Jason Ernst


Advances in high-throughput technologies such as DNA sequencing are enabling the generation of massive amounts of biological data. This data is providing unprecedented opportunities to gain a systematic understanding of the genome of organisms and the regulation of genes encoded in them, but calls for new computational approaches for its analysis.


To address these challenges, I have developed computational methods for genome interpretation and for understanding gene regulation. (1) I developed a clustering method, STEM, for the analysis of short time series gene expression data, and initially applied it to data on immune response in human. STEM has since become a widely used method in many species and contexts. (2) I developed DREM, a method for integrating time series gene expression data with transcription factor-gene interactions, which reveals gene regulation temporal dynamics, which I applied originally in yeast and most recently in the context of the Drosophila modENCODE project. (3) I developed a method for predicting targets of transcription factors across the human genome by integrating sequence, annotation, and chromatin features, given the increasing availability of epigenetic information on chromatin modifications. (4) To exploit epigenomic information more systematically, I developed an algorithm for discovering and characterizing biologically-significant combinations of chromatin modifications across a genome, or 'chromatin states', based on their recurring patterns across the genome. (5) I used these chromatin states to study the dynamics of epigenetic changes across nine cell types in the context of the human ENCODE project, revealing a dynamic epigenomic landscape, that reveals causal regulators for cell type-specific enhancers, and provides new insights for interpreting disease-associated SNPs from genome-wide association studies (GWAS).


These methods provide a systematic way to discern regulatory information amidst the vast non-coding space of the human genome, towards a systematic understanding of gene regulation in the context of health and disease.


Jason Ernst is a NSF Postdoctoral Fellow in Manolis Kellis' group, and also collaborating closely with Bradley Bernstein. His PhD was advised by Ziv Bar-Joseph. Previously he was part of the Systems Biology Group, Machine Learning Department, and School of Computer Science at Carnegie Mellon University.  His research interests involve applying machine learning methods to high-throughput biological data to better understand gene regulation, the epigenome, and the causes of human disease.


B17 Upson Hall

Thursday, February 3, 2011

Refreshments at 3:45pm in the Upson 4th Floor Atrium


Computer Science


Spring 2011


Computational Regulatory Genomics and Epigenomics in Human, Fly, and Yeast