Viral infections are commonly observed in nature. Recently, SARS-CoV-2 has caused a global pandemic which has infected over 200 million individuals worldwide (as of August 2021). Effective and efficient detection of viruses in host genomes, together with tracking how viruses interact with host genomes, are major challenges. In this talk, I will first introduce our computational approaches for detecting viruses and their integration sites in host genomes from next-generation sequencing data. Then, based on our recently developed Viral Integration Site DataBase (VISDB), we have developed a deep learning method, DeepVISP, for virus site integration prediction and motif discovery. To study COVID-19, we developed a deep learning method, DrivAER: Identification of Driving transcriptional programs with AutoEncoder derived Relevance scores from single-cell RNA sequencing (scRNA-seq) data. We applied DrivAER to COVID-19 scRNA-seq data and also for integrative analysis of COVID-19 genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS). Our investigation identified a number of genes, regulatory factors, and cellular trajectories that may be relevant in COVID-19 disease severity.
Dr. Zhongming Zhao has a unique, interdisciplinary educational and research background. He completed master’s degrees in Genetics (1996), Biomathematics (1998), and Computer Science (2002), Ph.D. degree in Human and Molecular Genetics (2000), and Postdoctoral Fellowship in Bioinformatics (2001 – 2003). Dr. Zhao currently serves as Chair Professor for Precision Health at UTHealth. Before he joined UTHealth in 2016, he was Ingram Endowed Professor of Cancer Research, Professor (tenured) in the Departments of Biomedical Informatics, Psychiatry, and Cancer Biology at Vanderbilt University Medical Center, Chief Bioinformatics Officer of the Vanderbilt-Ingram Cancer Center (VICC), and Associate Director of the Vanderbilt Center for Quantitative Sciences. Dr. Zhao has broad interests in bioinformatics, genomics, precision medicine, and machine learning and has co-authored more than 400 scientific papers in these areas (H-index = 68, Google Scholar). Throughout his career, he has collaborated with numerous researchers while also pursuing his own independent research, funded by numerous federal, state, and foundation grants. He has trained more than 70 students and postdoctoral fellows (24 have become academic faculty), mentored 11 junior faculty, and co-mentored/collaborated with five NIH K awardees. For more information see attached flyer.