The field of genetic testing has evolved dramatically over the past few decades. From labor-intensive manual methods to today’s fast, accurate, and cost-effective solutions. Among these, Next-Generation Sequencing (NGS) has emerged as the cornerstone of modern genomic diagnostics and research. But how did we get here?
From Sanger to NGS: A Technological Revolution in Genomics
Sanger Sequencing
1977
Reading one sentence at a time with a magnifying glass
- Developed by Nobel Laureate Frederick Sanger, this method became the gold standard for DNA sequencing
- It reads DNA by synthesizing fragments that terminate at specific nucleotides, allowing scientists to piece together sequences with the help of fluorescent markers
Sanger sequencing played a key role in the Human Genome Project, laying the groundwork for future breakthroughs
Low throughput, time-consuming and expensive, sequencing a full human genome took years and billions of dollars
Quantitative Polymerase Chain Reaction(qPCR)
1990s
Searching for a specific word with a highlighter
- Unlike sequencing, qPCR focuses on quantifying DNA or RNA in real time using fluorescent dyes
- It is widely used in diagnostic labs for detecting viruses like COVID-19 and HIV, and in cancer research to study gene expression patterns
Gene expression studies, pathogen detection
Limited to targeted regions
Microarrays
Late 1990s
Scanning an entire book for predefined keywords
- DNA microarrays contain thousands of known DNA probes on a chip
- When exposed to a sample, matching sequences bind and are detected using fluorescence
- This technique revolutionized genomics by enabling simultaneous detection of thousands of genetic variants
Enabled scientists to scan for thousands of genetic variants at once, revolutionizing genetic association studies and diagnostics for large chromosomal changes
Cannot detect small mutations or novel variants
Next-Generation Sequencing (NGS)
2000s-Present
Digitizing the entire book and running a spell-checker
- NGS technology revolutionized genetic testing by enabling massively parallel sequencing of millions of DNA fragments in a single run
- With advanced computing, NGS platforms can accurately reconstruct entire genomes quickly and affordably
Made it possible to sequence entire genome in days for a fraction of the cost. It is the engine behind breakthroughs in personalized medicine, cancer genomics, rare disease diagnosis, and even tracking infectious outbreaks
High-throughput sequencing of billions of DNA bases
Reduced long-run costs, increased speed
Comparing the Technologies: Throughput, Speed, and Detail
| Technology | Main Use | Typical Output/Run | Time to Sequence a Human Genome |
| Sanger | Small-scale targeted sequencing | ~1,000 bases | Years |
| Microarray | Screening known variants | 100,000s of data points | N/A |
| qPCR | Quantify DNA/RNA, not sequencing | 1-100 genes | Minutes-hours |
| NGS | Comprehensive genome analysis | Billions of bases | 1-2 days |
Applications Across Clinical and Research Fields
- Sanger Sequencing: The Gold Standard for Targeted Analysis
-
- Confirmatory testing for BRCA1/2 and other hereditary cancer genes
- Verifying plasmid constructs in synthetic biology
- Species identification in phylogenetics
- qPCR: Speed and Quantification in Real Time
-
- Rapid pathogen detection (e.g., COVID-19, Influenza)
- Gene expression profiling in oncology
- Microbial load monitoring in environmental and biotherapeutic studies
- Microarrays: High-Throughput Snapshot Analysis
-
- CNV detection in developmental disorders
- SNP mapping in chronic disease studies (e.g., diabetes)
- Gene expression profiling for toxicology research
- NGS: Unlocking Genomic Complexity
-
- Whole-genome sequencing (WGS) and whole-exome sequencing (WES)
- Liquid biopsy for cancer mutation tracking
- Metagenomic testing for pathogen discovery
- Single-cell sequencing and microbiome analysis
The journey from Sanger Sequencing to NGS marks a monumental shift in how we understand and leverage the human genome. Today, NGS enables precision diagnostics, drives innovation in personalized medicine, and opens the door to insights previously unimaginable.
For clinicians, researchers, and healthcare providers, adopting NGS technology means faster, deeper, and more actionable genomic insights; improving patient care, accelerating discovery, and supporting public health efforts globally.
The Push Toward Comprehensive Technologies
As genomics advances, so do the expectations. Modern medicine is no longer satisfied with just identifying a handful of mutations; it demands depth, breadth, and integration in genetic analysis.
What Does Modern Genomics Require?
- Depth
The ability to detect rare and subtle variants that may be missed by traditional methods - Breadth
Analysis of the entire genome, not just the coding regions, to understand the full picture of the disease - Integration
Combining genomic, transcriptomic, and epigenetic data to uncover complex interactions driving disease mechanisms
Why Next-Generation Sequencing (NGS) is a Game-Changer?
NGS is driving a paradigm shift in genetic testing and precision diagnostics because it addresses these needs at scale
Unprecedented Scale
NGS can sequence entire genomes, exomes, and transcriptomes in a single experiment—ideal for comprehensive analysis
Speed
What used to take years now takes just days, enabling faster clinical decisions and shortened diagnostic odysseys
Cost Efficiency
The cost of genome sequencing has dropped from billions to just thousands of dollars, democratizing access to advanced diagnostics
Breadth
NGS captures a wide array of genetic variations in a single test:
- SNVs (single-nucleotide variants)
- INDELs (insertions and deletions)
- CNVs (copy number variants)
- Structural rearrangements
- Regulatory region mutations
Genomics 101
Genomics decodes the structure, function, and variation within genomes, providing insights into health, disease and evolution
Coding vs. Non-Coding
Coding DNA (1-2% of the genome)
- Encodes proteins via exons
- Mutations can directly impact amino acid sequences and protein function
- Sickle cell anemia, caused by a single base change in the β-globin genes
Non-Coding DNA (98-99% of the genome)
- Regulates gene expression, splicing, and chromatin structure
- Contains introns, promoters, enhancers, and non-coding RNAs
- Mutations here can disrupt regulatory networks, even when the coding sequence appears normal
Types of Genetic Variants
Single Nucleotide Variants (SNVs)
A typo in a sentence
(e.g., “cat” → “car”)
- Single-base substitutions (e.g., A → T)
- Synonymous
- No amino acid change (e.g., CGA → CGG, both code for arginine)
- Non-synonymous
- Alters amino acids (e.g., GAG → GTG changes glutamic acid to valine, causing sickle cell anemia)
- Stop-gain
- Creates a premature stop codon, truncating proteins (e.g., TP53 variants in cancer)
Insertions/ Deletions (INDELs)
Missing/extra words in a paragraph
(e.g., “The cat sat” → “The cat at”)
- Insertions or deletions of 1–50 bases
- Frameshifts
- If not multiples of 3 (e.g., a 4-bp deletion in BRCA1 disrupts breast cancer suppression)
Copy Number Variants (CNVs)
Duplicated or missing paragraphs in a book
(e.g., CCL3L1 CNVs influence HIV susceptibility)
- Duplications or deletions of DNA segments (>50 bp)
- Deletions
- SMN1 deletions → spinal muscular atrophy
- Duplications
- APP duplications → increased risk for Alzheimer’s
Why Missing Variants Can Be Devastating?
If genetic testing is limited to only coding regions or lacks resolution, critical variants can go undetected, resulting in:
Missed Diagnoses and Delayed Care
- Diseases caused by regulatory mutations or structural variants may be overlooked
- Patients with rare or complex conditions may remain undiagnosed for years
Inaccurate Treatment Decisions
- Genomic variants impact drug metabolism and treatment response
- Example: Missing a BRCA mutation could mean skipping life-saving preventive surgery or targeted PARP inhibitor therapy
Gaps in Preventive and Personalized Care
- Without full variant analysis, individuals at high genetic risk may miss out on:
- Early cancer screening
- Tailored lifestyle or drug interventions
- Preventative options like prophylactic surgeries
Family Planning Implications
- Inherited conditions can go undetected in carriers
- Missed variants can lead to uninformed reproductive decisions
- Couples may face unexpected genetic conditions in their children
Using the right lens—deep, broad, and integrative—makes all the difference
Comprehensive technologies like NGS empower clinicians and researchers to:
- Detect all relevant genetic variants
- Interpret results within a holistic genomic context
- Deliver precise, personalized care
- Reduce the diagnostic odyssey for families affected by rare or complex diseases
Today’s Genetic Testing Landscape
Most traditional genetic tests rely on microarray technology, which scans predefined regions of the genome for common, known changes. While useful for detecting large-scale variants and familiar SNPs, microarrays fall short when it comes to:
- Rare or novel mutations
- Structural variations
- Clinically relevant non-coding regions
That’s where Blueprint of Life™ stands apart—powered by next-generation sequencing (NGS), this test delivers a deeper, broader, and more dynamic analysis of your DNA
Microarrays vs. NGS: The Paper Map vs. The GPS
Microarrays: The Paper Map
- Limited Resolution
- Pre-selected markers mean you only see major genomic “landmarks”, missing many important variations
- Static and Outdated
- Microarrays can’t detect new or unexpected mutations outside their fixed targets
- Blind Spots
- Struggles with complex genomic regions like PMS2, often requiring extra confirmatory tests like PCR or MLPA
Blueprint of Life™ NGS: The GPS
- Comprehensive Navigation
- Uses Cell3™ Target: Nexome to cover both coding and clinically relevant non-coding regions, capturing 30% more variants than standard panels
- Real-Time Updates
- Simultaneously detects SNVs, INDELs, and CNVs; reducing turnaround time and cost
- Dynamic Precision
- Identifies both subtle (e.g., single-nucleotide BRCA1 changes) and large-scale (e.g., SMN1 deletions) mutations in a single test
What Makes Blueprint of Life™ Different?
While most tests look at isolated genomic “chapters”, Blueprint of Life™ reads the entire story—connecting non-coding regions, rare mutations, and drug-response genes to actionable health insights
Beyond Chapter Skimming
- Expanded Exome Coverage
- Targets 51 Mb, including hard-to-capture non-coding regions tied to diseases like epilepsy and markers like CYP2D6 for drug metabolism
- Multi-Variant Detection
- Detects SNVs, INDELs, and CNVs, all in one streamlined workflow
Precision-Driven by Design
- Optimized probes
- Delivers 94.18% on-target rates, higher than typical commercial tests (85–93%), ensuring efficiency and accuracy.
- Clinically Relevant Insights
- Covers:
- ACMG73 (actionable variants)
- ClinVar (disease-associated variants)
- CPIC (pharmacogenomics guidance)
- Covers:
Microarray vs. NGS: A Head-to-Head Comparison
Next-generation sequencing (NGS) technologies like Whole Exome Sequencing (WES) have revolutionized genetic testing, offering unprecedented insights compared to older methods like microarrays
| Features | Microarray | Advanced WES (Cell3™ Target: Nexome) |
| Coverage | Known SNPs/CNVs only | Protein-coding + clinically relevant non-coding regions |
| Accuracy | Lower for small variants | High precision for SNVs, INDELs, CNVs |
| Uniformity | Basic | Advanced probes for consistent data |
| Variant Types | SNPs, CNVs | SNVs, INDELs, CNVs—all in one |
| Diagnostic Yield | Lower | Detects 30% more variants |
| Workflow Efficiency | Separate tests for CNVs | Unified workflow |
| Cost Effectiveness | Lower upfront cost | Reduced long-term cost with higher yield |
The Future Is Now with NGS
Next-generation sequencing (NGS), especially advanced Whole Exome Sequencing (WES) with Cell3™ Target: Nexome, is redefining what’s possible in genomic diagnostics. Compared to older technologies, it offers:
✅ More variants detected
✅ Faster, more accurate diagnoses
✅ Truly personalized treatment paths
Whether you’re guiding treatment, assessing inherited risks, or planning for your family’s future, Blueprint of Life™ provides the clarity and confidence modern medicine demands.
Ready to unlock your genetic potential?
Choose Blueprint of Life™, powered by Cell3™ Target: Nexome, and step confidently into the era of precision medicine.
Your blueprint, Your future, Start today!
References
- Muzzey, D., Evans, E. A., & Lieber, C. (2015). Understanding the Basics of NGS: From Mechanism to Variant Calling. Current Genetic Medicine Reports, 3(4), 158–165. https://doi.org/10.1007/s40142-015-0076-8
- Slatko, B. E., Gardner, A. F., & Ausubel, F. M. (2018). Overview of Next‐Generation Sequencing Technologies. Current Protocols in Molecular Biology, 122(1). https://doi.org/10.1002/cpmb.59
- Lohmann, K., & Klein, C. (2014). Next Generation Sequencing and the Future of Genetic Diagnosis. Neurotherapeutics, 11(4), 699–707. https://doi.org/10.1007/s13311-014-0288-8
- Dahui, Q. (2019). Next-generation sequencing and its clinical application. Cancer Biology & Medicine, 16(1), 4–10. https://doi.org/10.20892/j.issn.2095-3941.2018.0055
- Anderson, M. W., & Schrijver, I. (2010). Next Generation DNA Sequencing and the Future of Genomic Medicine. Genes, 1(1), 38–69. https://doi.org/10.3390/genes1010038
- Behjati, S., & Tarpey, P. S. (2013). What is next generation sequencing? Archives of Disease in Childhood Education & Practice, 98(6), 236–238. https://doi.org/10.1136/archdischild-2013-304340
- Satam, H., Joshi, K., Mangrolia, U., Waghoo, S., Zaidi, G., Rawool, S., Thakare, R. P., Banday, S., Mishra, A. K., Das, G., & Malonia, S. K. (2023). Next-Generation Sequencing Technology: Current Trends and Advancements. Biology, 12(7), 997. https://doi.org/10.3390/biology12070997
- Lovatt, A. (2002). Applications of quantitative PCR in the biosafety and genetic stability assessment of biotechnology products. Reviews in Molecular Biotechnology, 82(3), 279–300. https://doi.org/10.1016/s1389-0352(01)00043-5
- Karunakaran Kaliyappan, Govindarajan, R., Jeyapradha Duraiyan, & Murugesan Palanisamy. (2012). Microarray and its applications. Journal of Pharmacy and Bioallied Sciences, 4(6), 310–310. https://doi.org/10.4103/0975-7406.100283
- Jaluria, P., Konstantopoulos, K., Betenbaugh, M., & Shiloach, J. (2007). A perspective on microarrays: current applications, pitfalls, and potential uses. Microbial Cell Factories, 6(1). https://doi.org/10.1186/1475-2859-6-4
- Bianconi, I., Aschbacher, R., & Pagani, E. (2023). Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology. Antibiotics, 12(11), 1580–1580. https://doi.org/10.3390/antibiotics12111580
- Bumgarner, R. (2013). Overview of DNA Microarrays: Types, Applications, and Their Future. Current Protocols in Molecular Biology, 101(1). https://doi.org/10.1002/0471142727.mb2201s101
- Barzon, L., Lavezzo, E., Militello, V., Stefano Toppo, & Giorgio Palù. (2011). Applications of Next-Generation Sequencing Technologies to Diagnostic Virology. International Journal of Molecular Sciences, 12(11), 7861–7884. https://doi.org/10.3390/ijms12117861
- Najm, J., Rath, M., Schröder, W., & Felbor, U. (2018). Diagnostic Single Gene Analyses Beyond Sanger. Hämostaseologie, 38(03), 158–165. https://doi.org/10.5482/hamo-17-01-0008
- Grumbt, B., Eck, S. H., Hinrichsen, T., & Kaimo Hirv. (2013). Diagnostic Applications of Next Generation Sequencing in Immunogenetics and Molecular Oncology. Transfusion Medicine and Hemotherapy, 40(3), 196–206. https://doi.org/10.1159/000351267
- Shevchenko, Y., & Bale, S. (2016). Clinical Versus Research Sequencing. Cold Spring Harbor Perspectives in Medicine, 6(11), a025809–a025809. https://doi.org/10.1101/cshperspect.a025809
- Engstrom-Melnyk, J., Rodriguez, P. L., Peraud, O., & Hein, R. C. (2015). Clinical Applications of Quantitative Real-Time PCR in Virology. Methods in Microbiology, 161–197. https://doi.org/10.1016/bs.mim.2015.04.005
- Zhong, Y., Xu, F., Wu, J., Schubert, J., & Li, M. M. (2020). Application of Next Generation Sequencing in Laboratory Medicine. Annals of Laboratory Medicine, 41(1), 25–43. https://doi.org/10.3343/alm.2021.41.1.25
- A Brief Guide to Genomics. (2019). Genome.gov. https://www.genome.gov/about-genomics/fact-sheets/A-Brief-Guide-to-Genomics
- Human Genomic Variation. (2023). Genome.gov. https://www.genome.gov/about-genomics/educational-resources/fact-sheets/human-genomic-variation
- Vitsios, D., Dhindsa, R. S., Middleton, L., Gussow, A. B., & Petrovski, S. (2021). Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-21790-4
- Lauer, S., Avecilla, G., Spealman, P., Sethia, G., Brandt, N., Levy, S. F., & Gresham, D. (2018). Single-cell copy number variant detection reveals the dynamics and diversity of adaptation. PLOS Biology, 16(12), e3000069. https://doi.org/10.1371/journal.pbio.3000069
- Bennett, E. P., Petersen, B. L., Johansen, I. E., Niu, Y., Yang, Z., Chamberlain, C. A., Özcan Met, Wandall, H. H., & Morten Frödin. (2020). INDEL detection, the “Achilles heel” of precise genome editing: a survey of methods for accurate profiling of gene editing induced indels. Nucleic Acids Research, 48(21), 11958–11981. https://doi.org/10.1093/nar/gkaa975
- Hastings, P. J., Lupski, J. R., Rosenberg, S. M., & Ira, G. (2009). Mechanisms of change in gene copy number. Nature Reviews Genetics, 10(8), 551–564. https://doi.org/10.1038/nrg2593
- Collins, F. S., Green, E. D., Guttmacher, A. E., & Guyer, M. S. (2003). A vision for the future of genomics research. Nature, 422(6934), 835–847. https://doi.org/10.1038/nature01626
- Locey, K. J., & White, E. P. (2011). Simple Structural Differences between Coding and Noncoding DNA. PLoS ONE, 6(2), e14651–e14651. https://doi.org/10.1371/journal.pone.0014651
- Poliseno, L., Lanza, M., & Pandolfi, P. P. (2024). Coding, or non-coding, that is the question. Cell Research, 34(9), 609–629. https://doi.org/10.1038/s41422-024-00975-8
- Li, J., & Liu, C. (2019). Coding or Noncoding, the Converging Concepts of RNAs. Frontiers in Genetics, 10. https://doi.org/10.3389/fgene.2019.00496
- Bianchi, A., Zelli, V., D’Angelo, A., Alessandro Di Matteo, Giulia Scoccia, Cannita, K., Dimas, A., Stavros Glentis, Zazzeroni, F., Edoardo Alesse, Antinisca Di Marco, & Tessitore, A. (2024). A method to comprehensively identify germline SNVs, INDELs and CNVs from whole exome sequencing data of BRCA1/2 negative breast cancer patients. NAR Genomics and Bioinformatics, 6(2). https://doi.org/10.1093/nargab/lqae033
- Sairam Behera, Severine Catreux, Rossi, M., Truong, S., Huang, Z., Ruehle, M., Arun Visvanath, Parnaby, G., Roddey, C., Vitor Onuchic, Finocchio, A., Cameron, D. L., English, A., Shyamal Mehtalia, Han, J., Rami Mehio, & Sedlazeck, F. J. (2024). Comprehensive genome analysis and variant detection at scale using DRAGEN. Nature Biotechnology. https://doi.org/10.1038/s41587-024-02382-1
- Lee, C.-L., Chuang, C.-K., Chiu, H.-C., Chang, Y.-H., Tu, Y.-R., Lo, Y.-T., Lin, H.-Y., & Lin, S.-P. (2025). Understanding Genetic Screening: Harnessing Health Information to Prevent Disease Risks. International Journal of Medical Sciences, 22(4), 903–919. https://doi.org/10.7150/ijms.101219
- Marian, A. J. (2020). Clinical Interpretation and Management of Genetic Variants. JACC Basic to Translational Science, 5(10), 1029–1042. https://doi.org/10.1016/j.jacbts.2020.05.013
- Shieh, J. T. C. (2023). Genomic technologies to improve variation identification in undiagnosed diseases. Pediatrics & Neonatology, 64, S18–S21. https://doi.org/10.1016/j.pedneo.2022.10.002
