Gary Schroth
Gary Schroth, PhD, Illumina

Next-generation sequencing (NGS) has been an invaluable tool to understand both health and disease in many different organisms. However, for the most part, genomes are static replications of the original germline coding that fail to reflect how an organism is responding to environmental change.

In other words, while DNA studies offer an incredible window to improve our understanding of biology, they are only a starting point. Downstream from DNA, epigenomics, transcriptomics, spatial omics, proteomics, and many other omics are providing more comprehensive and granular information to expand our knowledge and improve clinical care.

Multiomics can help us understand a tissue’s full dynamic range, showcasing how cells respond to disease, diet, aging, toxins, temperature, salinity, pH, and many other factors. Over the past decade, new omics techniques have emerged to characterize biology (right down to single cells), and many of these technologies are integrally connected to NGS. At the same time, prices have plummeted. Sequencing a whole genome now costs as little as $200, and that price should continue to drop.

This confluence of powerful new workflows and reduced sequencing costs should have a profound impact on biomedical research. With the cost barrier lessened, life scientists can dive even deeper into biological complexity.

Addressing tumor heterogeneity

Researchers and oncologists have known for years that tumor genomes are heterogeneous, one of the many factors that make cancers so clinically challenging. Sequencing biopsied tissue from only one tumor region, or at best a handful of regions, will not provide the most complete picture of the driving mutations in that tumor, or of the low-prevalence variations that could gain ascendence in response to treatments.1

Low-cost genomic sequencing, combined with less invasive biopsies, will help clinicians more thoroughly sample tumors to create detailed genomic maps. Genetic information from multiple tumor regions will provide more complete information on each patient’s cancer. In turn, advanced informatics will analyze this data to better predict the cancer’s trajectory.

These capabilities are evolving rapidly, and single-cell analyses are playing a major role. New technologies are producing single-cell whole genome and transcriptome data, and they are revealing features such as heterozygous alleles, which are key to calling cancer mutations.2

As sequencing becomes more economical, the barrier will no longer be the cost of sequencing 50 or 100 tumor sites, but rather the difficulty of obtaining that much diverse tumor material. Researchers are working on this problem as well, investigating new techniques to sequence cells from fine-needle biopsies.3 Producing complete genomic and transcriptomic readouts from single cells will advance these efforts.

These analyses could provide a wealth of data about variations throughout a patient’s tumor. Currently, a lot of guesswork goes into understanding tumor heterogeneity—a minor allele could be 1% of a tumor or 10%. More comprehensive sampling and analysis could help change that.

On the clinical level, this could help oncologists look around corners—seeing where the cancer is now and predicting where it’s likely to go. From there, they can develop more comprehensive plans to address the most abundant clones, as well as the more obscure ones that could cause trouble later.

Equally important, reduced costs mean clinicians can bring more tools into the mix to assess each patient’s cancer. Even three years ago, sequencing a tumor genome would have cost thousands of dollars. In the next few years, assuming a $200 genome, laboratories will be able to conduct transcriptomic, proteomic, and possibly even single-cell analyses for $1,000 or less. These capabilities will also advance bench research, as investigators will have more detailed information about tumor microenvironments, cell signaling pathways, and many other elements. Ultimately, spatially resolved genomic/transcriptomic sequencing could help us better manage tumor heterogeneity in the clinic.

A scientist conducts NGS library preparation
A scientist conducts NGS library preparation, which involves converting a genomic DNA sample (or cDNA sample) into a library of fragments which can then be sequenced. [Illumina]

The new virology

NGS played an enormous role in mitigating the SARS-CoV-2 pandemic—from sequencing the viral genome(s) to creating diagnostic tests to developing mRNA vaccines in record time.4 But now that COVID-19 is mostly contained, scientists must scan the horizon for the next viruses that could jump from animals into people. This may be one of the most difficult problems researchers and governments face. There are millions of viruses with the potential to infect humans, and those are only the ones we know about.

The Global Virome Project is working to sequence all viruses, a task that’s possible only through more accessible sequencing.5 Other efforts that focus on coronaviruses have found many that are significantly different genomically than the ones in current databases, highlighting potential future threats.6 On the other side of the coin, the Human Immunome Project, an international group being led by Vanderbilt University Medical Center, is working to identify all the genes and other molecules associated with human immunity.7

Again, inexpensive sequencing will accelerate these efforts, as researchers can stretch their resources. In addition, technologies that support single-cell studies, such as droplet microfluidics, will also play a significant role.8

Another emerging technology, descended from NGS, is multiplex assay technology. It can identify millions of antibody epitopes and comprehensively determine an individual’s pathogen exposures. This offers enormous advantages when assessing patient exposures and determining treatment plans.9

This could be particularly helpful to understanding autoimmune responses, as well as coinfections. We saw during COVID-19 that people infected with SARS-CoV-2 sometimes had respiratory syncytial virus disease and/or influenza as well. Having a broader read on pathogen exposures could, again, improve care.

The importance of understanding pathogenic risks was illustrated by a recent study, sponsored by the Bill and Melinda Gates Foundation, to reassess biological samples from COVID-19 and identify co-infections.10 The researchers ran broader genomic panels to look for different bacterial and viral pathogens, antimicrobial resistance genes, and other markers.

The study essentially produced a pathogen weather map, showing which microbes were most active in certain parts of the country at specific times. This comprehensive approach offers new public health opportunities in advanced and emerging nations around the world.

Scientists conduct NGS library preparation at the laboratory bench.
Scientists conduct NGS library preparation at the laboratory bench. Protocols are available to accommodate different throughput needs and sample types. [Illumina]

Beyond human biology

Genomics discussions tend to be human-centric, but there’s a lot more we can learn in plants and animals. Veterinary medicine is exploding, and many of the omics advances discussed here could easily be applied to that arena.

Agriculture is another area where low-cost genomic sequencing is likely to have a major impact. Agribusiness is already using low-cost genotyping to better understand herds and crops and select for beneficial traits in both. Low-cost NGS and other omics technologies will likely help advance these studies even further.

At the Salk Institute, researchers are using a variety of omics techniques to develop crop plants and wetland plants that are better at sequestering carbon. Most plants do a good job of removing carbon from the atmosphere, but they also give it back when they die or shed leaves. In its plant research, the Salk Institute seeks to create roots that store carbon for decades or longer, which could have a positive impact on our devolving climate.11

These transitions will not be entirely seamless. There is an ongoing evolutionary struggle between omics (and other technologies that produce massive datasets) and the informatics acumen needed to analyze all that information. Machine learning and other computational approaches will have to keep pace to ensure knowledge from single-cell studies and other data-intensive applications is not wasted. The hard part will be transitioning these techniques into the clinic. No clinician has time to personally crunch this much data. We will need systems that provide actionable information in a hurry.

The Human Genome Project was completed a generation ago, and it has spawned a wide array of useful applications. NGS has become the power source for this ecosystem, helping life scientists branch off into even more granular studies. As sequencing prices continue to drop, we will see even more creative applications to dissect biology and improve care, with each application contributing to a more comprehensive view.


Gary Schroth, PhD, is a distinguished scientist, emerging applications, at Illumina.



  1. Zhu L, Jiang M, Wang H, et al. A narrative review of tumor heterogeneity and challenges to tumor drug therapy. Ann. Transl. Med. 2021; 9(16): 1351. DOI: 10.21037/atm-21-1948.
  2. BioSkryb Genomics. Accurate Single-Cell Genomic Analysis Holds the Key to Understanding Cancer Heterogeneity. Accessed November 17, 2023.
  3. Xia Y, Gawad C. Bringing precision oncology to cellular resolution with single-cell genomics. Clin. Exp. Metastasis 2022; 39(1): 79–83. DOI: 10.1007/s10585-021-10129-4.
  4. MacDonald A. NGS During the COVID-19 Pandemic and Beyond. Technology Networks. Accessed November 17, 2023.
  5. Global Virome Project.
  6. Ruiz-Aravena M, McKee C, Gamble A, et al. Ecology, evolution and spillover of coronaviruses from bats. Nat. Rev. Microbiol. 2022; 20: 299–314. DOI: 10.1038/s41579-021-00652-2.
  7. Human Immunome Project.
  8. Jing W, Han HS. Droplet Microfluidics for High-Resolution Virology. Anal. Chem. 2022; 94(23): 8085–8100. DOI: 10.1021/acs.analchem.2c00615.
  9. Ibsen KN, Daugherty PS. Prediction of antibody structural epitopes via random peptide library screening and next generation sequencing. J. Immunol. Methods 2017; 451: 28–36. DOI: 10.1016/j.jim.2017.08.004.
  10. Illumina. Aegis writes a new chapter on pathogen surveillance. Accessed November 17, 2023.
  11. Harnessing Plants Initiative. Salk Institute for Biological Studies.
Previous articleOur Continuously Changing Cellular Genomes
Next articleFDA Approves the First CRISPR Therapy for Sickle Cell Disease