Advancing patient care starts with making complex science usable in the real world. Yueyao Gao, Senior Computational Scientist in the Translational Analysis Group (TAG) at Broad Clinical Labs, works at the intersection of research and clinical application – developing and validating computational workflows that make genomic sequencing reliable, reproducible, and ready for patient care at scale. We recently asked her to share more about her work, what motivates her, and how she’s helping turn data into impact.
What do you do at Broad Clinical Labs?
I develop and validate computational workflows that make BCL sequencing products reliable and reproducible for clinical use. In practice, that often means taking methods that work in research settings and enabling them to work consistently at scale for real patients.
What drew you to this field of work?
I came from a plant biology background. My PhD focused on nitrogen fixation and RNA sequencing. However, I found that the pipelines I was building were often limited to individual research questions.
I joined BCL because I wanted to solve a different kind of challenge: building computational pipelines that matter at scale. At BCL, we are not just answering one question at a time – we are enabling workflows that bridge the gap between genomic data and real-world patient care.
What is something you’ve worked on at Broad/ BCL that you’re especially proud of?
Patients and their families undergoing genetic testing often face a grueling diagnostic odyssey – endless laboratory tests, long waits, and mounting costs. My work with a leading diagnostic collaborator and Illumina focused on a clear goal: evaluating whether whole genome sequencing (WGS) could replace chromosome microarray (CMA), the current gold standard, to help simplify that journey.
The transition wasn’t simple. While the sensitivity of WGS in CNV calling is well-established in the literature, we were solving a different challenge: operationalizing that sensitivity. The sheer volume of copy number variants generated by WGS is often more than variant analysts can realistically interpret. To address this, we collaborated with Illumina’s DRAGEN team to improve their cytogenetics module, and I developed a cohort-based filtering approach. We validated this approach on hundreds of real patient samples that had previously gone through CMA, demonstrating that WGS was not only equivalent in sensitivity and higher in resolution, but also usable for routine clinical reporting.
What I am most proud of is striking that balance between sensitivity and usability. It is one thing to build a tool that works in a controlled research setting; it is an entirely different challenge to make it part of a routine clinical workflow. Seeing that bridge form between research and real-world patient care is incredibly rewarding.
What keeps you motivated and/or inspired in your role?
Knowing that the work has real clinical impact keeps me motivated. We are not just building or assessing tools in a vacuum – we are refining workflows that scientists and families depend on for crucial clinical decisions.
When you realize that the accuracy of your computational pipeline directly affects patient care, the work stops being just science and becomes a fundamental responsibility.
In your own words, how, or why, does #ScienceStartsHere at BCL?
Science starts here because we are not just generating sequencing data, we are asking how that information can be translated into action.
BCL sits at a unique intersection of cutting-edge research and clinical responsibility. With scalable infrastructure, scientific expertise, and an innovative mindset, we are positioned to help redefine the standards of diagnosis and treatment.
When not in the office/lab, what are some things you enjoy doing?
Outside of work, I share my home with two cats, Huhu and Zizzy, who are excellent at reminding me when it’s time to step away from my computer. I also play badminton regularly and love cooking and exploring different cuisines – the kitchen is where I do some of my best thinking!