Title: Using multi-omics and AI to accelerate Precision Medicine
Abstract: This talk will review the development and implementation of clinical grade (CLIA) whole-exome sequencing based genomic tests for precision cancer medicine and immunotherapy. A novel analytical pipeline will be described that evaluates genomic profiles to unravel the immune landscape of tumors and integrates multi-omics features using machine learning to predict immunotherapy response. High-throughput single-cell imaging approaches and custom spatial analytics will be presented that dissect the tumor microenvironment and unravel immune repertoires at single-cell resolution. These multi-omic approaches are increasingly complemented by novel AI approaches that analyze medical images to improve diagnosis and predict treatment response.

Biography: Olivier Elemento directs the Englander Institute for Precision Medicine at Weill Cornell Medicine, an Institute that focuses on using genomics and informatics to make medicine more individualized. He is also the Associate Program Director of the Clinical and Translational Science Center, and the Associated Director of the Institute for Computational Biomedicine. The focus of Dr. Elemento’ research is on the systems biology of cancer, particularly on prostate cancer and hematological malignancies. His lab has developed several computational approaches for analysis of deep sequencing data, e.g. ChIPseeqer (for integrative analysis of ChIP-seq data) and SNVseeqer/INDELseeqer (full pipeline for mutation detection and characterization from deep sequencing data). His lab has also developed several additional computational approaches that include a pathway analysis tool (iPAGE) several tools for regulatory element detection (FIRE and FastCompare) and RRBseeqer for ERRBS analysis (including detection of differentially methylated regions). Dr. Elemento has a PhD from University of Montpellier, and has received numerous awards including NSF CAREER, Hirschl Trust Career Scientist Award, and Walter B Wriston Award.

Title: Real-time Computational science for COVID-19 pandemic planning and response
Abstract: COVID-19 pandemic represents an unprecedented global crisis. Its global economic, social and health is already staggering and will continue to grow. Computation and, more broadly, computational thinking plays a multi-faceted role in supporting global real-time epidemic science especially because controlled experiments are impossible in epidemiology. High performance computing, data science and new sources of massive amounts of data from device-mediated interactions have created unprecedented opportunities to prevent, detect and respond to pandemics.

In this talk, using COVID-19 as an exemplar, I will describe how scalable computing, AI and data science can play an important role in advancing real-time epidemic science.

Biography: Madhav Marathe is an endowed Distinguished Professor in Biocomplexity, Director of the Network Systems Science and Advanced Computing (NSSAC) Division, Biocomplexity Institute and Initiative, and a tenured Professor of Computer Science at the University of Virginia. Before joining UVA, he held positions at Virginia Tech and Los Alamos National Laboratory. Dr. Marathe is a passionate advocate and practitioner of transdisciplinary team science. During his 25-year professional career, he has established and led a number of large transdisciplinary projects and groups. His areas of expertise are network science, artificial intelligence, high performance computing, computational epidemiology, biological and socially coupled systems, and data analytics. His division focuses on developing the scientific foundations and the associated engineering principles to study large-scale biological, information, social, and technical (BIST) systems. His works have appeared in over 350 articles at various prestigious peer-reviewed venues. Dr. Marathe is a recipient of numerous awards and is a Fellow of IEEE, a Fellow of ACM, a Fellow of AAAS, and a Fellow of SIAM.

Title: The Human Gene Catalogue: Are we there yet?
Abstract: A huge and still-growing number of genetic studies depend on the human gene catalogue, including thousands of experiments each year and an enormous investment of time and effort. However, despite its critical role in biomedical research, the human gene list is still incomplete and, in many ways, unstable. The widespread use of RNA sequencing technology over the past decade has allowed scientists to discover a far larger and richer repertoire of genes and transcripts than previously known. As of mid-2020, the two leading human gene databases, RefSeq and GENCODE, agree roughly on the number of protein-coding loci, but disagree radically on the precise exon-intron structure of those genes. Furthermore, they also differ on thousands of long noncoding RNA genes (lncRNAs) that are just as vital to human biology as many protein-coding genes.

Our own recent efforts led to the creation of a new human gene catalogue, called CHESS, that we built using a very large collection of nearly 10,000 RNA-seq experiments from 31 tissues, all sequenced as part of the GTEx project. Processing this large amount of data was one of the most challenging tasks, made possible by the computational efficiency of StringTie, a transcriptome assembler we developed in our lab. The transcript assembly process produced over 135,000 novel protein-coding transcripts, not present in either RefSeq or GENCODE. CHESS also shares a larger number of transcripts with both GENCODE and RefSeq than they share with one another. Although CHESS only modestly increases the number of protein-coding genes, it includes over 4000 novel lncRNAs and it more than doubles the number of splice variants and other isoforms of the known genes. It also provides a reference with substantially greater experimental support than previous human gene catalogs.

Biography: Mihaela Pertea is an Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University. She received her B.S. and M.S. degrees in Computer Science from University of Bucharest in Romania, and her Ph.D in Computer Science from the Johns Hopkins University School of Engineering. Dr. Pertea’s work in computational biology draws upon techniques and data from multiple disciplines, including computer science and molecular biology, genetics, biotechnology, and statistics. Her work has focused on computational gene finding and sequence pattern recognition and she has developed several open-source gene finders that were used for the annotation of the genomes of Plasmodium falciparum (malaria parasite), Arabidopsis thaliana, rice, Aspergillus fumigatus, Cryptococcus neoformans, and others. A major focus of her current research is on developing innovative and efficient methods to analyze large DNA and RNA sequence data in order to provide a genome-scale understanding of cellular function. Dr. Pertea believes that the principled use of algorithms from other fields, adapted to the problems of computational biology and coupled with careful software engineering and high performance computing, has the potential to make a significant impact in the life sciences. She has published over 50 scientific papers that have received more than 30,000 citations to date.