Redefining the Impossible With Genomics

For Research Use Only. Not for use in diagnostics procedures. Unlock the next wave of genomic discovery Broader, deeper, multiomic sequencing enabled on the NovaSeq ™ X Series UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 2 | M-GL-01796 v1.0 3 Revolutionizing the pace of discovery 4 The dawn of the Genome Era 5 Broader, deeper sequencing methods 6 Bigger studies to improve statistical power 6 Large-cohort data at work 7 Increasing diversity in genomic data 8 Broader studies to access missed information 9 Value of whole genomes 10 Value of whole transcriptomes 11 Multiomic studies for a wider perspective 12 Common multiomic combinations 13 Next-level multiomics 14 Higher analytical resolution to decode complex systems 14 Single-cell sequencing 15 Spatial sequencing 15 Pushing the boundaries 16 Deeper studies to find rare genetic events 16 Liquid biopsy 17 Genome-wide tumor–normal sequencing 18 How the NovaSeq X Series accelerates genomic discovery 19 Enabling broader, deeper sequencing 21 Managing big experiments and big data 24 Example NovaSeq X Series workflows 26 Unimaginable experiments, made possible 27 Abbreviations 28 References Table of contents UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 3 | M-GL-01796 v1.0 TABLE OF CONTENTS BIGGER STUDIES Revolutionizing the pace of discovery Genomics research is expanding our understanding of biology and making personalized medicine a reality. Advancements in next-generation sequencing (NGS) are enabling genomic visionaries to perform the studies that can answer the most complex biological questions. Increased discovery power will come from larger studies with bigger cohorts, deeper sequencing to identify rare genetic events, and broader sequencing methods and multiomics for a more comprehensive view of cellular activity. Still, this new scale of experiments has been held back by high sequencing costs and analytical complexity. Now, with the NovaSeq X and NovaSeq X Plus Sequencing Systems, the extraordinary can become routine. Transformational throughput and simplified informatics bring greater speed and lower costs that will be a force for democratizing sequencing and powering the Genome Era. UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 4 | M-GL-01796 v1.0 TABLE OF CONTENTS BIGGER STUDIES The dawn of the Genome Era The first draft of the human genome sequence used hundreds of instruments performing capillary electrophoresis Sanger sequencing and took over 13 years and nearly $3 billion (USD). Since the introduction of massively parallel sequencing technologies like NGS, the throughput of genome sequencing has increased and the costs have decreased at an incredible rate. Over the last two decades, sequencing output has grown over 10,000-fold, from less than 1 gigabase (Gb) to 16 terabases (Tb) per run, while the number of reads has increased from millions to tens of billions. Experiments that once required complex workflows now use simple push-button sequencing. These incredible advancements in genome analysis are ushering in a new era of personalized medicine. Whole-genome sequencing (WGS) has helped diagnose children with rare genetic diseases. Comprehensive genomic profiling of tumors has been used to identify driver mutations and match them with targeted therapies. During the COVID-19 pandemic, DNA sequencing tracked specific variants and provided the basis for rapid vaccine development. Managing future pandemics and developing personalized therapies will depend on expanding access to high-throughput genomic sequencing. COST-PER-GENOME Dramatic decrease in sequencing costs Genome Analyzer™ II $3000/Gb 0.3 Gb/day NovaSeq X Series $2/Gb 6000 Gb/day $2000 | 6 hours NY What if this breakneck pace of advancement was applied to air travel? A hypothetical flight from New York City to Paris, France could decrease Paris $1.33 | 1 Sec 2001 $100 M $2 M $100,000 $10,000 $1000 $600 $200 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 from to Cost (USD) per human genome Year UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 5 | M-GL-01796 v1.0 TABLE OF CONTENTS BIGGER STUDIES Broader, deeper sequencing methods Despite this progress, there is much work left to do to unlock the full power of the genome. To understand genetic variation and complex disease, we will need to sequence millions of genomes and integrate health data. We need to represent diverse populations, ensuring that more people can access sequencing in more places. To diagnose rare disease in the neonatal intensive care unit (NICU), we need to sequence genomes at record speed for maximum impact. To use cell-free DNA to track disease noninvasively, we need to sequence deeply. To see the full picture of cellular physiology, we need to look at multiple “omes” together—genomes, transcriptomes, epigenomes, and proteomes. And we will need powerful bioinformatics tools to turn all that data into insight. With the decreased costs of sequencing, scientists can leverage budgets to perform studies of greater scale and depth, as well as new applications. Researchers can generate more detailed data from their samples or interrogate their samples at multiple levels for greater insights. This eBook highlights the types of studies that once were unimaginable, and are now possible thanks to breakthrough advancements in high-throughput genomics. UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 6 | M-GL-01796 v1.0 TABLE OF CONTENTS BROADER STUDIES PACE OF DISCOVERY “A measure of the success of our mission will be whether we can convince similar healthcare systems around the world to adopt approaches developed within the 100,000 Genomes Project to transform the application of genomic medicine in healthcare and bring better outcomes to patients worldwide. Mark Caulfield, Chief Scientist, Genomics England ” Bigger studies to improve statistical power Increase statistical power from larger sample sizes and more diverse cohorts Even decades after the first human genomes were sequenced, a majority of gene–disease connections remain a mystery. To understand the significance of human genetic variation in relation to complex disease, genome-wide association studies (GWAS) aim to connect genotype to phenotype. Most GWAS have been performed using microarray genotyping. As the cost of genome sequencing drops, researchers can generate higher quality, more comprehensive sequence data at population scale. A shared global knowledge base that integrates genomic data with longitudinal health records is needed to clearly identify key genes and disease mechanisms. Large-cohort data at work The 100,000 Genomes Project in the United Kingdom (UK), supported by Genomics England, is the archetype for integrating genomics with a large-scale health system and demonstrates what routine, genomic-informed medicine might look like.1–3 The UK BioBank has also built one of the largest biomedical databases in the world, containing genetic, lifestyle, and health information from 500,000 individuals.4 The population-scale WGS data coming from these projects has yielded incredible insights for cancer and increased diagnostic yield for rare disease.3 For example, WGS of over 12,000 matched tumor–normal samples revealed mutational signatures that could help direct personalized cancer treatments.5 The UK is undertaking an even larger initiative, Our Future Health, which aims to sequence five million more genomes.6 UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 7 | M-GL-01796 v1.0 TABLE OF CONTENTS BROADER STUDIES PACE OF DISCOVERIES Population genomics projects across the globe with numbers of participants United Kingdom1 100,000 France13 1 million Denmark16 50,000 Finland17 500,000 Estonia18 100,000 Turkey19 100,000 Israel22 100,000 Africa7 118,000+ Saudi Arabia21 100,000 United States29 1 million United States31 1 million Australia8 200,000 China26 100,000 China25 100,000 Japan23 100,000 Asia10,11 100,000 Hong Kong24 50,000 Mexico27 1 million Brazil28 7000 United States30 1 million United States32 100,000 United Arab Emirates20 1 million United Kingdom6 5 million Ireland14 400,000 Australia9 7000 Singapore12 100,000 Iceland15 200,000 United Kingdom4 500,000 Many of these efforts focus on increasing the diversity in the pools of genome data to represent underserved groups,7–12,19–27,31 because, as of 2021, 86% of genomics studies have been conducted on individuals of European descent.33 Additional tools are helping draw out the full value of these large-cohort projects. The “pan genome” reference genome includes 47 diverse references to uplevel variant calling.34 The Primate AI-3D resource mines data from other primate genomes (233 species) to train artificial intelligence to use information from natural selection to better classify variants of unknown significance (VUS).35,36 Together, these efforts will help us realize a more complete view of the genome for all populations and develop ways to stop disease outbreaks, decrease early deaths due to sudden heart attacks, and reduce the strain of chronic diseases like Alzheimer’s and diabetes. Learn more Population genomics Increasing diversity in genomic data Increased statistical power for these studies will also come from larger, more diverse cohorts. Expanding access to NGS across the globe, especially in regions with underrepresented populations, will increase the diversity of our genome data sets. Following in the footsteps of the pioneering work in the UK, population genomics projects are growing in other countries and regions around the world.7–32 UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 8 | M-GL-01796 v1.0 TABLE OF CONTENTS MULTIOMIC STUDIES BIGGER STUDIES Broader studies to access missed information See the whole breadth of variation with whole-genome or whole-transcriptome sequencing Historically, when sequencing is expensive and data analysis is burdensome, many labs have chosen targeted sequencing approaches and narrowed their study focus. With lower costs and streamlined informatics, it becomes more practical to query more broadly—to shift from gene panels to exomes or from exomes to whole genomes—and gather the most data possible from each sample. The scale of broader research minimizes bias from experiments by extending analyses beyond a few preselected targets. * Coverage requirements vary depending on use-case, such as rarity of factor being measured. Whole-genome sequencing Whole-exome sequencing Sequencing region Entire genome Coverage Pros Cons ~30×* Captures all DNA variants in the least biased way Higher per-sample cost, more demanding analysis Sequencing region Protein-coding regions (~2% of the genome) Coverage Pros Cons ~50×–100×* Reliable detection of coding variants Manipulation of the sample, misses 98% of the genome Targeted sequencing Sequencing region Specific genes or regions of interest Coverage Pros Cons > 500×* Resources focused on specific areas of interest Limited resolution Beyond the exome Exome: 2% Noncoding genome: 98% Structural variation Repeat expansions Copy number variants Mitochondrial variants Noncoding SNVs and indels Regulatory elements In our view, for deciphering the molecular causes of any genetic disorder, we need the variants of the entire genome—especially when various disorders exhibit overlapping symptoms and consequences. “ Dr. Kamran Shazand, Director, Shriners Genomics Institute ” UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 9 | M-GL-01796 v1.0 TABLE OF CONTENTS MULTIOMIC STUDIES BIGGER STUDIES Value of whole genomes When NGS is more accessible, studies can cast a “wider net” to survey more genes. For example, labs that routinely do exome sequencing can now afford to switch to WGS. WGS offers multiple advantages for finding variants. WGS examines the entire genome and has the capability to assess variants in both coding and noncoding regions of the genome.37–43 Noncoding variants are frequently key biomarkers for disease. Unlike other methods, WGS robustly captures all common variant types.37–39,44 WGS captures copy number variants (CNVs) with greater resolution than chromosomal microarrays (CMAs).37,43,45,46 WGS also captures some variants in exomes with greater accuracy than whole-exome sequencing (WES).2,3,37,44,46–49 † Variant detection may vary depending on the particular laboratory and performance limits of validated variant types. Detection of repeat expansions by PCR is typically limited to single-gene analysis, compared to multigene capabilities of WGS. Improvements to SV callers and increased success with long-read whole-genome approaches suggest fully capable SV insights from WGS. PCR, polymerase chain reaction; FISH, fluorescence in situ hybridization. WGS provides the most comprehensive analysis of genomic variant types† Single nucleotide variants (SNVs)37 Insertion-deletions (Indels) Copy numbers variants (CNVs)37,50 Repeat expansions39,40,51,52 Structural variants (SVs)38,48 Mitochondria37 Paralogs41 Sanger Targeted NGS PCR FISH Karyotype CMA WES WGS Capable Limited capabilities In the case of rare genetic disease research, many disease-causing variants identified with WGS would have been missed by WES, including those caused by repeat expansions or mutations in noncoding regions.2,3,39,40,47,48,51–53 Because WGS provides better coverage and higher yield across the genome, including GC-rich regions, the European Society for Human Genetics (ESHG) guidelines recommend use of WGS, even if only the exome or specific genes are examined bioinformatically.28 UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 10 | M-GL-01796 v1.0 TABLE OF CONTENTS MULTIOMIC STUDIES BIGGER STUDIES Value of whole transcriptomes Even whole genomes don’t tell the whole story. Certain variants, like gene fusions, may not be readily apparent in the DNA sequence and can be overlooked by WGS.53 RNA sequencing (RNA-Seq) enables scientists to identify and confirm the presence of novel gene fusions and alternate transcript isoforms. Because RNA is dynamic between cell types and states, RNA-Seq also offers critical information about biological activity. Whole-transcriptome sequencing (WTS), or total RNA-Seq, delivers a high-resolution, base-by-base view of coding and multiple forms of noncoding RNA activity. This provides a comprehensive picture of gene expression across the full transcriptome at a specific moment in time. With total RNA-Seq, the whole transcriptome—including both known and unknown regions—is captured.54–56 The increased affordability of high-throughput NGS, paired with push-button analysis tools to decipher richer data sets, make WTS accessible for more routine use. RNA-Seq for cancer research In cancer research, RNA-Seq is a critical tool for direct measurement of the functional consequences of mutations. Despite the average cancer containing about 46 mutations, only five to eight are necessary for initiation.57 Genomic profiling alone is insufficient to differentiate these driver mutations from passenger mutations, or those mutations that do not influence cancer initiation or progression. Measurement of gene expression patterns and mutation consequences using RNA-Seq enables large-scale, unbiased differentiation of factors crucial for cancer progression, resulting in more thorough and accurate cancer modeling.58–62 Growing evidence demonstrates the value of combining wholegenome and transcriptome sequencing (WGTS) to collect broader information from cancer samples.63 RNA-Seq for genetic disease research RNA-Seq offers a complementary approach to GWAS for genetic disease research that increases diagnostic yield.64–66 Measuring expression abundance in specific tissues can reveal the functional impact of pathogenic mutations and identify which genes mediate the genotype’s effect on phenotype.64–67 RNA-Seq can also validate computational predictions of splicing and increase confidence in the reclassification of VUS. Many studies have shown that GWAS risk variants co-localize with genes that regulate expression.67,68 These genes, known as expression quantitative trait loci (eQTLs), suggest that regulation is an important molecular mechanism used by GWAS risk variants, most of which lie in noncoding regions of the genome. Polygenic risk scores based on eQTLs are helping scientists better understand complex phenotypes.69 Learn more Whole-genome sequencing Whole-transcriptome sequencing UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 11 | M-GL-01796 v1.0 TABLE OF CONTENTS HIGH-RESOLUTION STUDIES BROADER STUDIES Multiple layers of information connect genotype to phenotype Combining DNA, epigenetics, RNA, protein, or other molecular measurements into a full cellular readout provides researchers with novel scientific insights that cannot be found from single omic methods alone. Multiomic studies for a wider perspective Multidimensional insights with sequencing provide a single readout from multiple omes across DNA, RNA, and protein Biology is multilayered and complex. The central dogma outlines the intertwined relationship between DNA, RNA, and protein. Genetic variation at the DNA level can impact RNA expression or protein function in diverse and unpredictable ways. Environmental factors can also alter regulatory pathways and cellular metabolism to affect biology and human health. Multiomics provides a perspective to power discovery across multiple levels of biology. This biological analysis approach combines genomic data with data from other modalities, measuring gene expression, gene activation, and protein levels to enable a more comprehensive understanding of molecular changes contributing to normal development, cellular response, and disease. Bigger picture biology through multiomics Multiomics goes beyond the genome to unlock deeper biological insights. Using every piece of molecular data available can accelerate biological discoveries and transform our understanding of human health. Through the combined lens of multiomics, researchers can witness the complicated interplay between the molecules of life. Integrating these complementary metrics into multiomic data sets brings a more comprehensive picture of cellular phenotypes and helps pull more high-quality information from each sample. The transformational power in the latest sequencing systems makes it easier to query multiple omes in parallel. UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 12 | M-GL-01796 v1.0 TABLE OF CONTENTS HIGH-RESOLUTION STUDIES BROADER STUDIES Common multiomic combinations Genomics + transcriptomes GWAS have successfully identified genetic variants associated with complex diseases. The genotype offers information on susceptibility to the disease; however, determining the specific genes and pathways affected by those variants is more difficult. Incorporating RNA-Seq can help researchers annotate and prioritize variants uncovered in GWAS for functional analysis to understand mechanisms of disease. Gene expression analysis informs if and when the genes of interest are down- or upregulated in the disease samples. This multiomic approach to functional genomics can help power drug target identification and biomarker discovery. Genomics + epigenetics The majority of human variation identified by GWAS is in the noncoding regions of the genome, including introns, promoters, or enhancers. Comprehensive epigenetic profiling can reveal patterns of gene regulation to help find the function of those variants. NGS-based epigenetic techniques include chromatin immunoprecipitation (ChIP-Seq), assay for transposase-accessible chromatin (ATAC-Seq), and chromosome conformation capture (HiC). DNA methylation patterns are also conserved and can represent a new class of biomarkers. Multiomic approaches that combine methylation, or other epigenetic profiling, with genetic information can connect functional layers to decipher complex pathways and disease mechanisms. Epigenetics + transcriptomics Epigenetics and transcriptomics offer complementary information to study the details of cellular differentiation and response. Combining epigenetic and RNA-Seq methods allows researchers to directly measure the ties between gene regulation and gene expression, instead of simply inferring those connections. Integrating epigenetics and RNA-Seq can help researchers identify candidate genes and understand the mechanisms controlling interesting phenotypes. This holistic, nonbiased multiomics approach can uncover new regulatory elements for biomarkers and therapeutic targets. Transcriptomics + proteomics RNA-Seq offers unparalleled discovery power to interrogate the transcriptome without prior knowledge. Incorporating protein detection with RNA-Seq can tie new discoveries back to known canonical markers and historical clinical outcomes. Antibodies tagged with oligonucleotide barcodes enable analysis of cell surface proteins with results read by sequencing, which scales to a much higher number of parameters than flow cytometry or mass cytometry. Single-stranded nucleotide aptamers with selective protein binding targets can be used in a similar way. Methods like cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq) combine single-cell RNA-Seq with cell surface protein analysis. Bulk epitope and nucleic acid sequencing (BEN-Seq) is performed at the bulk level. Spatial transcriptomics interrogates RNA and proteins in context with tissue morphology. Genomics + proteomics This multiomic approach directly connects genotype to phenotype for more informed research on disease and therapeutics development. Linking genetic variation to protein expression at the single-cell level can reveal the functional impact of somatic mutations on human cancers to better understand tumor evolution and disease progression. UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 13 | M-GL-01796 v1.0 TABLE OF CONTENTS HIGH-RESOLUTION STUDIES BROADER STUDIES Next-level multiomics The multiomic methods highlighted here represent a holistic approach to understanding biology. Additional discovery power comes from studies that measure three or more modalities.70,71 Combining WGS, RNA-Seq, and methylation sequencing is improving diagnostic yield for neurodevelopmental disorders and other rare diseases.70,72 Multiomic studies have also focused on complex diseases. For example, integrated genome, epigenome, and transcriptome data from populationscale data sets helped identify genes and biological mechanisms associated with blood pressure regulation.71 As the cost of sequencing continues to decrease and the technology advances, multiomic assays will become more comprehensive and better integrated. Labs will be able to study more samples under different conditions to reveal the dynamic properties of cells and systems. Sequencing will support proteomic studies with oligotagged antibodies or aptamers for hundreds to thousands of markers. More assays will incorporate multimodal measurements at higher resolution. One of the biggest challenges for multiomic research is how to integrate different molecular data sets in a standardized way. Researchers need robust computational strategies to extract biologically meaningful insights from these vast amounts of data. Sophisticated bioinformatics tools enable normalization and integration of multimodal single-cell sequencing experiments. Machine learning methods will also help organize and filter complex multiomic data. Learn more Multiomics CITE-Seq UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 14 | M-GL-01796 v1.0 TABLE OF CONTENTS DEEPER STUDIES MULTIOMIC STUDIES Higher analytical resolution to decode complex systems Transition to high-resolution approaches, including single-cell sequencing and spatial analysis, for insights into complex tissues High-resolution methods like single-cell sequencing and spatial analysis offer another layer of fundamental detail to examine heterogeneity in complex cell populations.73 These approaches have enabled researchers to look at cancer, development, and infectious disease at the single-cell level within tissue context.74–82 Studies have combined single-cell and spatial RNA-Seq to map the architecture of skin cancer,83 characterize human intestinal development,84 and track COVID-19 pathology.74,75 Single-cell sequencing Single-cell sequencing is a popular approach used to characterize hundreds to millions of individual cells from a tissue. This method reveals cellular heterogeneity and provides a more comprehensive understanding of tissue composition. Significant advances in the area of single-cell characterization include technologies for cell isolation and new methods and applications for single-cell sequencing. These advances have stimulated the launch of accessible commercial solutions for every step of the single-cell sequencing workflow, from tissue preparation through data analysis. Single-cell RNA sequencing (scRNA-Seq) has become a powerful tool in immunology, cancer, and developmental biology studies. As part of the Human Cell Atlas, a large-scale effort to map human development, researchers used single-cell combinatorial indexing to profile the transcriptomes of ~2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation in a single experiment.85 Cell atlas studies often have implications in genetic disease research. For cystic fibrosis, scRNA-Seq of human bronchial epithelial cells helped uncover a rare cell type, pulmonary ionocytes, that accounts for the majority of CFTR expression in the lungs.86 Cancer researchers use scRNA-Seq to better understand cancer biology, as traditional bulk RNA-Seq does not address the heterogeneity within and between tumors. scRNA-Seq has aided the development of targeted therapy and immunotherapy treatments. Researchers often use scRNA-Seq in conjunction with cell-surface protein expression and immune repertoire sequencing to characterize the inflammatory response. UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 15 | M-GL-01796 v1.0 TABLE OF CONTENTS DEEPER STUDIES MULTIOMIC STUDIES Spatial sequencing Typical NGS methods using dissociated samples can lose key spatial information present in vivo. Traditionally, immunohistochemistry and in situ hybridization have been the tools of choice to reveal spatial gene expression in tissue sections. But the throughput of these procedures is limited, analyzing only a few genes at a time. By combining high-throughput imaging and sequencing technologies, spatial RNA-Seq provides a previously inaccessible view of the full transcriptome in morphological context. Spatial RNA-Seq methods that retain the precise location of biological molecules in tissue samples can further our understanding of mechanisms in health and disease. Pushing the boundaries As single-cell and spatial technologies advance, researchers are increasing the scale, scope, and dimensions of their experiments. One consortium, the BRAIN Initiative Cell Census Network, coordinated large-scale multiomic analyses from multiple labs of the mammalian primary motor cortex with single-cell and spatial resolution.87 Cross-modal computational analysis integrated data with single-cell transcriptomes, chromatin accessibility, DNA methylation, and phenotypic properties, across species, to establish a framework for neuron organization.87 A team of researchers at the Howard Hughes Medical Institute performed genome-scale Perturb-Seq (a CRISPR-based functional genomics screen with scRNASeq readouts) across 2.5 million human cells.88 From the single-cell transcriptional phenotypes, the team was able to dissect complex cellular pathways and develop a map of gene and cellular function.88 The researchers note ways to increase the power of future studies, including greater numbers of cells or sequencing depth, full-length RNA-Seq or protein-level readouts, and sampling a wider range of time points or cell types.88 Learn more Single-cell RNA sequencing Spatial transcriptomics UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 16 | M-GL-01796 v1.0 TABLE OF CONTENTS NOVASEQ X SERIES HIGH-RESOLUTION STUDIES Deeper studies to find rare events NGS assays can be designed to either target a large number of genes with low sequencing depth (more comprehensive, less sensitive) or a relatively small number of genes with higher sequencing depth (less comprehensive, more sensitive). In cases of heterogenous samples such as blood or tumors, high sequencing depth is necessary to provide the sensitivity required to detect low-abundance variants accurately. Scientists can take advantage of the latest advancements in high-throughput sequencing to perform deeper sequencing experiments, more rapidly, at production-scale. Liquid biopsy Liquid biopsy detects and characterizes various tumorderived biomarkers present in the blood of an individual with cancer. These biomarkers are absent in healthy individuals and patients who are cancer free. Liquid biopsy is a noninvasive approach that offers a potential alternative to invasive tissue biopsies to detect targetable oncogenic drivers and resistance mutations.89 Tumors release circulating tumor DNA (ctDNA) into the bloodstream through various cellular mechanisms, including apoptosis, necrosis, phagocytosis, and active secretion.90 Even so, ctDNA represents a small fraction of total cell-free DNA (cfDNA) in the blood,91 the majority of which is released by erythrocytes, leukocytes, and endothelial cells.90,92 The rarity of ctDNA combined with the fact that different cancer types at different stages shed ctDNA at different rates complicates analysis. However, recent improvements in NGS instrumentation provide options for sequencing samples at extremely high depth of coverage for large portions of the genome in a single sample. This enables analysis of hundreds of genes with the sequencing depth required for ctDNA analysis. Furthermore, these technologies enable hypothesis-free interrogation of biofluid analytes, providing discovery power to assay genes and pathways that were not considered prior to experimental design. Liquid biopsy combined with comprehensive genomic profiling (CGP) can identify tumor-specific mutations in patient samples93 and provide information of cancer type, stage, and vascularization.94–97 Recent studies that performed liquid biopsy paired with corresponding tissue biopsy from tumor samples have demonstrated that, when comprehensive assays are used, ctDNA analysis detected a significant number of guideline-recommended biomarkers and resistance alterations not found in matched tissue biopsies.98,99 Furthermore, ctDNA sequencing identifies variants in the tumor from which it originates, including both driver and passenger mutations, respectively.90 Given the rapid turnover of cfDNA and ctDNA in the bloodstream, liquid biopsy can be a powerful tool for cancer research to assay tumor burden in real time and monitor response to therapy.100 Studies also demonstrate how liquid biopsybased detection of molecular residual disease (MRD) can predict recurrence with high sensitivity and specificity, with lead times that precede standard imaging.101–103 UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 17 | M-GL-01796 v1.0 TABLE OF CONTENTS NOVASEQ X SERIES HIGH-RESOLUTION STUDIES Next-level liquid biopsy Circulating analytes include more than just ctDNA and can apply to more than just cancer. As technology continues to advance, there is the potential to apply multiomic approaches that integrate analyses of the genome, transcriptome, epigenome, proteome, and microbiome to liquid biopsy.104 For example, cell-free RNA (cfRNA) is indicative of earlystage cancer and other conditions like preeclampsia.105–111 Methylation patterns in ctDNA can also recapitulate the abnormal methylation patterns that are a hallmark of many cancers.112–116 The ability to combine multiomics with liquid biopsy will provide unique discovery power for deeper insights and comprehensive answers to the mechanisms of cancer. Genome-wide tumor– normal sequencing Deep sequencing of tumor exomes or genomes can provide rapid and accurate CGP.117,118 Through tumor–normal WGS, researchers can compare tumor mutations to a matched normal sample. Tumor–normal comparisons are crucial for identifying the somatic variants that act as driver mutations in cancer progression. Tumor–normal sequencing typically requires a minimum of 80× sequencing depth. WGS for cancer studies offers base-pair resolution of the unique mutations present in a tumor. Cancer genomes typically contain unpredictable numbers of point mutations, gene fusions, and other aberrations. As a hypothesis-free approach, cancer WGS offers unbiased insights into a variety of genomic aberrations, including emerging biomarkers.119,120 It enables discovery of novel cancer-associated variants, including single nucleotide variants (SNVs), copy number changes, indels, and structural variants. Learn more Circulating tumor DNA sequencing Comprehensive genomic profiling Cancer whole-genome sequencing We've known for many years that there is tumorrelated material in the bloodstream, we just didn't have the technologies to detect it for it to be meaningful. Minetta C. Liu, MD, Mayo Clinic ” “ UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 18 | M-GL-01796 v1.0 TABLE OF CONTENTS DEEPER WORKFLOWS STUDIES How the NovaSeq X Series accelerates genomic discovery The potential of broader, deeper sequencing can now be realized, thanks to the latest advancements in Illumina NGS systems. The NovaSeq X and NovaSeq X Plus Sequencing Systems are built with breakthrough technological innovations to transform the economics of high-throughput sequencing. The NovaSeq X Series is powered by XLEAP-SBS™ chemistry—a faster, higher fidelity, and more robust version of proven Illumina sequencing by synthesis (SBS) chemistry. XLEAP-SBS reagents are optimized for performance and speed, to maximize throughput without sacrificing data quality. Ultrahigh-resolution optics were developed to match the capabilities of the chemistry. Ultrahigh-density patterned flow cells with tens of billions of nanowells enable up to 16 Tb output (or up to 52 billion single reads) per dual flow cell run on the NovaSeq X Plus System. In addition to advancements in chemistry and optics, the NovaSeq X Series is built with DRAGEN™ hardware onboard the instrument to accelerate and streamline secondary analysis and compress data by 80% without loss. The NovaSeq X Series also sets a new standard in operational simplicity with a software ecosystem built specifically to support the Illumina NGS workflow. NovaSeq X Series specification sheet High-accuracy NGS data with the NovaSeq X Series application note UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 19 | M-GL-01796 v1.0 TABLE OF CONTENTS DEEPER WORKFLOWS STUDIES Enabling broader, deeper sequencing The NovaSeq X Series will allow scientists to perform broader, more ambitious studies at a new level of scale. Researchers can increase the scope of their studies without increasing their budgets. Lower costs and streamlined informatics enable more analyses per sample and more data per analysis. With the ability to sequence three-fold more samples, population genomics researchers can add statistical power with larger cohorts—up to tens of thousands of genomes per year. Cancer or genetic disease researchers can sequence three-fold deeper to detect low-frequency signals and rare variants. Other researchers can easily adopt multiomic interrogation techniques with more omes and multiple simultaneous analyses. The comprehensive, high-resolution view of biological systems will expand the discovery power of genomic scientists. 25B 350 300 450 400 250 200 150 100 50 0 10B 1.5B S4 S2 S1 SP NextSeqTM 2000 P3 EXAMPLE PROJECT Single-cell gene expression study with fixed $50K reagent budget‡ ~2× NovaSeq X Series NovaSeq 6000 System Single-cell samples per $50K reagent budget ‡ Analysis based on 20,000 reads per cell and 10,000 cells per sample. All pricing is in USD, based on United States list prices. UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 20 | M-GL-01796 v1.0 TABLE OF CONTENTS DEEPER WORKFLOWS STUDIES The NovaSeq X Series more than doubles the throughput of the NovaSeq 6000 System, while taking up the same lab footprint—less than one square meter of floor space. NovaSeq X and NovaSeq X Plus Systems provide massive throughput and productivity gains with the ability to sequence ~2.5 genomes/hour and up to 128 genomes (30× coverage) per run and 96 human genomes (40× coverage) per dual 25B flow cell run. The NovaSeq X Series decreases cost/Gb by up to 50% and shrinks the cost gap between WGS and WES by 2.5× to a difference of only $183 (USD). ~2.5× ~2× Throughput Faster NovaSeq 6000 System NovaSeq X Series 65 Gb–6 Tb Output 165 Gb–16 Tb 800M–20B Read number 1.68B–52B ~1 genome/hr Genomes per hour ~2.5 genomes/hr 2–48 genomes Genomes per run 4–128 genomes WGS WES SHRINKING THE GAP WGS and WES Sequencing cost Library prep cost $183 Setting a new bar in every sequencing metric UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 21 | M-GL-01796 v1.0 TABLE OF CONTENTS DEEPER WORKFLOWS STUDIES Managing big experiments and big data with the NovaSeq X Series Day-to-day operational simplicity Illumina believes that genomics should be available to the many, not the few. We have an obligation to make our technology as affordable and accessible as possible while setting the highest standard for data quality and security. To democratize access to the power of the genome, the NovaSeq X Series was designed to be especially easy to operate, from run planning through analysis. The simple workflow requires fewer touchpoints and fewer steps than the NovaSeq 6000 System, reducing the learning curve and the chance of user error. Expanded global access to NGS The stability of XLEAP-SBS reagents allows for ambient temperature shipping, without ice packs or dry ice. Eliminating the need for cold-chain transport expands access to NGS globally, especially for hardto- reach geographies. This key innovation will speed adoption of high-throughput sequencing for more diverse populations. Flexible scalability The choice of three flow cell types with individually addressable lanes offers flexible scalability. The NovaSeq X 10B and 25B flow cells enable larger projects and deeper sequencing. The NovaSeq X 1.5B flow cell eases the transition from benchtop to higher-throughput sequencing. The fast turnaround times for the 1.5B flow cell are ideal for small batch sizes and data-lite applications. UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 22 | M-GL-01796 v1.0 TABLE OF CONTENTS Adjustable screen height with position memory 4K ultra high-definition touch screen System prompts to guide user along workflow Easy-to-handle flow cell with illuminated flow cell stage Push-to-access hidden keyboard Light-guided consumable loading Assisted open doors and waist-height reagent drawers Less time and effort with established workflow applications Reduced chance of error with intuitive user interface with clear visualization of run status and lane-level metrics, and immediate view of secondary analysis reports and QC metrics > 90% reduction in reagent packaging waste/weight and 50% reduction in plastic for easier handling and reduced disposal costs§ Designed with the user in mind DEEPER WORKFLOWS STUDIES § In comparison to the NovaSeq 6000 System. UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 23 | M-GL-01796 v1.0 TABLE OF CONTENTS DEEPER WORKFLOWS STUDIES Integrated, cohesive bioinformatics ecosystem The NovaSeq X Series streamlines genomic data management with onboard secondary analysis and integration with a comprehensive suite of bioinformatics solutions. The Illumina Connected Software suite includes some of the fastest, most accurate,94 and advanced solutions for data analysis, interpretation, and aggregation. With flexible local and cloud-based options for lab operations, run planning, and data analysis, the NovaSeq X Series enables users to run high-throughput sequencing without creating a bioinformatics bottleneck. Plug-and-play onboard DRAGEN workflows enable variant calling directly on the system. Up to four DRAGEN applications can run in parallel, speeding multiomic analysis. Integrated DRAGEN ORA (original read archive) data compression reduces data storage needs five-fold. The savings on server, licenses, and cloud storage can total more than $1 million (USD) over five years of ownership.** Access to a broader menu via the cloud with highly configurable pipelines will accelerate larger, more data-intensive sequencing projects. ** Assuming FASTQ files are archived 30 days after cloud upload and throughput of 10,000 WGS/year for the NovaSeq X System and 20,000 WGS/year for the NovaSeq X Plus System. “ “ The physical reduction in kit size has greatly decreased our cold storage needs in lab and elimination of dry ice shipping has made unpacking larger orders much easier. One of the big benefits I can see in the new DRAGEN onboard analysis on the NovaSeq X Plus System is the considerable reduction in file size now being compressed to approximately one-fifth of what would be a traditional file, which allows us to run more projects at population scale. Eric Chow, Assistant Adjunct Professor and Director, University of California San Francisco (UCSF) Center for Advanced Technology Lachlan Morrison, Senior Technical Officer Biomolecular Resource Facility (BRF), Australian National University (ANU) ” ” NovaSeq X Series software ecosystem technical note UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 24 | M-GL-01796 v1.0 TABLE OF CONTENTS NOVASEQ X CONCLUSION SERIES Example NovaSeq X Series workflows Illumina DNA PCR-Free Prep 25–300 ng DNA input ~1.5-hour workflow DRAGEN Germline (onboard or in the cloud) NovaSeq X 1.5B flow cell ~4 samples per flow cell NovaSeq X 10B flow cell ~24 samples per flow cell NovaSeq X 25B flow cell ~64 samples per flow cell 400M reads per sample 2 × 150 bp read length Prepare libaries Sequence Analyze data WGS Illumina Complete Long Read Prep, Human 50 ng DNA input ~1-day workflow Illumina Complete Long Read WGS App (in the cloud) NovaSeq X 10B flow cell 4 samples per flow cell NovaSeq X 25B flow cell 10–11 samples per flow cell 750 Gb per sample 2 × 150 bp read length Illumina DNA Prep with Exome 2.0 Plus Enrichment 50–1000 ng DNA input ~6.5-hour workflow DRAGEN Enrichment (onboard or in the cloud) NovaSeq X 1.5B flow cell ~41 samples per flow cell NovaSeq X 10B flow cell ~250 samples per flow cell NovaSeq X 25B flow cell ~750 samples per flow cell 8 Gb per sample 2 × 100 bp read length Prepare libaries Sequence Analyze data WES UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 25 | M-GL-01796 v1.0 TABLE OF CONTENTS NOVASEQ X CONCLUSION SERIES Illumina Stranded Total RNA Prep 1–1000 ng high-quality DNA input ~7-hour workflow DRAGEN RNA (onboard or in the cloud) NovaSeq X 1.5B flow cell ~32 samples per flow cell NovaSeq X 10B flow cell ~200 samples per flow cell NovaSeq X 25B flow cell ~520 samples per flow cell 50M reads per sample 2 × 100 bp read length Prepare libaries Sequence Analyze data Illumina Stranded mRNA Prep 25–1000 ng high-quality RNA input ~7-hour workflow DRAGEN RNA (onboard or in the cloud) NovaSeq X 1.5B flow cell ~64 samples per flow cell NovaSeq X 10B flow cell ~400 samples per flow cell NovaSeq X 25B flow cell ~1040 samples per flow cell 25M reads per sample 2 × 100 bp read length Illumina RNA Prep with Enrichment 20 ng FFPE RNA input ~9-hour workflow DRAGEN RNA (onboard or in the cloud) WTS NovaSeq X 1.5B flow cell ~64 samples per flow cell NovaSeq X 10B flow cell ~400 samples per flow cell NovaSeq X 25B flow cell ~1040 samples per flow cell 25M reads per sample 2 × 100 bp read length UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 26 | M-GL-01796 v1.0 TABLE OF CONTENTS WORKFLOWS ABBREVIATIONS Unimaginable experiments, made possible The NovaSeq X Series is driven by your vision to help moonshot projects become a reality. Increase statistical power with broader study designs and larger sample cohorts. Maximize read numbers and increase sequencing depth for a higher resolution view that can detect low-frequency signals and variants. Answer the most complex questions in human genomics, with larger sample cohorts, deeper sequencing, and more data-intensive methods—from whole-genome sequencing to multiomics. Genomics visionaries are ready to usher in the Genome Era, and the NovaSeq X Series can help make it happen. “[The NovaSeq X Series] now allows us to provide much larger scale projects for both human health and nonhuman scenarios in the research world, and to be able to translate that into more meaningful outcomes. Joe Baini, CEO, Australian Genome Research Facility (AGRF) ” NovaSeq X and NovaSeq X Plus Sequencing Systems NovaSeq X Series virtual tour NovaSeq X Series overview video UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 27 | M-GL-01796 v1.0 TABLE OF CONTENTS CONCLUSION REFERENCES Abbreviations ATAC-Seq: assay for transposase-accessible chromatin BEN-Seq: bulk epitope and nucleic acid sequencing CGP: comprehensive genomic profiling cfDNA: cell-free DNA ChIP-Seq: chromatin immunoprecipitation sequencing CITE-Seq: cellular indexing of transcriptomes and epitopes by sequencing CMA: chromosomal microarray CNV: copy number variant CRISPR: clustered regularly interspaced short palindromic repeats ctDNA: circulating tumor DNA eQTL: expression quantitative trait loci FFPE: formalin-fixed, paraffin-embedded Gb: gigabase GWAS: genome-wide association studies HiC: chromosome conformation capture Indel: insertion–deletion MRD: molecular residual disease NGS: next-generation sequencing NICU: neonatal intensive care unit RNA-Seq: RNA sequencing SBS: sequencing by synthesis scRNA-Seq: single-cell RNA sequencing SNV: single nucleotide variant SV: structural variant Tb: terabase VUS: variant of unknown significance WES: whole-exome sequencing WGS: whole-genome sequencing WGTS: combined whole-genome and transcriptome sequencing WTS: whole-transcriptome sequencing UNLOCK THE NEXT WAVE OF GENOMIC DISCOVERY 28 | M-GL-01796 v1.0 TABLE OF CONTENTS ABBREVIATIONS References 1. 100,000 Genomes Project. 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