05 Nov 2021

Harnessing Next-Generation Sequencing Technologies for Drug Target Discovery


Register for the seminar: http://go.hawaii.edu/JJq

Seminar Abstract:
Next-generation sequencing (NGS) has transformed how we study human diseases and develop drug therapies. We are no longer limited to studying several genes at a time. Instead, the high-throughput, scalable nature of NGS-based technologies allows us to explore global changes at the genome and transcriptome levels. In this talk, I will discuss how I have employed NGS applications and data science techniques to develop therapeutic interventions for human diseases. First, I will discuss previous research using NGS to understand the role of RNA-binding proteins in amyotrophic lateral sclerosis, a devastating neurological disease. By understanding the normal function of these RNA-binding proteins in neurons, we hope to understand how they contribute to disease and identify gene signatures of pathology. Second, I will discuss my efforts to create data-driven frameworks for drug target and biomarker discovery in the oncology space.

Speaker Bio:
Dr. Katannya Kapeli received B.S. ‘s in Molecular Biology and Bioinformatics from Pacific University, Oregon in 2004 and a Ph.D. in Cancer Biology from Oregon Health & Science University in 2012. Her graduate work with Dr. Peter Hurlin focused on understanding how aberrant activation of the Ras/MAPK signaling pathway leads to deregulation of N-Myc, a potent transcription factor frequently implicated in neuroblastoma. As a postdoctoral fellow with Dr. Gene Yeo at UC San Diego and later at the National University of Singapore, Katannya focused on RNA binding proteins and their role in neurodegeneration and cancer. Using next-generation sequencing, she carried out genomic and transcriptomic type studies to explore global effects of RNA-binding proteins to gene expression that ultimately dictate cell function. At present, Katannya is a Bioinformatics Data Scientist at SEngine Precision Medicine, where she builds models using public and internal dataset from high-throughput screens for drug target discovery.

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