Large-scale collections of epigenomic data are a powerful source of information to annotate and understand genomes including the vast non-coding portions of them. However, to make full use of such data requires the development of novel computational methods. In this talk I will give an overview of some computational methods that we have developed for modeling and analyzing epigenomic data including ChromHMM for chromatin state modeling, ChromImpute for epigenome imputation, ChromGene for gene based epigenome annotations, and LECIF for scoring evidence of conservation at the functional genomics level.
Jason Ernst is a Professor of Biological Chemistry, Computer Science, and Computational Medicine at UCLA. Prior to joining the UCLA faculty, he was a postdoctoral fellow with Manolis Kellis in the MIT Computer Science and Artificial Intelligence Laboratory and affiliated with the Broad Institute. In 2008, Jason completed a PhD advised by Ziv Bar-Joseph in the Machine Learning Department and School of Computer Science at Carnegie Mellon University. Jason also earned BS degrees in Computer Science and Mathematics from the University of Maryland College Park. He has developed a number of widely used bioinformatics methods and software include ChromHMM for chromatin state modeling and genome annotation and STEM and DREM for the analysis of time series gene expression data. He is a member of the editorial board at Genome Research and co-chairs the steering committee for International Society of Computational Biology (ISCB) Regulatory and Systems (RegSys) Genomics Community of Special Interest Group. He is a recipient of a NSF CAREER Award, NIH-Avenir Award, Sloan Fellowship, and a John H. Walsh Young Investigator Research Prize.