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:

• 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