Presented by: Dr. Hongzhe Lee, PhD, Perelman Professor of Biostatistics, Epidemiology and Informatics, Director, Center for Statistics in Big Data, Vice Chair for Research Integration, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania

About the presentation: The gut microbiome plays an important role in maintenance of human health. High-throughput shotgun metagenomic sequencing of a large set of samples provides an important tool to interrogate the gut microbiome. Besides providing footprints of taxonomic community composition and genes, these data can be further explored to study the bacterial growth rate and metabolic potentials via generation of small molecules and secondary metabolites. Everything from microbiome diagnosis to microbiome-based therapy will rely on vast amounts of data analysis. In this talk, I will present several computational and statistical methods for analysis of data measured on phylogenetic tree and methods for estimating bacterial growth rate for metagenome-assembled genomes (MAGs). I will also present a deep learning algorithm for predicting all biosynthetic gene clusters (BGCs) in the bacterial genomes. The key statistical and computational tools used include Wasserstein distance estimation, optimal permutation recovery based on low-rank matrix projection and a LSTM deep learning method to improve prediction of BGCs. I will demonstrate the application of these methods using several ongoing microbiome studies of inflammatory bowel disease at University of Pennsylvania.

To join via Zoom, visit https://pshealth.zoom.us/j/94860982030. To join via the phone, dial 929-205-6099.

The meeting ID is 948 6098 2030 and the passcode is 320367.