Seth is a graduate of Johns Hopkins University with a B.S. in Molecular and Cellular Biology, combining academic excellence with extensive research and community service experience. His undergraduate research focused on sex-specific differences in orofacial cleft risk using pediatric genomics data, and has presented this research at several scientific conferences. Currently, Seth works as a Research Assistant at the Johns Hopkins Bloomberg School of Public Health, contributing to the NIH’s Acute to Chronic Pain Signatures Consortium’s Data Integration and Resource Center team. Here, he contributes by preparing and analyzing multimodal data to enhance the field of pain science.

What have you enjoyed most about working on the CAVATICA platform?

I have enjoyed CAVATICA’s versatility, which empowers researchers to investigate any scientific question. As someone passionate about integrating large-scale omics and other multimodal biomedical data, it is exciting to have a resource that can incorporate datasets from any cloud-based repository, operate with diverse datatypes, and support the use of any computation tool required for a custom analysis.

Could you describe your current research focus and its potential impact?

After graduating from JHU, I joined the NIH’s Acute to Chronic Pain Signatures (A2CPS) Consortium’s Data Integration and Resource Center team, which is stationed at our Bloomberg School of Public Health. On the team, I currently work with omics, neuroimaging, EHR, and psychosocial data in an effort to create “biosignatures” that predict one’s likelihood to develop chronic pain. There exists a current healthcare gap for validated clinical biomarkers connected to chronic pain, and I am enthusiastic about the potential for our team’s findings to address this need and improve patient outcomes.

How did the Common Fund Data Ecosystem data sets help you in your analysis?

In my undergraduate research, I worked with CFDE datasets from Gabriella Miller Kids First in CAVATICA to uncover the sex-specific genomic effects underpinning orofacial clefts (OFCs). The consortium released whole genome sequencing (WGS) data for several trio cohorts across nationalities, which we included in our overall meta-analysis. As the most common group of craniofacial malformations in humans, OFCs represent a significant public health burden, and our findings will contribute to the literature on their complex and heterogeneous genetic etiology, which currently remains largely unexplained.

Now, I am excited to be working with A2CPS – another CFDE consortium!

What challenges have you encountered, and how have you overcome them using CAVATICA?

While meta-analyzing OFC trios, our team initially downloaded GMKF’s WGS data to our local high-performance computational cluster, which presented challenges with data duplication and management. Through CAVATICA, we overcame these issues by moving our analysis to the cloud, which allowed us to work directly with the data without duplication and with streamlined file management.

Additionally, there is often a difficulty – one that I also encountered – in carrying out custom, specialized analytic pipelines with biomedical cloud providers. CAVATICA resolved this issue as it integrates with the software containment platform Docker, which allowed for the migration of the computational environments for each of our distinct processing steps to their platform.

How do you see your research evolving in the next few years?

Currently, I am in the PhD application process, pursuing programs in Genetics to further develop my skills as a scientist. My ultimate goal is to advance the science of personalizing healthcare by analyzing large-scale omics and other biological datatypes. In terms of my research evolution, I look forward to enhancing my understanding of systems biology, physiology, data science, biostatistics, and computation while working with A2CPS, and then building on these skills during my PhD.

Additionally, I plan to pursue analyses harnessing multimodal data from across several NIH CFDE Consortia – something I am very excited about.

Do you have any advice for others in the field who are new to CAVATICA?

My best advice would be to start by learning the fundamentals of cloud computing. By first understanding cloud infrastructure, software containment, and workflow languages, CAVATICA’s interface and overall flow will come naturally. Other than that, enjoy it – working with the platform is rewarding and can help uncover new biomedical discovery!