By Mike Garland and Mitra Geier
This past fall, we traveled to the Pacific Northwest National Laboratory (PNNL) for training in computational analysis of RNA-seq data. During this two-day externship, we worked with PNNL scientists as they walked us through our data and gave us an overview of computational approaches they use to analyze RNA-seq data.
During the externship we were provided hands-on experience with computational methods under the guidance of experts. Our ultimate goal was to apply what we learned at PNNL to current and future RNA-seq projects.
Our work at PNNL centered around an experiment that compared regenerating vs non-regenerating caudal fins of zebrafish, which is a phenomenon of interest for a variety of applications. The regenerating caudal fin model is a useful toxicological tool for chemical screening, and is well-suited for studying how chemical exposure can lead to changes in molecular signaling events that occur during the wound healing process. Furthermore, regeneration and development share many critical signaling events, making this model useful for interrogating mechanisms of developmental toxicity.
By using a systems approach to understand expression patterns of mRNA and miRNA during regeneration, we can improve our understanding of molecular processes involved in wound healing. This would allow us to be better-informed when making hypotheses about the mechanisms of toxicity following chemical exposure in zebrafish. Given the applicability of this model to developmental toxicology, the results from this experiment will be particularly useful for future directions of SRP Project 3.
Age is a known factor of regenerative ability, and different life stages are frequently used in various toxicological studies. This was incorporated into the experiment using age-based cohorts and we learned methods to compare age-dependent differences in gene expression during regeneration. Drs. Joe Brown and Jason Wendler, both computational biologists at PNNL, trained us over our externship on a variety of methodologies including quality control, read alignment, statistical inference, biological pathway enrichments, and data visualization methods.
Over the course of the two days, we covered many computational methods involved in RNA-seq data analysis, which will be useful in our other ongoing projects, as well as future work as our careers progress. We are also grateful for the opportunities for professional networking outside of our typical academic circles. We learned quite a bit about the mechanics of working in a national laboratory and how that is different than working for a university. We are appreciative of the time and effort put in by Drs. Brown and Wendler, and we also thank Dr. Katrina Waters who helped organize our trip to PNNL.