Difference between revisions of "Computational Regulatory Genomics"
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* '''[http://www.ncbi.nlm.nih.gov/pubmed/21875935 Discriminative prediction of mammalian enhancers from DNA sequence.]''' Lee D, Karchin R, and Beer MA. 2011. Genome Research 21:2167-2180. | * '''[http://www.ncbi.nlm.nih.gov/pubmed/21875935 Discriminative prediction of mammalian enhancers from DNA sequence.]''' Lee D, Karchin R, and Beer MA. 2011. Genome Research 21:2167-2180. | ||
| − | 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 | + | 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 | * improving this methodology by including more general sequence features and constraints | ||
Revision as of 23:58, 1 December 2013
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.
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.