Training Program in Computational Genomics

$566,850T32FY2025HGNIH

University Of Pennsylvania, Philadelphia PA

Investigators

Li-San Wangcontact Junhyong Kim Mingyao Li

Linked publications & trials

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Abstract

Enter the text here that is the new abstract information for your application. The University of Pennsylvania has trained computational genomicists for over 20 years supported by this NHGRI program, training 75 predoctoral and 13 postdoctoral trainees, the majority of whom have gone on to careers in research and development. We propose to continue our Computational Genomics Training program with 10 predoctoral trainees per year. Trainees will be appointed for 2 years, and this program will support PhD and MD/PhD students in years 3 and 4 or 4 and 5 of their PhD programs. The programâs focus will be on the themes of algorithms and statistic modeling, databases, high performance computing, genomic technologies, complex trait population genetics, molecular evolution, single cell and subcellular omics analysis, spatial omics and image genomics, artificial intelligence and machine learning, and population/biobank scale genomics and precision medicine. Our program concentrates on a rigorous course-based curriculum supported by courses in multiple graduate groups and provides training in responsible conduct of research (RCR) and scientific reproducibility (SRR). Our program consists of trainers with expertise spanning disease genomics, genomic technologies, multidimensional statistics, algorithms, data sciences, and machine learning. Our training environment is enhanced by key facilities including large biobanks, high-throughput genomics core, high- performance computing core, and a unique immersive data visualization facility. Penn overall hosts more than 60 NIH training programs with strong institutional administrative support for managing the training programs. Success of our training program will help train the next generation of genomic workforce in the skills and knowledge necessary to apply state-of-art computational techniques to genomics and develop new techniques for novel genomic data.

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