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Sravanthi Tejomurthula is advancing genomic research by designing high-throughput, automated sequencing workflows that merge NGS, robotics, and bioinformatics for greater speed, accuracy, and scalability. Her systematic innovations—from miniaturized protocols to predictive maintenance tools—are helping shape the future of intelligent, reproducible, and data-driven biology.
Genomics research has undergone a significant transformation. What was once a field defined by manual, low-throughput methods has shifted toward highly automated, data-intensive operations.
This evolution is driven by the convergence of next-generation sequencing (NGS), robotics, and sophisticated data analytics, enabling scientists to address biological questions at an increased scale.
Sravanthi Tejomurthula, a molecular biologist and Senior Research Associate at Lawrence Berkeley National Laboratory, has focused on developing and optimizing high-throughput workflows. With over eight years of experience at institutions including Stanford University and Gilead Sciences, her work involves the technical development required to make large-scale biological discovery more efficient and reproducible.
The field of next-generation sequencing has progressed from a niche academic tool into a component of modern biology. This field has seen improvements in speed, cost, and scalability, largely driven by automation and protocol optimization. Early NGS methods were characterized by complex, manual processes that were both time-consuming and expensive.
Tejomurthula recalls her initial interest in this potential. “My passion for NGS was ignited during my Master’s at the University of the Pacific, revealing NGS’s potential to decode complex genomic data for drug development and environmental solutions,” she states. This early exposure to genomic analysis influenced her career focus on high-throughput applications, similar to how modern clinical bioinformatics platforms process millions of sequencing reads.
Over the years, the technology has matured, moving past the initial hurdles of manual sample preparation that limited throughput. “Over my eight-year career, I’ve witnessed NGS evolve dramatically from labor-intensive, high-cost technologies to highly automated, scalable platforms that have revolutionized genomic research,” Tejomurthula adds. This transition is evident in modern labs that utilize instruments like the Illumina MiSeq for streamlined workflows, changing the pace of discovery.
The integration of robotics has been a key factor in the evolution of high-throughput biology, replacing repetitive manual tasks with precision automation. This shift increases sample throughput and can enhance accuracy and reproducibility by minimizing human error. Platforms from Hamilton, Agilent, and Beckman Coulter have become central to this transformation.
Tejomurthula’s transition into this area was influenced by observing the impact of automation. “My focus on robotic automation was sparked at Novartis (2018), where I transitioned from labor-intensive manual workflows to using the Agilent BRAVO liquid handler,” she explains. The ability of such systems to perform complex tasks in minutes, compared to hours manually, indicated a potential for greater efficiency.
Later, she addressed the limitations of disconnected systems. “At Gilead Sciences (2019–2020), I encountered standalone robots that have limited high-throughput ADME assay screening,” Tejomurthula notes. These efforts to create integrated workstations are reflected in broader industry trends, where solutions like Beckman Coulter systems are paired with industrial robots for high-throughput applications.
Implementing fully integrated automation can improve data quality and operational stability. One of the advancements in this area is the development of systems that can monitor performance and predict failures, preventing downtime and sample loss.
Tejomurthula points to a specific project at Lawrence Berkeley National Laboratory as an example of this impact. “One of the most impactful innovations I led was where I automated NGS library preparation using the Hamilton Star platform for a large-scale microbial genomics project,” she says. This initiative cut processing time by 40% and improved sequencing accuracy to 99.5%.
Beyond workflow optimization, she developed tools for instrument management, a concept supported by industry data showing that predictive maintenance can reduce disruptions. “I created a tool that predicted hardware failures before they occurred,” Tejomurthula states. Such systems often rely on in-line sensors for process analytical technology, like the Hamilton Dencytee Arc, to provide the real-time data needed for these models.
Developing effective high-throughput workflows requires a systematic approach that balances scientific goals with operational realities. As sample volumes increase, protocols must be robust enough to handle variability while remaining cost-efficient and scalable. This requires a strategic design process that considers every step from sample input to data output.
Tejomurthula’s methodology is built on four principles. “My approach to developing and implementing scalable next-generation sequencing (NGS) protocols is systematic and innovation-driven. I design workflows that balance four key pillars: technical robustness, cost-efficiency, biological fidelity, and scalability,” she explains.
A key strategy for achieving this balance is miniaturization. “For example, while adapting the Smart-seq3xpress method, I miniaturized the workflow to reduce reagent consumption while maintaining full-transcript coverage for over 16,000 single cells,” Tejomurthula says. This optimization is relevant in a field where the high cost of ownership for laboratory systems remains a consideration.
In high-throughput genomics, raw data requires interpretation. The value is derived from translating vast datasets into biological insights, a task that falls to the field of bioinformatics. These computational tools are used for tasks ranging from identifying drug targets to understanding complex systems-level interactions.
Tejomurthula notes that bioinformatics has become an integral part of modern research. “Bioinformatics is now the backbone of molecular biology, especially in high-stakes environments where it transforms vast and complex biological datasets into actionable insights,” she asserts. The creation of these analytical platforms often requires combining mechanistic knowledge with statistical tools for data analysis.
Her work at Gilead Sciences provides an example of its direct application in drug development. “I co-developed a bioinformatics tool to analyze high-throughput ADME assay data. This tool improved drug candidate selection efficiency by 25%, accelerating preclinical screening in oncology and virology,” Tejomurthula notes.
The consistent execution of advanced laboratory protocols depends on the ability of diverse teams to perform them reliably. As workflows become more complex, training involves more than technical instruction; it requires fostering a shared understanding of best practices to ensure reproducibility across different operators and departments.
Tejomurthula has addressed this challenge at LBNL. “Training cross-functional teams requires more than technical instruction; it demands alignment across disciplines, clarity in expectations, and a shared commitment to reproducibility,” she states. Without this alignment, variability in technical fluency can affect data quality.
To address these issues, she implemented a structured, multi-phase training framework. “This approach reduced training time by 50%, improved protocol compliance to 95% within three months, and increased reproducibility to 98% across sequencing runs,” she explains. Ensuring compliance is critical in regulated environments, where using AI and machine learning requires an understanding of concepts like Good Machine Learning Principles (GMLP).
Emerging trends in laboratory automation suggest a move toward more intelligent, integrated, and flexible systems. The focus is shifting beyond simple task execution toward creating orchestrated systems that can adapt in real time, leveraging machine learning, cloud connectivity, and modular designs to accelerate research.
Tejomurthula sees this as a shift toward improved management of complex processes. “Robotics in molecular biology is entering an era defined not just by automation, but by intelligent orchestration of complex workflows,” she says. AI and machine learning are central to this trend.
“Robotic platforms paired with machine learning algorithms can predict workflow errors, adapt pipetting parameters in real time, and prevent equipment downtime,” she notes. This vision includes cloud-based dashboards for remote monitoring and predictive maintenance, enabling multi-site collaboration and further breaking down operational silos.
Future developments in NGS and high-throughput biology are expected to focus on deeper integration and efficiency through miniaturization and multiplexing. The goal is to build automated, end-to-end pipelines that can yield more complex biological insights from smaller sample volumes, making advanced analyses more accessible.
Tejomurthula’s future work is aimed at this goal. “My next phase of innovation focuses on miniaturizing, multiplexing, and integrating NGS workflows to unlock deeper biological insights at greater efficiency,” she states. This involves creating automated single-cell sequencing pipelines and building multi-omic assays.
Ultimately, this work contributes to new capabilities for the scientific community. “Innovation in this space is not simply about building new tools—it’s about designing integrated, intelligent systems that expand the possibilities of high-throughput biology,” she concludes. “That is the future I am actively creating.”
The convergence of NGS, robotics, and data science continues to shape biological research. The work of researchers like Tejomurthula contributes to the development of new systems for discovery. This systematic approach, blending hardware, software, and biological knowledge, is applied to complex challenges in medicine and environmental science.
