Real-Time Genomic Surveillance of Pathogens through Bioinformatics Applications

Abstract: Viral molecular surveillance, a technique in which viral genomes are reconstructed from sequence data generated from samples collected from patients as well as the environment (e.g. wastewater) and are monitored in real-time or near real-time, has been critical throughout the COVID-19 pandemic. As the cost of viral genomic sequencing falls and the throughput of sequencing technologies grows, the amount of viral sequencing data produced by researchers around the world has grown exponentially, resulting in a critical need for improved scalability of bioinformatics analysis pipelines. Further, the ability to simulate epidemics as a function of model parameters enables insights that are unobtainable from real datasets. In this talk, I will introduce standard bioinformatics problems in the world of viral molecular epidemiology, and I will present massively-scalable tools my lab has developed to enable both the simulation and the analysis of ultra-large viral genomic datasets.

Bio: Niema Moshiri is an Associate Teaching Professor in the Computer Science & Engineering Department at the University of California, San Diego (UCSD). He works on computational biology, with a research focus on viral phylogenetics and epidemiology. He also places a heavy emphasis on teaching, namely on the development of online educational content, primarily Massive Adaptive Interactive Texts (MAITs).