Difference between revisions of "Computational Regulatory Genomics"

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<h1>Welcome to the Beer Lab!</h1>
 
<h1>Welcome to the Beer Lab!</h1>
  
[[File:Beer_lab_plate_art.jpg]]   [[File:Beer_Michael.jpg‎]]
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[[File:Beer_Michael.jpg‎]] [[File:Beer_lab_plate_art_small.jpg]]
  
 
<h3>Research Interests: </h3> The ultimate goal of our research is to understand how genomic DNA sequence specifies gene regulation.  
 
<h3>Research Interests: </h3> The ultimate goal of our research is to understand how genomic DNA sequence specifies gene regulation.  

Revision as of 00:06, 2 December 2013

Welcome to the Beer Lab!

Beer Michael.jpg Beer lab plate art small.jpg

Research Interests:

The ultimate goal of our research is to understand how genomic DNA sequence specifies gene regulation.

We have recently made significant progress in understanding how DNA sequence features control cell-type specific mammalian enhancer activity by using kmer-based SVM machine learning approaches. For details, see:

Our work uses functional genomics DNase-seq, ChIP-seq, RNA-seq, and chromatin state data to computationally identify combinations of transcription factor binding sites which operate to define the activity of cell-type specific enhancers. We are currently focused on:

  • improving this methodology by including more general sequence features and constraints
  • predicting the impact of SNPs on enhancer activity (delta-SVM) and GWAS association for specific diseases
  • experimentally assessing the predicted impact of regulatory element mutation in mammalian cells
  • systematically determining regulatory element logic from ENCODE human and mouse data
  • using this sequence based regulatory code to assess common modes of regulatory element evolution and variation

We are located in the McKusick-Nathans Institute for Genetic Medicine, and the Department of Biomedical Engineering, which has long been a leader in the development of rigorous quantitative modeling of biological systems, and is a natural home for graduate studies in Bioinformatics and Computational Biology at Johns Hopkins, including research in Genomics, Systems Biology, Machine Learning, and Network Modeling.

Lab Members

Publications