After the great success of the SMODIA14 workshop (http://smodia2014.com/) we are proud to announce the
¨Statistical Methods for Omics Data Integration and Analysis 2015”, Valencia, Spain, September 14-16, 2015. SMODIA15
Host and Venue: Centro de Investigación Príncipe Felipe (CIPF)
Dr. Jason Ernst works at the University of California, California.
Dr. Jason Ernst is an Assistant Professor at UCLA in the Department of Biological Chemistry, the Computer Science Department, and the Bioinformatics Program. 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.
Source: http://www.biolchem.ucla.edu/labs/ernst/
Title of his presentation: “Computational Methods for Large-scale Detection and Dissection of Human Regulatory Elements”
Abstract: 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. While understanding the genome by directly reading the primary DNA sequence is extremely challenging, the presence of epigenetic marks on top of the sequence holds great promise to aid our understanding of the genome. Technological advances in sequencing has made it possible in a single experiment to generate tens of millions of data points on the location of a epigenetic mark across the genome in a specific cell type, which is raising a number of computational challenges and opportunities. In this talk I will first describe a method that I previously developed, ChromHMM, that learns de novo combinatorial and spatial patterns from maps of multiple epigenetic marks using a multivariate hidden Markov model. These patterns correspond to different classes of genomic elements, which I have then used to provide a cell type specific annotation of the human genome. I will then describe a combined computational modeling and experimental approach 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, 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.