864-889-0519 chg@clemson.edu

Bioinformatician/Programmer

Email: jopoole@clemson.edu

Dr. John Poole

Dr. John Poole

Biosketch

I earned a BS in Electrical Engineering from Clemson University and PhD in Physics from NC State University before pursuing a postdoctoral fellowship in Structural Biology at the University of the Western Cape and the University of Cape Town.  My early research in the biological sciences used NMR spectroscopy to study protein structure, function, and interactions before my first exposure to nascent NGS and the broader field of bioinformatics.

In 2012, I joined the University of KwaZulu-Natal in Durban as a lecturer in Electronic and Computer Engineering, before moving back to Cape town in 2016 as the Bioinformatics Service Platform manager at the CSIR Centre for High Performance Computing.   After a change in plans due to the COVID-19 pandemic, I undertook a temporary grant-linked Research Associate in Physics at Clemson University working on data analysis for a NASA project.   From there, I joined the Clemson University Center for Human Genetics in Greenwood as a Bioinformatician/Programmer in November of 2021.

Research

I am interested in a wide range of topics related to the understanding complex biological systems at the molecular and cellular level using computational methods.   These include gaining insight into the relationship between genotype and phenotype, the mechanisms of gene expression and regulation, RNA transcription, processing and folding, systems biology, epigenetics, developmental biology, neurobiology, and host pathogen interactions as well as noncommunicable chronic disease.

In addition to the science and applications, I am also interested in the computational technology that makes data-driven science and science at scale possible.   This includes traditional HPC clusters, highly parallel hardware such as GPUs, and adaptive computing (FPGAs).    I am optimistic about the use of AI/ML techniques for simulation, prediction, and scientific data analysis in addition to traditional algorithmic approaches.