Computational Regulatory Genomics

Welcome to the Beer Lab!

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 the DNA sequence features which control cell-type specific mammalian enhancer activity by using kmer-based SVM machine learning approaches. For details, see:

This 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 a set of cell-specific enhancers. We are currently focused on: 1) improving this methodology by including more diverse constraints and features 2) predicting the impact of SNPs on enhancer activity (delta-SVM) and GWAS disease association 3) experimentally characterizing the predicted impact of regulatory element mutation in mammalian cells 4) systematically determining regulatory elements from ENCODE human and mouse data 5) using the inferred regulatory code to assess common modes of regulatory element evolution and variation