{"id":19976,"date":"2026-09-30T05:51:00","date_gmt":"2026-09-30T05:51:00","guid":{"rendered":"https:\/\/www.thermofisher.com\/blog\/behindthebench\/?p=19976"},"modified":"2026-10-01T22:16:23","modified_gmt":"2026-10-01T22:16:23","slug":"taqman-assay-design-genomic-data","status":"publish","type":"post","link":"https:\/\/www.thermofisher.com\/blog\/behindthebench\/taqman-assay-design-genomic-data\/","title":{"rendered":"What does it take to keep 20 million Applied Biosystems TaqMan Assays current?"},"content":{"rendered":"\n
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How do you keep more than 20 million TaqMan assays current as genomic knowledge evolves?<\/strong><\/h2>\n\n\n\n

Every year, our understanding of biology becomes more complete. New variants are discovered, transcript annotations improve, gene models evolve, and our assay-design algorithms advance. That does not mean an assay designed years ago is suddenly outdated. In many cases, re-evaluating an existing assay against today’s genomic knowledge confirms that the original design remains a strong choice. In other cases, new information reveals an opportunity to improve the design or expand coverage to a newly characterized target.<\/p>\n\n\n\n

Keeping a portfolio current therefore means systematically revisiting it as science evolves. At this scale, that evaluation can lead to three outcomes: confirm designs that continue to meet current criteria, improve designs where new biological or computational information helps provide an opportunity, and expand the portfolio where new genomic knowledge creates gaps in coverage.
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TaqMan Assay portfolio updates at a glance<\/strong><\/h2>\n\n\n\n

As genomic knowledge and bioinformatics methods evolve, TaqMan Assays are systematically re-evaluated with three potential outcomes:<\/p>\n\n\n\n

Confirm<\/strong>
 The majority of existing Gene Expression and SNP Genotyping Assays evaluated continued to meet current design criteria.<\/p>\n\n\n\n

Improve<\/strong>
 Approximately 81,000 SNP Genotyping Assays and 1,300 Gene Expression Assays were redesigned based on updated variant information and bioinformatics evaluation.<\/p>\n\n\n\n

Expand<\/strong>
 Approximately 660,000 new SNP Genotyping Assay designs and 5,700 new Gene Expression Assays were added, with the SNP portfolio now covering >70% of ClinVar variants, to support research needs.<\/p>\n\n\n\n

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Keeping the TaqMan Gene Expression portfolio current<\/strong><\/h2>\n\n\n\n

Gene expression assays are designed with a purpose. Some are intended to quantify overall gene expression by targeting regions shared across transcript isoforms, while others are designed to detect specific transcript variants. As gene models and transcript annotations evolve, these original design assumptions must be revisited to evaluate whether assays continue to help provide excellent coverage and analytical specificity.<\/p>\n\n\n\n

Our work focused on maintaining a comprehensive and biologically relevant Gene Expression portfolio. We evaluated existing TaqMan assays against 2026 gene and transcript annotations for human, mouse, and rat, mapped assays to current transcript models, performed portfolio-wide coverage analysis, and identified opportunities to confirm existing designs, improve them where needed, or expand coverage with new assays. We also refined our SNP-to-transcript mapping pipeline to improve annotation accuracy and incorporated variant-aware redesign where underlying sequence variation affected assay performance. Together, this led to approximately 5700 new Gene Expression Assays and 1300 redesigned assays.<\/p>\n\n\n\n

Keeping the TaqMan Genotyping portfolio current<\/strong><\/h2>\n\n\n\n

The pace of change is particularly dramatic for genotyping. dbSNP Build 150, released in 2017, contained approximately 323 million RS IDs, including approximately 38 million classified as common. By Build 157, the number of RS IDs had grown to approximately 1.25 billion\u2014nearly four times the number represented in Build 150.<\/p>\n\n\n\n

For the predesigned TaqMan genotyping portfolio, this expanding body of knowledge presents both an opportunity and a challenge. Every new dbSNP release introduces newly cataloged variants while improving our understanding of existing ones. Existing assays may overlap variants that were not known when the assays were originally designed, while newly cataloged SNPs create opportunities to expand portfolio coverage.<\/p>\n\n\n\n

Our approach combined portfolio expansion with variant-aware redesign. We systematically evaluated the existing portfolio against the latest variant information, refined our SNP and indel masking strategies, and assessed assays for underlying sequence variation. This evaluation confirmed that majority of our existing assays continued to meet current design criteria. Where opportunities for improvement were identified, these analyses guided redesign, while newly cataloged SNPs enabled expansion of the portfolio. Together, these efforts resulted in approximately 660,000 new SNP assay designs and 81,000 redesigned assays.<\/p>\n\n\n\n

Keeping pace with better design algorithms<\/strong><\/h2>\n\n\n\n

Biological knowledge isn’t the only thing that evolves \u2014 our bioinformatics methods and design algorithms evolve alongside it. As our understanding of assay design improves, we continually refine our computational models to better predict assay performance and identify opportunities for improvement.<\/p>\n\n\n\n

Every SNP and Gene Expression assay was re-evaluated using our latest bioinformatics tools and design algorithms. We analyzed oligonucleotide interactions, including self-interactions and secondary interaction risks, and applied improved computational models across the portfolio. For many assays, this evaluation confirmed the existing design. Where an opportunity for improvement was identified and redesign was technically feasible, the assay was updated.<\/p>\n\n\n\n

Better algorithms therefore do more than help us design new assays. They give us a systematic way to revisit previous design decisions \u2014 confirming strong designs while identifying where the portfolio can be made even better.<\/p>\n\n\n\n

Scale changes the questions<\/strong><\/h2>\n\n\n\n

As the portfolio grows, so does the nature of the challenge. Working with millions of assays fundamentally changes the questions we ask. Instead of asking only whether an individual TaqManassay is well designed, we begin asking which parts of the portfolio would benefit most from improvement, where redesign will have the greatest scientific impact, where current designs should be retained, and how new biological knowledge can be applied systematically across millions of assays.<\/p>\n\n\n\n

At this scale, keeping the TaqMan Assay portfolio current isn’t simply about designing something new. It’s about continuously applying the latest biological knowledge and computational methods to know when an assay should change \u2014and just as importantly, when an existing design remains the right one. This is where bioinformatics becomes much more than an assay design discipline. It becomes the science of managing knowledge at scale by integrating new biological discoveries, improving computational methods, and translating both into better assays for researchers around the world.<\/p>\n\n\n\n

Frequently asked questions<\/strong><\/h2>\n\n\n