MassBio is partnering with the ElevateBio team on November 2 to host 2026 R&D Reimagined – Techbio Solutions, the second in a series of events designed to connect biotech and pharma R&D leadership with the technology innovators and solution providers transforming the life sciences with new tools and services.
Biotech currently stands at a critical crossroads. Therapeutic modalities are increasingly complex and leverage data-rich methods with terabytes of biological insight. However, a gap remains in how to more effectively process and analyze those insights for patient benefit. Trends in biotech, particularly cell and gene therapy, find that this effective analysis gap may be filled with automation. Increasing automation, fortunately, does not require an entirely new foundation built from scratch. Instead, we can leverage expertise derived from next-generation sequencing (NGS) operations in industry that have already built automated infrastructure to process massive genomic datasets and help accelerate patient impact.
The automation principles that have helped NGS groups process massive genomic datasets offer valuable lessons for the broader biotech ecosystem including, for example, moving from a traditional infectious titer analysis that is highly manual to a fully automated one within an electronic lab notebook (ELN) to save time and reduce error. Why did sequencing require automation with increased infrastructure, and what can other areas of biotech learn from its success? The following sections explore these topics and demonstrate how the industry is beginning to apply them to enhance patient care.
The Evolution from Manual to Automated: Lessons from Sequencing
Since the inception of Sanger sequencing in the 1970s and NGS, starting in the early 2000s, we have gone from a highly manual single-strand sequence process to an automated, massively parallel one in which billions of DNA strands can be sequenced at once. This automation evolution in sequencing has fundamentally reshaped drug discovery, but the transformation was not only in the sequencing technology itself. The evolution in sequencing was also supported by automated digital infrastructure to help handle, process, and analyze exponentially growing datasets.
With sequencing, infrastructure encompasses the computational, storage, and network resources required to support the lifecycle of genomic data. Modern sequencing operations often use automated, cloud-based platforms to support sequencing including sample sheet generation before sequence runs based on ELN entries, triggered quality control reports after sequencing runs, long-term data storage solutions, and the ability to launch automated processing pipelines and data transfers. These automated systems eliminate days of manual work for scientists and bioinformaticians and can ultimately represent the difference between a patient receiving targeted therapies in weeks versus months.
The Automation Imperative Across Biotech
Currently, biotech is expanding the automation imperative well beyond sequencing. The same pressures that drove sequencing toward automation: increasing data volume and the need for faster, more reliable results, similarly manifest across other corners of biotech including in vaccine development, manufactured therapy products, infectious disease monitoring, contamination detection, and more. Fortunately, we can take the lessons learned from sequencing and apply similar infrastructure and automation principles already in place, with some adaptations, to these other corners of biotech.
Consider the challenge of infectious titer assay analysis in cell and gene therapy manufacturing. Traditional analysis for infectious titer is relatively manual but a fundamental method today. The analysis process can be long, 30 minutes per plate, and is error prone. For example, this manual analysis is prone to copy and paste errors, wrong formulas being used or inadvertently manipulated, easily missed acceptance criteria, and incorrect interpretations. By applying the automated framework principles that we’ve already generated in the sequencing space, like automated sample sheet generation and auto-analysis all captured within an ELN, we can reduce infectious titer analysis time drastically to just a couple of minutes while simultaneously improving consistency and reducing human error.
The Path Forward: Transforming Patient Impact
This cross-pollination of automation expertise in sequencing applied to other corners of the industry is helping create scalable, adaptable biotech infrastructure ready for the next generation of therapeutics. And, most importantly, by sharing this expertise, our cross-functional efforts can ultimately accelerate patient impact. Every day saved by automating processes, analyses, results, and product generation translates directly to patients receiving life-saving therapies sooner.
About the Author
Christina Codden is a dynamic Bioinformatics Senior Scientist at ElevateBio with 7+ years of experience in the sequencing space leading pipeline development, cloud-based data processing, and data analysis, including driving auto-analysis initiatives. She holds a PhD from Northeastern University, where she developed novel machine learning algorithms to estimate marsh carbon fluxes. Additionally, Christina completed a Postdoctoral Fellowship at Tufts Medical Center focused on better understanding human heart disease through single-nuclei RNA-seq. Christina is dedicated to automating and improving analytical processes as well as mentoring the next generation of informaticians with OUTBio.
