Dr. Jason Ernst, UCLA
Computational Approaches for Deciphering the Non-coding Genome
Department of Biological Chemistry
University of California, Los Angeles
Understanding the human genome sequence and in particular the vast non-coding regions is a central challenge for modern molecular biology with profound implications towards understanding the genetic basis of disease. In this talk I will survey multiple different computational approaches that I have developed for better understanding the non-coding genome. I will first describe a method, ChromHMM, that learns de novo combinatorial and spatial patterns from maps of multiple epigenetic marks using a multivariate hidden Markov model (HMM). These patterns correspond to different classes of genomic elements, which I have then used to provide cell type specific annotations of the human genome. I will then describe a method, ChromImpute, to impute maps of epigenetic marks that I have applied in the context of the Roadmap Epigenomics project to computationally predict over 4000 epigenomic datasets vastly accelerating the coverage of the human epigenome while providing overall more robust maps than have been obtained experimentally. I will then describe a combined computational modeling and experimental approach, Sharpr-MPRA, that in high-throughput can test putative regulatory elements of interest identified based on epigenomics patterns and identify within them at high resolution bases activating or repressing gene expression. Finally, I will describe a new method, ConsHMM, also based on a multivariate HMM to annotate the human genome at single nucleotide resolution into a large number of different conservation states based on the combinatorial patterns of which species align to and which match the human reference genome within a multi-species sequence alignment.
Jason Ernst is an Assistant Professor in the Departments of Biological Chemistry and the Computer Science at UCLA. Prior to that, he was a postdoctoral fellow in Manolis Kellis’ Computational Biology Group in the Computer Science and Artificial Intelligence Laboratory at MIT and affiliated with the Broad Institute. Jason completed a PhD advised by Ziv Bar-Joseph where he was part of the Systems Biology Group, Machine Learning Department, and School of Computer Science at Carnegie Mellon University. He is a member of the editorial board at Genome Research and has been a program co-chair for the ISMB Regulatory Genomics Special Interest Group (RegGenSIG) meeting. He is a recipient of a Sloan Fellowship, NIH-Avenir Award, NSF Career Award, NSF Postdoctoral Fellowship, a Siebel Scholarship, and a Goldwater Scholarship.