Using NGS for viral surveillance

Collibri Library Prep Kits for Illumina Systems enhance next-generation sequencing (NGS)-based viral surveillance with higher quality sequences and rapid protocols.

  • Increase the quality of sequences to achieve robust tracing of epidemics
  • Improve accuracy and speed of mRNA sequencing results by adding adapters for Illumina NGS directly to the mRNA
  • Identify differences in gene expression that may offer insight into why some people become gravely ill and others do not show symptoms

Viral surveillance

Understanding disease transmission pathways requires collaboration between governmental and private organizations. Government-sponsored programs, such as the Global Influenza Programme (GIP) [1] that is sponsored by the World Health Organization (WHO), establish standards and offer training.

Public-private consortiums, such as the academic labs that partner with the Center for Disease Control (CDC) in the United States, collaborate on viral surveillance for pandemics such as SARS-CoV-2 (COVID-19) to collect and analyze viral samples around the world. This allows the scientific community to monitor global and local trends in virus transmission and to support the selection of strains for vaccine production. Data on the rate of viral transmission may also inform government decisions on transportation, health screening checks, and potential quarantine enforcement policies.

Non-governmental groups such as assist with viral surveillance by compiling public databases and visualizing trends in viral transmission. NGS surveillance can reveal cryptic transmission patterns in which a pathogen is undetected by non-NGS surveillance programs. By sequencing 346 SARS-CoV-2 viruses collected in Washington State prior to mid-March of 2020, including samples obtained from the Seattle Flu Study, Hodcroft and colleagues from the United States and Switzerland estimated that the virus was transmitting undetected in Washington State during January and February of 2020 [2].

Nextstrain visualizes viral transmission pathways

Figure 1. Visualizing viral transmission. The open-source pathogen genome project visualizes data from publicly available SARS-CoV-2 genomes [3].

Improving the quality of pathogen sequences

While the number of available full genomes produced by NGS can grow rapidly, datasets have historically faced challenges in the quality of the sequences. High-quality sequences are required to trace epidemics, monitor for potentially emerging viruses within the population, develop potential anti-viral therapies, identify targets for vaccine development, and understand host-pathogen interactions.

One option to improve the quality of sequences is to use library preparation kits that are designed to handle a wide variety of sample qualities and genome sizes to produce consistent, full-genome coverage. In comparison to transposomic methods, the Invitrogen Collibri DNA Library Prep Kits for Illumina Systems consistently provide full coverage of pathogen and host genomes. The Collibri DNA kits are suitable for genomes of all sizes and may be used to sequence both the pathogen and the host to perform host-pathogen studies.

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Collibri DNA library prep kits cover entire genome

Figure 2. Obtain full genomic coverage to sequence the entire viral genome. With a workflow time of 1.5* hours, the Collibri DNA library prep kits convert the entire genome into sequencing-ready libraries for Illumina platforms. Other library prep methods, such as transposomic methods that rely on near-random insertion of transposome complexes throughout the genome, do not provide coverage of fragment ends.
* PCR adds up to one additional hour.

Optimized protocol to sequence SARS-CoV-2 samples

The optimized workflow to sequence SARS-CoV-2 reduces time and sequencing costs while providing flexibility to use available reagents. Generate high-quality genomic coverage from nasopharyngeal, oropharyngeal, and other diverse samples to improve accuracy in SARS-CoV-2 research studies.

Review the optimized protocol ›

Improve accuracy, speed of RNA sequencing for pathogen research

To improve accuracy of RNA sequencing results for RNA viruses and other samples, the Invitrogen Collibri Stranded RNA Library Prep Kits for Illumina Systems use a unique protocol in which helper adapters are added directly to RNA. The helper adapters are extended into full-length adapters for Illumina NGS systems during PCR in the final steps of the workflow. Unlike library prep kits that convert RNA into cDNA prior to addition of adapters, diversity of RNA transcripts is retained by the Collibri library prep kits because cDNA priming is not affected by GC content. In addition, ligation of helper adapters directly to RNA enhances accuracy of NGS results because random oligonucleotide sequences are not incorporated into cDNA, preventing false SNPs and point mutations.

The Collibri Stranded RNA protocol is rapid (4.5 hours) and produces highly stranded results, enabling detection of overlapping genes. The protocol is suitable for RNA sequencing of genomes of all sizes and so is suitable to study viral genomes, host gene expression, and host-pathogen interactions.

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Figure 3. Rapid 4.5-hour protocol enhances accuracy by ligating helper adapters directly onto RNA. Partial adapters, also known as helper adapters, are ligated directly to single-stranded RNA prior to creation of cDNA. Full-length indexed adapters are generated during PCR.

Identify why a pathogen produces different outcomes

NGS 3′ mRNA gene expression analysis is a powerful, hypothesis-free tool to investigate why people respond in various ways to a pathogen and has been used to study the impact of Zika infections.  

Gene expression profiles of people who become gravely ill can be compared to gene expression profiles from people who do not show symptoms when infected with the same virus (carriers). 3′ mRNA sequencing is a technique to rapidly compare gene expression profiles of many samples using simplified informatics. The method achieves simplicity and cost savings by reducing the number of NGS reads required from 30–60 M down to 2–5 M per sample. The reduction in required reads provides flexibility in choice of Illumina NGS system and location of sequencing.

Gene expression profiles from the Invitrogen 3′ mRNA Library Prep Kits for Illumina Systems can be generated near the source of the sample using pre-built informatics profiles from companies such as Genialis, Inc.

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Figure 4. Gene expression profile of an acute Zika infection. Using the Collibri 3ʹ mRNA Library Prep Kit for Illumina Systems, Dr. Nicholas Maness of Tulane University reveals gene expression patterns (A) and depressed leukocyte mobilization (B) during an acute Zika infection. Watch presentation of results.

Tools for NGS viral surveillance on Illumina NGS systems

Collibri DNA Library Prep kits
Collibri Stranded RNA Library Prep kits**
Collibri 3ʹ mRNA Library Prep kits
NGS method Whole-genome sequencing mRNA sequencing 3ʹ mRNA sequencing
Viral insights
  • de novo assembly of novel pathogens 
  • Genomic mutations 
  • Transmission pathways 
  • Research host susceptibility to infection and predict interferon response
  • Detect coding RNA
  • Gene expression 
  • Gene fusions 
  • Transcript isoforms
  • Gene expression
Suitable for degraded RNA Yes after reverse transcription* Yes Yes
Workflow time 1.5 hr*** 4.5 hr 4.5 hr
Compatible with all Illumina NGS systems Yes Yes Yes

For accurate quantification of libraries prior to sequencing, we recommend using the Collibri Library Quantification Kit or Qubit assays.
* SuperScript IV Reverse Transcriptase is recommended to perform reverse transcription
** The Dynabeads mRNA DIRECT Purification Kit is recommended to perform mRNA enrichment prior to library preparation.
*** Optional PCR adds up to one hour


  1. Global Influenza Programme
  2. Bedford T, et al. Cryptic transmission of SARS-CoV-2 in Washington State. medRxiv 2020.04.02.20051417; doi:
  3. Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, Sagulenko P, Bedford T, Neher RA. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics. 2018 Dec 1;34(23):4121-4123.