Vaidhyanathan (Vaidhy) Mahaganapathy, Ph.D., is a Computational Biologist focused on genomics research for the Applied AI team at the Ellison Institute of Technology (EIT). He recently spoke with CRDC Insights, reflecting on the support he received during his dissertation research from the team at Seven Bridges-Cancer Genomic Cloud (SB-CGC), powered by Velsera. He also commented on the complexity of working with huge datasets and trends in cancer research data science, and offered advice to graduate students looking to start a research career.

Dr. Mahaganapathy’s dissertation focused on detecting clonal hematopoiesis (CH), a process prevalent in older people, which has an impact on immune function and inflammation, and is related to many cancers and diseases of aging. For his dissertation, he analyzed 17,000 whole exome sequences in the TCGA dataset, housed through the CRDC, to look across thousands of subjects for mutations consistent with CH. 

As he notes: My dissertation required that I work with vast amounts of data, and the Seven Bridges team helped me understand not only the science, but the technicalities and the logistics of working with that much data. For example, how do you download, store, or move it? If you break it up in chunks, how do you make sure the chunks are valid? And how do you not let the logistical challenges impact the pace of research or the budget?  

The SB-CGC team provided high-performance computing resources, necessary for this volume of data, and guidance around deploying software developed in his PhD mentor’s lab. The team also assisted with  the implementation of a novel pipeline that he developed to support his work. These are all skills that he would carry forward in his career. 

Dr. Mahaganapathy: The Seven Bridges team pretty much held my hand through the process, and I’m happy to say I was one of their collaborator grant recipients, which covered the cost of my compute time. They were instrumental in making sure that the deadline for my dissertation was met with on-demand access to more compute cores and technical support to debug issues.

Now at EIT, Dr. Mahaganapathy supports bench scientists with computational ‘omics sequencing study design and analysis, specifically related to colorectal and breast cancer, among others, for which researchers at EIT are investigating tissues, organoids, and organ-on-chip models. His experience working with thousands of sequences and familiarity with TCGA data makes him fluent in referencing TCGA as a control to confirm what he and his colleagues are seeing in their large, internal patient datasets. 

Commenting on how rapidly computational biology is changing, Dr. Mahaganapathy notes that researchers are now conducting more comprehensive sequencing analyses and can do large volumes of work more rapidly. 

Dr. Mahaganapathy: The research community can and should procure and analyze multi-omics data collected from the same subject over time to look at dynamic changes in the evolution of mutations and disease. Imagine what we could do if we had samples at three or more time points for every person in a dataset the size of TCGA. And then imagine if we went from 17,000 subjects to 50,000. What if we had pre-treatment transcriptomics data and then data from various treatments and then post treatment at multiple year intervals for this number of subjects? This is what we need to get to true personalized medicine – this kind of detailed molecular data that is also linked to clinical data. 

In concluding his conversation with CRDC Insights, Dr. Mahaganapathy offered some inspirational words for students considering a research career: Be positive – be curious. There are a lot of good people in the world doing amazing things. We have so much more useful information than we’ve ever had, and we can harness it to help the world. It’s only going to get better with all the amazing tools we have at our disposal. There is a lot of positivity in the world, and we should contribute as best we can.