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

• Mammalian Enhancer Prediction. Lee D, Beer MA. 2014. Genome Analysis: Current Procedures and Applications. Horizon Press (in press)

• Robust k-mer Frequency Estimation Using Gapped k-mers. Ghandi M, Mohammad-Noori M, and Beer MA. 2013. Journal of Mathematical Biology. (Epub ahead of print)

• kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic datasets. Fletez-Brant C*, Lee D*, McCallion AS and Beer MA. 2013. Nucleic Acids Research 41: W544–W556.

• Integration of ChIP-seq and Machine Learning Reveals Enhancers and a Predictive Regulatory Sequence Vocabulary in Melanocytes. Gorkin DU, Lee D, Reed X, Fletez-Brant C, Blessling SL, Loftus SK, Beer MA, Pavan WJ, and McCallion AS. 2012. Genome Research 22:2290-2301.

• Discriminative prediction of mammalian enhancers from DNA sequence. Lee D, Karchin R, and Beer MA. 2011. Genome Research 21:2167-2180.

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 the activity of a set of cell-type specific enhancers. We are currently focused on:

• improving this methodology by including more diverse constraints and features
• predicting the impact of SNPs on enhancer activity (delta-SVM) and GWAS disease association
• experimentally characterizing the predicted impact of regulatory element mutation in mammalian cells
• systematically determining regulatory elements from ENCODE human and mouse data
• using the inferred regulatory code to assess common modes of regulatory element evolution and variation