Biological Chemistry Seminar featuring Dr. Jason Ernst

  • Date: Monday, April 18, 2011
  • Location:
    BSRB 154

Monday, April 18, 2011
10:00am – 11:00am
BSRB 154

Jason Ernst, PhD
National Science Foundation Postdoctoral Fellow, MIT

Talk Title: Computational Regulatory Genomics and Epigenomics in Human, Fly, and Yeast

For more information, please contact Kelsey Martin

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
(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
(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.