, 12, 1358. [Google Scholar] [CrossRef]
  36. Deng, X.; Achari, A.; Federman, S.; Yu, G.; Somasekar, S.; Bártolo, I.; Yagi, S.; Mbala-Kingebeni, P.; Kapetshi, J.; Ahuka-Mundeke, S.; et al. Metagenomic Sequencing with Spiked Primer Enrichment for Viral Diagnostics and Genomic Surveillance. Nat. Microbiol. 2020, 5, 443–454. [Google Scholar] [CrossRef]
  37. Brinkmann, A.; Uddin, S.; Krause, E.; Surtees, R.; Dinçer, E.; Kar, S.; Hacıoğlu, S.; Özkul, A.; Ergünay, K.; Nitsche, A. Utility of a Sequence-Independent, Single-Primer-Amplification (Sispa) and Nanopore Sequencing Approach for Detection and Characterization of Tick-Borne Viral Pathogens. Viruses 2021, 13, 203. [Google Scholar] [CrossRef] [PubMed]
  38. Christenbury, J.G.; Aw, P.P.K.; Ong, S.H.; Schreiber, M.J.; Chow, A.; Gubler, D.J.; Vasudevan, S.G.; Ooi, E.E.; Hibberd, M.L. A Method for Full Genome Sequencing of All Four Serotypes of the Dengue Virus. J. Virol. Methods 2010, 169, 202–206. [Google Scholar] [CrossRef] [PubMed]
  39. Slatko, B.E.; Gardner, A.F.; Ausubel, F.M. Overview of Next-Generation Sequencing Technologies. Curr. Protoc. Mol. Biol. 2018, 122, e59. [Google Scholar] [CrossRef] [PubMed]
  40. Bishop-Lilly, K.A.; Turell, M.J.; Willner, K.M.; Butani, A.; Nolan, N.M.E.; Lentz, S.M.; Akmal, A.; Mateczun, A.; Brahmbhatt, T.N.; Sozhamannan, S.; et al. Arbovirus Detection in Insect Vectors by Rapid, High- Throughput Pyrosequencing. PLoS Negl. Trop. Dis. 2010, 4, e878. [Google Scholar] [CrossRef] [PubMed]
  41. Cruz, C.D.; Torre, A.; Troncos, G.; Lambrechts, L.; Leguia, M. Targeted Full-Genome Amplification and Sequencing of Dengue Virus Types 1–4 from South America. J. Virol. Methods 2016, 235, 158–167. [Google Scholar] [CrossRef]
  42. Hu, T.; Chitnis, N.; Monos, D.; Dinh, A. Next-Generation Sequencing Technologies: An Overview. Hum. Immunol. 2021, 82, 801–811. [Google Scholar] [CrossRef] [PubMed]
  43. Jerome, H.; Taylor, C.; Sreenu, V.B.; Klymenko, T.; Filipe, A.D.S.; Jackson, C.; Davis, C.; Ashraf, S.; Wilson-Davies, E.; Jesudason, N.; et al. Metagenomic Next-Generation Sequencing Aids the Diagnosis of Viral Infections in Febrile Returning Travellers. J. Infect. 2019, 79, 383–388. [Google Scholar] [CrossRef] [PubMed]
  44. Houldcroft, C.J.; Beale, M.A.; Breuer, J. Clinical and Biological Insights from Viral Genome Sequencing. Nat. Rev. Microbiol. 2017, 15, 183–192. [Google Scholar] [CrossRef] [PubMed]
  45. Brinkmann, A.; Ergünay, K.; Radonić, A.; Kocak Tufan, Z.; Domingo, C.; Nitsche, A. Development and Preliminary Evaluation of a Multiplexed Amplification and next Generation Sequencing Method for Viral Hemorrhagic Fever Diagnostics. PLoS Negl. Trop. Dis. 2017, 11, e0006075. [Google Scholar] [CrossRef]
  46. Fitch, A.; Rogers, M.B.; Cui, L.; Ghedin, E. Arbovirus Genomics and Metagenomics. In Arboviruses: Molecular Biology, Evolution and Control; Caister Academic Press: Poole, UK, 2016. [Google Scholar]
  47. Lu, H.; Giordano, F.; Ning, Z. Oxford Nanopore MinION Sequencing and Genome Assembly. Genom. Proteom. Bioinform. 2016, 14, 265–279. [Google Scholar] [CrossRef]
  48. Ciuffreda, L.; Rodríguez-Pérez, H.; Flores, C. Nanopore Sequencing and Its Application to the Study of Microbial Communities. Comput. Struct. Biotechnol. J. 2021, 19, 1497–1511. [Google Scholar] [CrossRef] [PubMed]
  49. Craddock, H.A.; Motro, Y.; Zilberman, B.; Khalfin, B.; Bardenstein, S.; Moran-Gilad, J. Long-Read Sequencing and Hybrid Assembly for Genomic Analysis of Clinical Brucella Melitensis Isolates. Microorganisms 2022, 10, 619. [Google Scholar] [CrossRef] [PubMed]
  50. Faria, N.R.; Sabino, E.C.; Nunes, M.R.T.; Alcantara, L.C.J.; Loman, N.J.; Pybus, O.G. Mobile Real-Time Surveillance of Zika Virus in Brazil. Genome Med. 2016, 8, 97. [Google Scholar] [CrossRef] [PubMed]
  51. Fonseca, V.S. Development of Bioinformatics Tools for Assembly and Genomic Characterization of Emerging and Reemerging Viruses Circulating in Brazil; Universidade Federal de MinasGerais: Belo Horizonte, Brazil, 2022. [Google Scholar]
  52. Bharti, R.; Grimm, D.G. Current Challenges and Best-Practice Protocols for Microbiome Analysis. Brief. Bioinform. 2021, 22, 178–193. [Google Scholar] [CrossRef] [PubMed]
  53. Esnault, G.; Earley, B.; Cormican, P.; Waters, S.M.; Lemon, K.; Cosby, S.L.; Lagan, P.; Barry, T.; Reddington, K.; McCabe, M.S. Assessment of Rapid MinION Nanopore DNA Virus Meta-Genomics Using Calves Experimentally Infected with Bovine Herpes Virus-1. Viruses 2022, 14, 1859. [Google Scholar] [CrossRef] [PubMed]
  54. Stewart-Ibarra, A.M.; Ryan, S.J.; Kenneson, A.; King, C.A.; Abbott, M.; Barbachano-Guerrero, A.; Beltrán-Ayala, E.; Borbor-Cordova, M.J.; Cárdenas, W.B.; Cueva, C.; et al. The Burden of Dengue Fever and Chikungunya in Southern Coastal Ecuador: Epidemiology, Clinical Presentation, and Phylogenetics from the First Two Years of a Prospective Study. Am. J. Trop. Med. Hyg. 2018, 98, 1444–1459. [Google Scholar] [CrossRef] [PubMed]
  55. Sanborn, M.A.; Klein, T.A.; Kim, H.C.; Fung, C.K.; Figueroa, K.L.; Yang, Y.; Asafo-Adjei, E.A.; Jarman, R.G.; Hang, J. Metagenomic Analysis Reveals Three Novel and Prevalent Mosquito Viruses from a Single Pool of Aedes Vexans Nipponii Collected in the Republic of Korea. Viruses 2019, 11, 222. [Google Scholar] [CrossRef] [PubMed]
  56. Schulz, A.; Sadeghi, B.; Stoek, F.; King, J.; Fischer, K.; Pohlmann, A.; Eiden, M.; Groschup, M.H. Whole-Genome Sequencing of Six Neglected Arboviruses Circulating in Africa Using Sequence-Independent Single Primer Amplification (SISPA) and MinION Nanopore Technologies. Pathogens 2022, 11, 1502. [Google Scholar] [CrossRef]
  57. Tchouassi, D.P.; Marklewitz, M.; Chepkorir, E.; Zirkel, F.; Agha, S.B.; Tigoi, C.C.; Koskei, E.; Drosten, C.; Borgemeister, C.; Torto, B.; et al. Sand Fly–Associated Phlebovirus with Evidence of Neutralizing Antibodies in Humans, Kenya. Emerg. Infect. Dis. 2019, 25, 681–690. [Google Scholar] [CrossRef]
  58. Wongsurawat, T.; Jenjaroenpun, P.; Taylor, M.K.; Lee, J.; Tolardo, A.L.; Parvathareddy, J.; Kandel, S.; Wadley, T.D.; Kaewnapan, B.; Athipanyasilp, N.; et al. Rapid Sequencing of Multiple RNA Viruses in Their Native Form. Front. Microbiol. 2019, 10, 260. [Google Scholar] [CrossRef]
  59. Vogels, C.B.F.; Hill, V.; Breban, M.I.; Chaguza, C.; Paul, L.M.; Sodeinde, A.; Taylor-Salmon, E.; Ott, I.M.; Petrone, M.E.; Dijk, D.; et al. DengueSeq: A Pan-Serotype Whole Genome Amplicon Sequencing Protocol for Dengue Virus. BMC Genom. 2024, 25, 433. [Google Scholar] [CrossRef] [PubMed]
  60. Batovska, J.; Mee, P.T.; Sawbridge, T.I.; Rodoni, B.C.; Lynch, S.E. Enhanced Arbovirus Surveillance with High-Throughput Metatranscriptomic Processing of Field-Collected Mosquitoes. Viruses 2022, 14, 2759. [Google Scholar] [CrossRef] [PubMed]
  61. Mendes, C.I.; Lizarazo, E.; Machado, M.P.; Silva, D.N.; Tami, A.; Ramirez, M.; Couto, N.; Rossen, J.W.A.; Carriço, J.A. DEN-IM: Dengue Virus Genotyping from Amplicon and Shotgun Metagenomic Sequencing. Microb. Genom. 2020, 6, e000328. [Google Scholar] [CrossRef] [PubMed]
  62. Liu, B.M.; Mulkey, S.B.; Campos, J.M.; DeBiasi, R.L. Laboratory Diagnosis of CNS Infections in Children Due to Emerging and Re-Emerging Neurotropic Viruses. Pediatr. Res. 2024, 95, 543–550. [Google Scholar] [CrossRef] [PubMed]
  63. Pérez-Losada, M.; Arenas, M.; Galán, J.C.; Bracho, M.A.; Hillung, J.; García-González, N.; González-Candelas, F. High-Throughput Sequencing (HTS) for the Analysis of Viral Populations. Infect. Genet. Evol. 2020, 80, 104208. [Google Scholar] [CrossRef] [PubMed]
  64. Faria, N.R.; Do Socorro Da Silva Azevedo, R.; Kraemer, M.U.G.; Souza, R.; Cunha, M.S.; Hill, S.C.; Thézé, J.; Bonsall, M.B.; Bowden, T.A.; Rissanen, I.; et al. Zika Virus in the Americas: Early Epidemiological and Genetic Findings. Science 2016, 352, 345–349. [Google Scholar] [CrossRef] [PubMed]
  65. de Matos, S.M.S.; Hennigen, A.F.; Wachholz, G.E.; Rengel, B.D.; Schuler-Faccini, L.; Roehe, P.M.; Varela, A.P.M.; Fraga, L.R. Possible Emergence of Zika Virus of African Lineage in Brazil and the Risk for New Outbreaks. Front. Cell Infect. Microbiol. 2021, 11, 680025. [Google Scholar] [CrossRef] [PubMed]
  66. Ciuoderis, K.A.; Usuga, J.; Moreno, I.; Perez-Restrepo, L.S.; Flórez, D.Y.; Cardona, A.; Cloherty, G.A.; Berg, M.G.; Hernandez-Ortiz, J.P.; Osorio, J.E. Characterization of Dengue Virus Serotype 2 Cosmopolitan Genotype Circulating in Colombia. Am. J. Trop. Med. Hyg. 2023, 109, 1298–1302. [Google Scholar] [CrossRef] [PubMed]
  67. de Jesus, J.G.; Dutra, K.R.; Sales, F.C.d.S.; Claro, I.M.; Terzian, A.C.; Candido, D.d.S.; Hill, S.C.; Thézé, J.; Torres, C.; D’agostini, T.L.; et al. Genomic Detection of a Virus Lineage Replacement Event of Dengue Virus Serotype 2 in Brazil, 2019. Mem. Inst. Oswaldo Cruz 2020, 115, e190423. [Google Scholar] [CrossRef]
  68. Laiton-Donato, K.; Guzmán-Cardozo, C.; Peláez-Carvajal, D.; Ajami, N.J.; Navas, M.C.; Parra-Henao, G.; Usme-Ciro, J.A. Evolution and Emergence of Mosquito-Borne Viruses of Medical Importance: Towards a Routine Metagenomic Surveillance Approach. J. Trop. Ecol. 2023, 39, e13. [Google Scholar] [CrossRef]
  69. Dente, M.G.; Riccardo, F.; Bolici, F.; Colella, N.A.; Jovanovic, V.; Drakulovic, M.; Vasic, M.; Mamlouk, H.; Maazaoui, L.; Bejaoui, M.; et al. Implementation of the One Health Approach to Fight Arbovirus Infections in the Mediterranean and Black Sea Region: Assessing Integrated Surveillance in Serbia, Tunisia and Georgia. Zoonoses Public Health 2019, 66, 276–287. [Google Scholar] [CrossRef] [PubMed]
  70. Leandro, A.d.S.; Lopes, R.D.; Martins, C.A.; Rivas, A.V.; da Silva, I.; Galvão, S.R.; Maciel-De-freitas, R. The Adoption of the One Health Approach to Improve Surveillance of Venomous Animal Injury, Vector-Borne and Zoonotic Diseases in Foz Do Iguaçu, Brazil. PLoS Negl. Trop. Dis. 2021, 15, e0009109. [Google Scholar] [CrossRef] [PubMed]
  71. Amato, L.; Dente, M.G.; Calistri, P.; Declich, S.; on behalf of the MediLabSecure Working Group. Integrated Early Warning Surveillance: Achilles′ Heel of One Health? Microorganisms 2020, 8, 84. [Google Scholar] [CrossRef] [PubMed]
  72. Coffey, L.L.; Page, B.L.; Greninger, A.L.; Herring, B.L.; Russell, R.C.; Doggett, S.L.; Haniotis, J.; Wang, C.; Deng, X.; Delwart, E.L. Enhanced Arbovirus Surveillance with Deep Sequencing: Identification of Novel Rhabdoviruses and Bunyaviruses in Australian Mosquitoes. Virology 2014, 448, 146–158. [Google Scholar] [CrossRef] [PubMed]
  73. Lewandowska, D.W.; Zagordi, O.; Geissberger, F.D.; Kufner, V.; Schmutz, S.; Böni, J.; Metzner, K.J.; Trkola, A.; Huber, M. Optimization and Validation of Sample Preparation for Metagenomic Sequencing of Viruses in Clinical Samples. Microbiome 2017, 5, 94. [Google Scholar] [CrossRef]
  74. Fonseca, V.; Libin, P.J.K.; Theys, K.; Faria, N.R.; Nunes, M.R.T.; Restovic, M.I.; Freire, M.; Giovanetti, M.; Cuypers, L.; Nowé, A.; et al. A Computational Method for the Identification of Dengue, Zika and Chikungunya Virus Species and Genotypes. PLoS Negl. Trop. Dis. 2019, 13, e0007231. [Google Scholar] [CrossRef] [PubMed]
  75. Vilsker, M.; Moosa, Y.; Nooij, S.; Fonseca, V.; Ghysens, Y.; Dumon, K.; Pauwels, R.; Alcantara, L.C.; Vanden Eynden, E.; Vandamme, A.M.; et al. Genome Detective: An Automated System for Virus Identification from High-Throughput Sequencing Data. Bioinformatics 2019, 35, 871–873. [Google Scholar] [CrossRef]
  76. Andrews, S. Babraham Bioinformatics. FastQC: A Quality Control Tool for High Throughput Sequence Data. 2010. Available online: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 24 July 2024).
  77. De Coster, W.; D’Hert, S.; Schultz, D.T.; Cruts, M.; Van Broeckhoven, C. NanoPack: Visualizing and Processing Long-Read Sequencing Data. Bioinformatics 2018, 34, 2666–2669. [Google Scholar] [CrossRef] [PubMed]
  78. Quick, J.; Loman, N.J.; Duraffour, S.; Simpson, J.T.; Severi, E.; Cowley, L.; Bore, J.A.; Koundouno, R.; Dudas, G.; Mikhail, A.; et al. Real-Time, Portable Genome Sequencing for Ebola Surveillance. Nature 2016, 530, 228–232. [Google Scholar] [CrossRef]
  79. Wick, R. Porechop; GitHub. Available online: https://github.com/rrwick/Porechop (accessed on 23 July 2024).
  80. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A Flexible Trimmer for Illumina Sequence Data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
  81. Martin, M. Cutadapt Removes Adapter Sequences from High-Throughput Sequencing Reads. EMBnet J. 2011, 17, 10–12. [Google Scholar] [CrossRef]
  82. Kolmogorov, M.; Yuan, J.; Lin, Y.; Pevzner, P.A. Assembly of Long, Error-Prone Reads Using Repeat Graphs. Nat. Biotechnol. 2019, 37, 540–546. [Google Scholar] [CrossRef] [PubMed]
  83. Koren, S.; Walenz, B.P.; Berlin, K.; Miller, J.R.; Bergman, N.H.; Phillippy, A.M. Canu: Scalable and Accurate Long-Read Assembly via Adaptive κ-Mer Weighting and Repeat Separation. Genome Res. 2017, 27, 722–736. [Google Scholar] [CrossRef] [PubMed]
  84. Wick, R.R.; Judd, L.M.; Gorrie, C.L.; Holt, K.E. Unicycler: Resolving Bacterial Genome Assemblies from Short and Long Sequencing Reads. PLoS Comput. Biol. 2017, 13, e1005595. [Google Scholar] [CrossRef] [PubMed]
  85. Nurk, S.; Bankevich, A.; Antipov, D.; Gurevich, A.; Korobeynikov, A.; Lapidus, A.; Prjibelsky, A.; Pyshkin, A.; Sirotkin, A.; Sirotkin, Y.; et al. Assembling Genomes and Mini-Metagenomes from Highly Chimeric Reads. In Research in Computational Molecular Biology: 17th Annual International Conference, RECOMB 2013, Beijing, China, 7–10 April 2013; Proceedings 17; Springer: Berlin/Heidelberg, Germany, 2013; pp. 158–170. [Google Scholar]
  86. Ruan, J.; Li, H. Fast and Accurate Long-Read Assembly with Wtdbg2. Nat. Methods 2020, 17, 155–158. [Google Scholar] [CrossRef] [PubMed]
  87. Li, H.; Durbin, R. Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef] [PubMed]
  88. Dobin, A.; Davis, C.A.; Schlesinger, F.; Drenkow, J.; Zaleski, C.; Jha, S.; Batut, P.; Chaisson, M.; Gingeras, T.R. STAR: Ultrafast Universal RNA-Seq Aligner. Bioinformatics 2013, 29, 15–21. [Google Scholar] [CrossRef] [PubMed]
  89. Li, H. Minimap2: Pairwise Alignment for Nucleotide Sequences. Bioinformatics 2018, 34, 3094–3100. [Google Scholar] [CrossRef]
  90. Sović, I.; Šikić, M.; Wilm, A.; Fenlon, S.N.; Chen, S.; Nagarajan, N. Fast and Sensitive Mapping of Nanopore Sequencing Reads with GraphMap. Nat. Commun. 2016, 7, 11307. [Google Scholar] [CrossRef]
  91. Wood, D.E.; Salzberg, S.L. Kraken: Ultrafast Metagenomic Sequence Classification Using Exact Alignments. Genome Biol. 2014, 15, R46. [Google Scholar] [CrossRef]
  92. Krawczyk, P.S.; Lipinski, L.; Dziembowski, A. PlasFlow: Predicting Plasmid Sequences in Metagenomic Data Using Genome Signatures. Nucleic Acids Res. 2018, 46, e35. [Google Scholar] [CrossRef] [PubMed]
  93. Truong, D.T.; Franzosa, E.A.; Tickle, T.L.; Scholz, M.; Weingart, G.; Pasolli, E.; Tett, A.; Huttenhower, C.; Segata, N. MetaPhlAn2 for Enhanced Metagenomic Taxonomic Profiling. Nat. Methods 2015, 12, 902–903. [Google Scholar] [CrossRef] [PubMed]
  94. Bağcı, C.; Patz, S.; Huson, D.H. DIAMOND+MEGAN: Fast and Easy Taxonomic and Functional Analysis of Short and Long Microbiome Sequences. Curr. Protoc. 2021, 1, e59. [Google Scholar] [CrossRef] [PubMed]
  95. Ondov, B.D.; Bergman, N.H.; Phillippy, A.M. Interactive Metagenomic Visualization in a Web Browser. BMC Bioinform. 2011, 12, 385. [Google Scholar] [CrossRef] [PubMed]
  96. Bik, H.M.; Bu, S.; Grubbs, W. Phinch: An Interactive, Exploratory Data Visualization Framework for Environnmental Sequence Data. Manuscript in Preparation. Available online: https://github.com/PitchInteractiveInc/Phinch (accessed on 24 July 2024).
  97. Ondov, B.D.; Treangen, T.J.; Melsted, P.; Mallonee, A.B.; Bergman, N.H.; Koren, S.; Phillippy, A.M. Mash: Fast Genome and Metagenome Distance Estimation Using MinHash. Genome Biol. 2016, 17, 132. [Google Scholar] [CrossRef] [PubMed]
  98. Pickett, B.E.; Greer, D.S.; Zhang, Y.; Stewart, L.; Zhou, L.; Sun, G.; Gu, Z.; Kumar, S.; Zaremba, S.; Larsen, C.N.; et al. Virus Pathogen Database and Analysis Resource (ViPR): A Comprehensive Bioinformatics Database and Analysis Resource for the Coronavirus Research Community. Viruses 2012, 4, 3209–3226. [Google Scholar] [CrossRef] [PubMed]
  99. Remita, M.A.; Halioui, A.; Malick Diouara, A.A.; Daigle, B.; Kiani, G.; Diallo, A.B. A Machine Learning Approach for Viral Genome Classification. BMC Bioinform. 2017, 18, 208. [Google Scholar] [CrossRef] [PubMed]
  100. Li, P.E.; Lo, C.C.; Anderson, J.J.; Davenport, K.W.; Bishop-Lilly, K.A.; Xu, Y.; Ahmed, S.; Feng, S.; Mokashi, V.P.; Chain, P.S.G. Enabling the Democratization of the Genomics Revolution with a Fully Integrated Web-Based Bioinformatics Platform. Nucleic Acids Res. 2017, 45, 67–80. [Google Scholar] [CrossRef]
  101. The Galaxy Community; Afgan, E.; Nekrutenko, A.; Grüning, B.A.; Blankenberg, D.; Goecks, J.; Schatz, M.C.; Ostrovsky, A.E.; Mahmoud, A.; Lonie, A.J.; et al. The Galaxy Platform for Accessible, Reproducible and Collaborative Biomedical Analyses: 2022 Update. Nucleic Acids Res. 2022, 50, W345–W351. [Google Scholar] [CrossRef]
  102. Markowitz, V.M.; Chen, I.M.A.; Chu, K.; Szeto, E.; Palaniappan, K.; Pillay, M.; Ratner, A.; Huang, J.; Pagani, I.; Tringe, S.; et al. IMG/M 4 Version of the Integrated Metagenome Comparative Analysis System. Nucleic Acids Res. 2014, 42, D568–D573. [Google Scholar] [CrossRef]
  103. Menzel, P.; Ng, K.L.; Krogh, A. Fast and Sensitive Taxonomic Classification for Metagenomics with Kaiju. Nat. Commun. 2016, 7, 11257. [Google Scholar] [CrossRef] [PubMed]
  104. Roux, S.; Tournayre, J.; Mahul, A.; Debroas, D.; Enault, F. Metavir 2: New Tools for Viral Metagenome Comparison and Assembled Virome Analysis. BMC Bioinform. 2014, 15, 76. [Google Scholar] [CrossRef] [PubMed]
  105. Mitchell, A.L.; Almeida, A.; Beracochea, M.; Boland, M.; Burgin, J.; Cochrane, G.; Crusoe, M.R.; Kale, V.; Potter, S.C.; Richardson, L.J.; et al. MGnify: The Microbiome Analysis Resource in 2020. Nucleic Acids Res 2020, 48, D570–D578. [Google Scholar] [CrossRef] [PubMed]
  106. de Koning, W.; Miladi, M.; Hiltemann, S.; Heikema, A.; Hays, J.P.; Flemming, S.; van den Beek, M.; Mustafa, D.A.; Backofen, R.; Grüning, B.; et al. NanoGalaxy: Nanopore Long-Read Sequencing Data Analysis in Galaxy. Gigascience 2020, 9, giaa105. [Google Scholar] [CrossRef] [PubMed]
  107. Hadfield, J.; Megill, C.; Bell, S.M.; Huddleston, J.; Potter, B.; Callender, C.; Sagulenko, P.; Bedford, T.; Neher, R.A. NextStrain: Real-Time Tracking of Pathogen Evolution. Bioinformatics 2018, 34, 4121–4123. [Google Scholar] [CrossRef]
  108. Zheng, Y.; Gao, S.; Padmanabhan, C.; Li, R.; Galvez, M.; Gutierrez, D.; Fuentes, S.; Ling, K.S.; Kreuze, J.; Fei, Z. VirusDetect: An Automated Pipeline for Efficient Virus Discovery Using Deep Sequencing of Small RNAs. Virology 2017, 500, 130–138. [Google Scholar] [CrossRef]
Viruses 16 01242 g001
Figure 1. Schematic diagram of different approaches that could be used for arbovirus HTS workflow for optimal assemblies.
Figure 1. Schematic diagram of different approaches that could be used for arbovirus HTS workflow for optimal assemblies.
Viruses 16 01242 g001
Table 1. Sequencing strategies used for arboviruses for targeted sequencing and/or metagenomics, and the bioinformatic workflows or pipelines employed for analysis.
Table 1. Sequencing strategies used for arboviruses for targeted sequencing and/or metagenomics, and the bioinformatic workflows or pipelines employed for analysis.
ArbovirusDepletion or Enrichment MethodsGenomic Scope (Sequence-
Dependent “sd”
or Sequence-
Independent “si”)		Sequencing PlatformBioinformatic
Workflow/Pipeline		ReferenceAdvantagesDisadvantages
DENV, ZIKV, CHIKVNot usedMetagenomics complete viral genomes (si)NextSeq 2000Pre-processing (FastQC, TrimGalore, and Fastp)
Taxonomic classification and removal of host not targeted reads (Kraken2)
De novo assembly (SPAdes)
Taxonomic classification based on protein identity (Diamond BLASTx)
Phylogenetic analysis (MAFFT, IQ-TREE, and FigTree)		[8]High specificity of viral identification and the unbiased nature of viral detection, full annotation of all viruses present in the tested sample was enabled.Not sensitive enough to detect low levels of viral nucleic acids, may also detect non-infectious viral particles or viral nucleic acids from previous infections.
Simultaneous detection of vector-borne virusesMetagenomic sequencing with spiked primer enrichment (MSSPE)Metagenomic (sd)MiSeq and MinION MK1B or GridION X5Identification of viral reads
Removal of spiked primers
Duplicate reads removed (Prinseq)
Mapping and assembly		[36]Simple, low cost, high sensitivity, and broad detection capability, fast and deployable.Prior knowledge of the target viruses is required.
DENV, CHIKVNot usedMetagenomic next-generation sequencing (si)MiSeqPre-processing (Trim_galore and Prinseq)
Taxonomic classification (Diamond BLASTx)
De novo assembly and mapping (SPAdes, dipSPAdes, and Tanoti)
Visualization (Krona plots and BLAST tables)		[43]Ability to detect new and emerging pathogens, potential for resistance screening and phyloepidemiological analyses. Rapid and accurate identification of viral infections in febrile returning travelers. This is essential for timely treatment and the prevention of further transmission.Limited detection of DNA viruses, enrichment or depletion methods may be necessary.
ZIKV, CHIKVMultiplex tiling PCRMetagenomic amplicon sequencing (sd)MinIONDemultiplexing (demultiplex.py script or Metrichor)
Mapping to reference genome (BWAmem and SAMtools view)
Primer trimming (align_trim.py script)
Variant calling (nanopolish variants)
Consensus generation (margin_cons.py script)
Visualization of variant calls and frequencies (vcfextract.py and pdf_tree.py)		[17]Inexpensive and convenient lab method, multiplex PCR reduces the cost of reagents and minimizes potential sources of laboratory error.Method is not suitable for the discovery of new viruses or for sequencing highly diverse or recombinant viruses because primer schemes are virus-genome-specific.
MiSeqPrimer trimming (Trimmomatic)
Alignment (Novoalign)
Statistics (SAMtools)
YBV1, YPLV3, YSLV1 aRandom amplification with octamer primersMetagenomics complete viral genomes (si)MiSeqPre-processing (Cutadapt and Prinseq-lite)
De novo assemblies (Ray Meta)
Contig/scaffold assembly (Cap3)
Taxonomy classification (iterative BLAST search against the NCBI nt database)
Verification of viral contig sequences (in-house reference-mapping pipeline NGS_Mapper)
Visualization and curation (Geneious R10 and IGV)
Annotations (Blastp, TMHMM, and EVEs)
Phylogenetic analysis (MEGA)		[55]The study identified novel viral genomes in mosquitoes in the Republic of Korea.
The research lays the groundwork for developing a map of complete mosquito viromes that is closely cross-referenced to the population statistics of every mosquito species in the ROK. Eventually, this map might be enlarged to create a global database.		The study did not investigate the potential pathogenicity or transmission of the identified viral genomes.
CCHFV, RVFV, DUGV, NSDV, MIDV, WSLV bSISPAWhole-genome sequencing (si)MinIONPre-processing (Mk1C Guppy)
Demultiplexing and adaptor removal (Porechop)
Mapping and genome assembly (KMA and Minimap2)		[56]Allows for a broad enrichment of viral genomic RNAs without the need for virus-specific whole-genome PCRs. Useful for the detection and characterization of viruses in samples with unknown or complex viral populations.Unclassified reads cannot be distinguished when multiple samples of the same virus but of
different origins have been sequenced in the same run (e.g., CCHFV from a tick and CCHFV
from cell culture supernatant).
DENV types 1–4, CHIKVNot usedWhole-genome sequencing of targeted virus (sd)MiSeqPre-processing (ngs_mapper pipeline and Trimmomatic)
Mapping and remapping to reference (BWA–Maximal Exact Matches)
Assembly processing (samtools and in-house basecaller.py)
Statistics (matplotlib)
Phylogenetic analysis (Phyml)		[54]The study presents one of the most detailed descriptions of dengue and chikungunya illnesses in Ecuador’s southern coast.Researchers only questioned the immediate neighbors of the original index case, which may not be typical of the entire community living within 200 m.
NPV cCell culture and random hexamer primersWhole-genome sequencing (si)MiSeq and Sanger sequencingMapping with phleboviruses and comparison against amino acid sequence libraries (BLAST)
Genome assembly and annotations (Geneious)
Phylogenetic analysis (MEGA)		[57]The broad reverse transcription PCR screening allowed us to discard orthobunyaviruses, alphaviruses, and flaviviruses. The screening for sand fly-borne phleboviruses by targeting the RdRp gene using degenerate primers and phylogenetic analysis allowed us to classify the new virus.This approach requires extensive laboratory work and expertise.
Pool of six viruses: MAYV, VEEV, CHIKV, ZIKV, VSV, and OROV dRibosomal RNA depletionDirect RNA sequencing (sd)MinIONPre-processing (Albacore)
Mapping (Minimap2, Samtools)
Read coverage calculation (Genomecov in BEDtools)
Reference-based assembly (Racon)		[58]Enables the detection of a complex population of RNA viruses simultaneously, fast and easy detection within 3 h.Requires high amounts of virus, poly(A)-tail reaction is not only specific to viral RNA, but host RNA can also be a target, requires prior knowledge of a target nucleotide, this method may not find novel RNA viruses or coinfections.
a Yongsan bunyavirus 1 (YBV1), Yongsan picorna-like virus 3 (YPLV3), Yongsan sobemo-like virus 1 (YSLV1). b Crimean–Congo hemorrhagic fever virus (CCHFV), Rift Valley fever virus (RVFV), Dugbe virus (DUGV), Nairobi sheep disease virus (NSDV), Middleburg virus (MIDV), and Wesselsbron virus (WSLV). c Ntepes virus (NPV). d Mayaro virus (MAYV), Venezuelan equine encephalitis virus (VEEV), Vesicular stomatitis Indiana virus (VSV), and Oropouche virus (OROV)5. Arbovirus, post-sequencing consideration.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Patiño, L.; Benítez, A.D.; Carrazco-Montalvo, A.; Regato-Arrata, M. Genomics for Arbovirus Surveillance: Considerations for Routine Use in Public Health Laboratories. Viruses 2024, 16, 1242. https://doi.org/10.3390/v16081242

AMA Style

Patiño L, Benítez AD, Carrazco-Montalvo A, Regato-Arrata M. Genomics for Arbovirus Surveillance: Considerations for Routine Use in Public Health Laboratories. Viruses. 2024; 16(8):1242. https://doi.org/10.3390/v16081242

Chicago/Turabian Style

Patiño, Leandro, Andrea Denisse Benítez, Andrés Carrazco-Montalvo, and Mary Regato-Arrata. 2024. "Genomics for Arbovirus Surveillance: Considerations for Routine Use in Public Health Laboratories" Viruses 16, no. 8: 1242. https://doi.org/10.3390/v16081242

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop