Unlocking capacities of genomics for the COVID-19 response and future pandemics

Standfirst

During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated development of testing methods, and allowed timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific, and organizational challenges. Here, we discuss the application of genomic and computational methods for the efficient data driven COVID-19 response, advantages of democratization of viral sequencing around the world, and challenges associated with viral genome data collection and processing.

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious pathogen that caused the COVID-19 pandemic, which reached an unprecedented scale of infection not seen since the influenza pandemic of 1918–1919. Within a month of its first reported case in Wuhan, China in December 2019, the virus had spread to many regions within the country as well as in several neighboring countries, including Thailand, Korea, and Japan. As international flights continued to operate, SARS-CoV-2 rapidly spread to Europe and North America¹.

During this time, it became clear that the genomic toolkits are essential for public health decision making, including testing for COVID-19, monitoring for emergence of new virus variants with altered biological or immunological properties, identification of at-risk individuals and informing epidemiological models that describe outbreaks in communities². This has allowed for the observation of SARS-CoV-2 genome evolution in almost real time, rapid tracking of SARS-CoV-2 genetic lineages, and variants of interest and concern (VOIs, VOCs) which in turn have facilitated the development of SARS-CoV-2 clinical tests and prediction of vaccines efficacy against viral variants^3,4. However, to reach the full potential of genomic data for future public health surveillance and outbreak response, we believe it is necessary to expand and coordinate best practices in genomics and bioinformatics that have now been field tested during the COVID-19 response⁵. Herein, we discuss the genomic techniques and corresponding bioinformatics algorithms that are addressing many of the pressing public health issues associated with COVID-19.

Genomics-based methods enabled early warnings of COVID-19 pandemic

When a local team of health professionals was investigating a small local outbreak of pneumonia consisting of the first 59 suspected cases from Wuhan in December 2019, they quickly discovered that they were dealing with a novel virus of unknown origin⁶. This rapid discovery was made possible by modern robust and accurate genomic and bioinformatic tools, which while now used routinely, did not exist a couple of decades ago. On January 30, 2020, when WHO declared a Public Health Emergency of International Concern (PHEIC) 339 SARS-CoV-2 genomes had already been sequenced and characterized¹.

To investigate the newly emerging outbreak, scientists in China performed whole-genome sequencing on specimens, followed by de-novo assembly and end mapping to annotate the complete 29,903 nucleotides long SARS-CoV-2 genome. Bioinformatics analysis revealed that the genome organization of SARS-CoV-2 was consistent with single-stranded, positive-sense RNA from the genus Betacoronavirus⁷. Additionally, sequence alignment tools including BLAST⁸ were used to search for related species of the newly discovered virus in the NCBI GenBank database, revealing alarming similarities to SARS-CoV (SARS-CoV-1), and a much higher similarity with Betacoronavirus from bats, proposing zoonotic origin of the virus. Some SARS-CoV-2 genome fragments, in addition, have the highest similarity to the corresponding fragments from pangolins, which suggests that there were possible recombination events. Subsequent analyses including on additional sarbecovirus genomes from bats and pangolins further scrutinized the evolution and recombination history, and found that the lineage giving rise to SARS-CoV-2 had been probably circulating unnoticed in bats for decades^9,10.

Genomics-based methods shaped the effective COVID-19 response

Once the SARS-CoV-2 genome was sequenced, the authors immediately publicly deposited the genome to GenBank^7,11. Timely open access release of the virus genome sequence was a laudable decision that allowed informed scientific analyses and pandemic preparation to begin immediately.

As the pandemic progressed, increased availability of modern sequencing technologies prompted the collection of SARS-CoV-2 viral genomic data at an unprecedented scale. Within a month, on average about 1,300 genomes were being submitted per day. Within six months of the pandemic (May 2020), GISAID had 110,000 SARS-CoV-2 full-length genome sequences. By December 2021, two years into the pandemic, 67,000 genomes per day were being deposited into public viral genome data repositories like GISAID, COG-UK, and GenBank, which currently contain over 6 million SARS-CoV-2 genomes^12-14 (Figure 1a, Table S1). The unprecedented volume of data collection for SARS-CoV-2 is seen when contrasted with HIV genomic data collection. HIV that consistently captivated the attention of public health officials and the general public since 1980’s, has fewer than 16,000 full-length genome sequences collected by the biggest public HIV database at Los Alamos sequence National Laboratory over the past 40 years¹⁵ (Figure 1a).

Figure 1. Available SARS-CoV-2 genomic sequencing data and its usage for outbreak investigation.

(a) The number of SARS-CoV-2 genomes sequenced according to Global Initiative On Sharing All Influenza Data (GISAID) between January 2020 and December 2021. (b) The number of available SARS-CoV-2 sequences in GISAID per 1 million (1M) individuals for each country or region vs. the number of cases per capita up to March 2021. (c) The number of available SARS-CoV-2 sequences in GISAID per 1 million (1M) individuals for each country in Africa vs. the number of sequencers per capita up to March 2021. Blue line is the correlation of all data points on the plot. (d) The number of available SARS-CoV-2 sequences in GISAID per number of reported COVID-19 cases for each country or region vs. the number of reported COVID-19 cases per capita from December 2019 up to December 2021. (e) Global outbreak investigations by phylogenetic analysis (red) and wastewater studies (yellow), dots were placed in the geographical centers of each country or region.

Sequencing data collected all over the world and rapidly shared on online databases ultimately aided public health officials and governments in making better-informed decisions¹⁶. However, to fully explore the potential of such databases, there are a few issues which need to be solved. Despite the unprecedented pace overall, inevitable delays caused by shortage of sequencing capacity and political interference in some regions led to problems in the logistical chain in these regions, including in sample collection, transporting, and shipping samples¹⁷. Depending on the country and the strength of their public health infrastructure, the median collection to submission time lag differs, ranging from one day to one year. Several factors impact the rate and scale of viral genomic sequencing across the globe. Countries with minimal sequencing are likely to encounter outbreaks of higher severity, leading to blind spots of genomic surveillance that can facilitate the spread of new variants to other countries¹⁷. On average high-income countries shared about 100 times more sequences per capita than low-income countries (Figures 1b and S2). However, some African countries with a low GDP per capita were able to sequence a comparable number of viral genomes of middle- and high-income countries¹⁸. This preparedness can be attributed to previous global initiatives to support African countries in mitigating outbreaks of other viruses that has enhanced the sequencing capacity of the region. Africa provides a remarkable example of the necessity of international cooperation that could be implemented in other parts of the globe to improve pandemic response (Figure 1c). The number of shared coronavirus genomes per capita is correlated with the country's GDP per capita (Figure 1d).

Moving forward, several important data sharing issues need to be addressed to facilitate open and rapid viral genome data sharing. Scientists depositing sequencing data should trust that their rights will be respected by data users and that their authorship rights will not be violated¹⁹. For instance, GISAID data access mechanism proved its ability to overcome these obstacles to the international sharing of virus data, making GISAID the largest repository of influenza and SARS-CoV-2 genomic data^16,20.

Bioinformatics methods are capable of accurately tracking SARS-CoV-2 genomic evolution

As SARS-CoV-2 has spread through the world population over the first year of the pandemic, it gradually evolved into several viral lineages^21-24. Based on statistical analysis of collected SARS-CoV-2 genomes, it was shown that SARS-CoV-2 has a mutation rate of at least 10-fold lower than seasonal influenza²⁵. The lower mutation rate initially gave hope for efficient control of the pandemic through vaccination because the slower the virus mutates, the less chances it has to adapt to vaccines. However, given the large number of COVID-19 cases (>277 million and climbing, according to WHO) and possibly because of SARS-CoV-2 recombination events, new variants continue to evolve, which are being classified as variant under investigation (VUI), of interest (VOI), and of concern (VOC) according to their epidemiological, biological and/or immunological properties. Indeed, some variants acquired numerous mutations in a rapid fashion (variants Alpha and Omicron) and showed evidence of immune escape (Omicron). Notably, it was observed that immunodeficient individuals with unusually long periods of SARS-CoV-2 infection can create a plausible environment for faster SARS-CoV-2 evolution because their immune system allows for viral immune escape²⁶.

Prior to the COVID-19 pandemic, the public health community has had experience with tracking and responding to genome evolution for viruses such as the seasonal flu causing influenza viruses. The Global Influenza Surveillance and Response System (GISRS) was established by WHO for timely collection, genetic and antigenic characterization of these viruses²⁷. Sharing of virus sequence data in the GISAID database along with Nextstrain²⁸ online phylogenetic tool was utilized for biannual influenza A and B vaccine seed strain selection and understanding viral genomic evolution and antigenic drift. GISAID and Nextstrain were both promptly adopted for collecting and analyzing SARS-CoV-2 genomic data, becoming the largest global system for tracking SARS-CoV-2 evolution and monitoring of the new variants.

The widespread application of sequencing technologies became possible because of extensive efforts by the scientific community to benchmark and standardize sequencing protocols and open-source bioinformatics workflows for accurate consensus genome assembly²⁹. However, the use of proprietary next-generation sequencing solutions and software has been more commonplace in well-resourced national and state/province level public health labs. The accessibility of tiled primer sequences (e.g., ARCTIC or midnight primer sets), lower costs of Illumina and Oxford Nanopore sequencing along with open access bioinformatics workflows supported sequencing in dozens of regional public health labs and academic institutions across the world. By December 24th, 2021, 80.49% of available SARS-CoV-2 genomic data at GISAID was generated by Illumina sequencers, 12.46% by Oxford Nanopore, and 3.85% by Pacbio, 1.59% by IonTorrent, 1.29% by BGI, 0.31% by Sanger and 0.02% by QIAGEN (Figure S1a). NCBI GenBank has 91.04% genomic data sequenced by Illumina, 8.1% by Oxford Nanopore, 0.47% by IonTorrent, < 0.01% by PacBio, and 0.38% unspecified (Figure S1b).

This democratization of viral sequencing methods has helped build pathogen sequencing capacity in low- to middle-income countries and has fostered insights into the genomic epidemiology of SARS-CoV-2, including emergence and spread of variants, for example in Colombia (VOI Mu), Ukraine (VOC Delta), the Philippines (VOC Alpha), in the U.K. (VOC Alpha) as it moved to the U.S., and in South Africa, where immune evasive VOC Omicron was identified by genome sequencing^30-33.

Bioinformatics methods enable tracking COVID-19 geographical spread in real time

As viruses evolve, tracking the appearance of new mutations and the locations where they were introduced can reveal geographical transmission routes. These routes help distinguish imported cases from community transmission, aiding the identification of high-risk transmission routes that can be subject to enhanced public health control³⁴. Comparative genomic analyses for studying COVID-19 outbreak transmission dynamics have been mostly conducted using classic maximum likelihood (ML) phylogenetic methods³⁵. Unfortunately, ML methods are not scalable enough to handle large volumes of SARS-CoV-2 genomic data available. It is often a requirement, therefore, for ML to reduce sample size and to consider only a fraction of the data in order to conduct the analysis, which can potentially compromise the accuracy of the results. Alternatively, more scalable approximate maximum parsimony methods (MP) can be used for phylogeny reconstruction for SARS-CoV-2 dense data³⁶. Indeed, it was shown theoretically that with dense enough sampling, MP produces an ML tree under certain maximum likelihood models^37-39. Another approach has been to use network-based methods, which are significantly faster but theoretically less accurate than phylogeny-based methods^40-42.

Diverse publicly available SARS-CoV-2 genome sequences from around the world have aided efficient and accurate tracking of local and global SARS-CoV-2 transmission routes^43-45 (Figure S3). Phylogenetics methods (listed in Table S2) revealed that SARS-CoV-2 was introduced into Europe from China and into the US from China and Europe^34,46-48 and have also been used to track domestic transmission chains and differentiate them from international ones. For example, studies showed that SARS-CoV-2 was likely introduced in Connecticut via a domestic transmission route while the most successful viral introductions in Arizona were likely via domestic travel^34,49. New York City area experienced multiple introductions of SARS-CoV-2, primarily from Europe⁵⁰. Similarly, phylogenetic analysis suggested that SARS-CoV-2 was likely introduced into France from several countries, including China, Italy, the United Arab Emirates, Egypt, and Madagascar⁵¹ (Figure 1e, Tables S2).

Differences in sampling across geographical locations and over time represent a considerable challenge to accurately reconstruct spatial transmission patterns. However, additional data such as travel information and epidemiological estimates may help mitigate non-uniform sampling across geographical locations and time and contribute to a more complete picture of viral spread. This has been illustrated by a study of SARS-CoV-2 importation and establishment in the UK⁵². Large-scale genomic data resulted in estimates of the number and timing of introductions events, but its combination with epidemiological and travel data allowed identification of the spatiotemporal origins of these introductions. Such additional data sources are also being increasingly integrated in phylodynamic inferences. For example, a study of the contribution of persistence versus new introductions to the second COVID-19 wave in Europe made use of Google mobility data to inform the phylogeographic component of the genomic reconstruction⁵³. Furthermore, the individual travel history of sampled individuals can be formally incorporated in such analyses⁵⁴.

Additionally, phylogenetics can be used to monitor the effectiveness of global travel restrictions and lockdowns. For example, it was shown that the risk of domestic transmission of SARS-CoV-2 in Connecticut already exceeded that of international introduction at the time federal travel restrictions were imposed, highlighting the critical need for local surveillance³⁴. Similarly in Brazil, three clades of European origin were established prior to the initiation of travel bans and lockdowns⁵⁵. In the UK, lineages introduced prior to national lockdown were shown to be larger and more dispersed and lineage importation and regional lineage diversity declined after lockdown⁵². Phylogenetics showed that, due to violations of imposed lockdowns with sea trade, several SARS-CoV-2 international introductions likely occurred in Morocco⁵⁶. In Australia, lockdown effectiveness was validated using SARS-CoV-2 genomic data coupled with agent-based modeling, a computation tool to simulate the interactions of autonomous agents such as individuals⁵⁷. Phylogenetic modeling of over 11,000 SARS-CoV-2 genomes collected in Switzerland throughout 2020 enabled estimating the effect of different public health measures, including lockdown, border closure, and test-trace-isolate efforts⁵⁸. Similarly, comparative phylodynamics analysis of SARS-CoV-2 transmission dynamics in neighboring Eastern European countries of Belarus and Ukraine, that followed highly different COVID-19 containment policies, allowed to assess the effectiveness of public health intervention measures in this region, and highlight the role of regional political and social factors in the virus spread⁵⁹.

Genomics methods enable wastewater-based monitoring of SARS-CoV-2 epidemiology

The presence of trace viral genomic material in wastewater has been successfully employed to track antibiotic use⁶⁰, tobacco consumption⁶¹ and the monitoring of several respiratory and enteric viruses including poliovirus⁶². Although COVID-19 is primarily associated with respiratory symptoms, SARS-CoV-2 is regularly shed in feces of infected individuals⁶³. As of December 2021, wastewater-based surveillance for tracking SARS-CoV-2 viral infection dynamics⁶⁴ has been implemented in many countries around the world (Figure 1e).

Wastewater-based epidemiology has been shown to provide more balanced estimates of viral prevalence rates in a population than clinical testing alone due to inherent limitations in testing resources and/or testing uptake rates especially in underserved communities. Combining clinical diagnostics with wastewater-based surveillance can provide a more comprehensive community-level profile of both symptomatic and asymptomatic cases, enabling identification of hospital capacity needs^65-72. Additionally, an important advantage of wastewater monitoring is the ability to detect early-stage outbreaks before they become widespread^62,73-76. Although tracking of SARS-CoV-2 viral RNA via qPCR-based methods can reveal temporal changes of virus prevalence in a given population, it cannot provide underlying epidemiological information for identifying transmission or genomic details on emerging variants. Tracking viral genomic sequences from wastewater significantly ameliorates community prevalence estimates and also detects emerging variants. Tracking SARS-CoV-2 viral genomic sequences from wastewater using a targeted tiled amplicon-based sequencing approach would significantly ameliorate community prevalence estimates and also detect emerging variants⁷⁷.

Wastewater genomic epidemiology can also act as a surrogate to elucidate strain geospatial distributions, helping identify outbreak clusters and track prevailing and newly emerging variants, covering even areas with insufficient clinical testing rates. However, the highly variable nature of wastewater, low viral loads, fragmented RNA and the presence of multiple genotypes in a single sample makes it challenging to obtain good quality genome sequences and discern lineages with a high degree of accuracy⁷⁸.

The commonly used tools used for discerning viral lineages in clinical samples such as pangolin³ and UShER⁷⁹ cannot deconvolute the multiple lineages that are commonly observed in a single wastewater sample and at best detect the most dominant one. As existing lineage calling methods require a single consensus sequence to perform assignment, they are ill-equipped to capture the diversity present in mixed viral samples. Hence, tools to robustly identify the multiple lineages and their relative proportions present in wastewater are critical in understanding and interpreting the underlying sequence data obtained from these samples. For example, a depth-weighted demixing algorithm Freyja⁸⁰, uses a “barcode” library of lineage defining mutations to represent each viral variant and can be used to recover relative abundance in the sample. This approach enabled the early detection of emerging VOCs in wastewater up to 14 days in advance of first clinical detection and also identified multiple instances of cryptic transmission not observed via clinical genomic surveillance⁸¹. Similar algorithms for mutation calling, haplotype reconstruction, and population characterization in viral specimens, can also be used to deconvolute the mixture of variants present in a wastewater sample^82,83. By searching for signature mutations co-occurring on the same amplicon, variant B.1.1.7 in wastewater was detected eight days before the first patient sample was tested positive for the variant⁸⁴. Similarly, RNA transcript quantification methods, such as Kallisto, can be used to estimate the relative abundance of SARS-CoV-2 variants in wastewater⁸⁵. Both digital PCR-based and sequencing-based estimates of variant abundance in wastewater have been used to derive the fitness advantage of a recently introduced variant, an important epidemiological parameter to assess the expected transmissibility and spread of the variant^86,87.

Alternatively, viral genomes in wastewater can be sequenced via next generation sequencing approaches after enriching for a wider array of RNA viruses present in a sample through a hybrid probe-capture approach. This approach allows characterization of the prevalent SARS-CoV-2 genomic variants in a defined local region and dynamics of other pathogenic viruses present in the sample^88-90. Shotgun metagenomic and metatranscriptomic sequencing (i.e. community-based sequencing approaches) can provide a comprehensive snapshot of the viral community ecology and thereby aid in tracking of viruses of clinical significance in a community.

As SARS-CoV-2 transitions to become an endemic pathogen, wastewater genomic sequencing offers a scalable, less expensive, long-term passive surveillance tool for tracking emerging variants in the population. A global metagenomics approach has been suggested to detect, collect, and store samples in preparation for future pandemics^91,92. Resources such as GISAID, GenomeTrakr^93,94 and CDC-NWSS⁹⁵ (National Wastewater Surveillance System)⁹⁵ could facilitate the above efforts.

Outlook

The unprecedented volume of available SARS-CoV-2 genomic data coupled with available bioinformatics tools accelerated the prompt and effective characterization of SARS-CoV-2 genomes and provided tools to epidemiologists and public health officials to more effectively respond to the COVID-19 pandemic. Numerous independent efforts across the globe utilized bioinformatics methods that demonstrated the utility of genomics-based approaches and created a solid foundation for the response to COVID-19 and future pandemics. This was achieved by the standardization of methodology, protocol and data sharing, and applications of using SARS-CoV-2 genomic data in epidemiological investigations.

Genome-based surveillance has been shown to be beneficial in addressing COVID-19. However, the unprecedented volume of sequencing data, currently six million complete SARS-CoV-2 genome sequences in databases, challenged the current systems of data storage, processing, and bioinformatics analysis^16,19,96. Due to various technological burdens, such systems were still in the early stages of development in December of 2019. COVID-19 has led to the mobilization of financial, scientific, and developmental resources in record time, with numerous global surveillance systems that provided resources for outbreak response using SARS-CoV-2 genome analysis (Table 1). A notable example is the timely deployment of GISAID and Nextstrain for addressing the COVID-19 response. This technology has taken a lead in centralizing efforts to collect and analyse SARS-CoV-2 genomic data.

Table 1:

Online services with SARS-CoV-2 genome resources and analytics

Resource	Description	Link
GISAID	Platform for assembled genome sharing and analysis	https://www.gisaid.org/
NCBI GenBank	Sequence read archive (SRA)	https://www.ncbi.nlm.nih.gov/sars-cov-2/
COG-UK	United Kingdom sequences database	https://www.cogconsortium.uk/
PANGO	Lineage analytics	https://cov-lineages.org/
Nextstrain	Phylogenetic analysis	https://nextstrain.org/
WBEC	Wastewater analytics	https://www.covid19wbec.org/
COVID-3D	Structural changes of lineages	http://biosig.unimelb.edu.au/covid3d/
Outbreak.info	Variants reports	https://outbreak.info/
CoVizu	Global and local variant distribution analytical tool	https://filogeneti.ca/covizu/
CoVsurver	GISAID quality check and annotation tool identifying phenotypically or epidemiologically interesting candidate amino acid (aa) changes for further research	https://corona.bii.a-star.edu.sg/ https://www.gisaid.org/epiflu-applications/covsurver-mutations-app/
KSA-KAUST	COVID-19 virus mutation tracker	https://www.cbrc.kaust.edu.sa/covmt/
COVID Genes	Shotgun RNA-seq viral data and host responses	https://covidgenes.weill.cornell.edu/

Emerging VOCs, VOIs, and VUIs are likely to continue shaping the course of the COVID-19 pandemic. Global genomics-based surveillance for new variants, in our view, will continue to play a leading role, with information on all SARS-CoV-2 lineages being collected and made available online for the rapid evaluation of their impact on transmission, virulence, and vaccine escape^97,98. Targeted genomic surveillance of SARS-CoV-2 in immunocompromised patients, in our view, can provide useful insights into the mechanisms of appearance of newly emerging VOCs. This can be done by applying bioinformatics tools for intra-host population analysis similar to those that are already available for other RNA viruses such as HCV and HIV^82,99-102.

Efficient early detection and tracking of potentially dangerous variants requires real-time data from all countries¹⁰³. The European Commission, for example, recommended gaining a capacity of sequencing of at least 5% of positive test results, which can be a good global standard. Yet, many underdeveloped countries in the world face insurmountable logistic, technological, and financial barriers to operating sequencing centers to accommodate this scale, suggesting that developed countries share responsibility for global surveilance¹⁰⁴. Following the example of many African countries, additional sequencing centers in countries without viral genomic sequencing could be established. In regions where that is not practical, a logistically efficient system of obtaining and delivering samples to sequencing centers in other countries might be an appealing alternative.

In our view, there are three potential benefits of a standard genome epidemiological sequencing system. The immediate benefit will allow improved timeliness and accuracy of tracking emerging VOI and VOC. A longer-term goal is an improved ability to learn about evolutionary pressures driving the emergence of novel, potentially dangerous variants. Presently, VOC are declared based on their increased transmissibility or virulence, or decreased effectiveness of public health and social measures, available diagnostics, vaccines and therapeutics. Learning more about evolutionary dynamics of emergent strains may lead to predictions of VOI based on genomic sequence alone, further improving response times. Finally, a truly global system of pathogen genome sequencing and analysis is likely to improve our ability to combat future pandemics.

Global coordination of genomic data surveys will also allow for wider application of wastewater-based or environmental-based virus surveillance¹⁰⁵. Currently, wastewater-based monitoring lacks the granularity of clinical diagnostic testing and cannot discern a particular area of an outbreak when the wastewater treatment plant serves a large population. Sampling at a higher spatial resolution within the sewer system or even at a building-level scale could potentially provide early indications of viral outbreaks and help monitor their progression¹⁰⁶.