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| + | * [http://www.ncbi.nlm.nih.gov/pubmed/21875935 Discriminative prediction of mammalian enhancers from DNA sequence. Lee D, Karchin R, Beer MA. Genome Res. Epub 2011 Aug 29] |
| − | <meta name="description" content="Computational Regulatory Genomics at Johns Hopkins University for graduate PhD and postdoctoral research in the Department of Biomedical Engineering and Human Genetics, top ranking programs in quantitative modeling of regulatory DNA using machine learning. ">
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| + | * [http://www.ncbi.nlm.nih.gov/pubmed/19253296 Identification of miR-21 targets in breast cancer cells using a quantitative proteomic approach. Yang Y, Chaerkady R, Beer MA, Mendell JT, Pandey A. Proteomics. 2009 Mar;9(5):1374-84] |
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| − | <h1>Beer Lab: Computational Regulatory Genomics</h1>
| + | * [http://www.ncbi.nlm.nih.gov/pubmed/19211792 Lin-28B transactivation is necessary for Myc-mediated let-7 repression and proliferation. Chang TC, Zeitels LR, Hwang HW, Chivukula RR, Wentzel EA, Dews M, Jung J, Gao P, Dang CV, Beer MA, Thomas-Tikhonenko A, Mendell JT. Proc Natl Acad Sci U S A. 2009 Mar 3;106(9):3384-9. Epub 2009 Feb 11.] |
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| − | [[File:Beer_Michael_small.jpg]] [[File:EncodeNatureGraphic_small.png]] [[File:Beer_lab_plate_art_small.jpg]] | + | * [http://www.ncbi.nlm.nih.gov/pubmed/18071029 Metrics of sequence constraint overlook regulatory sequences in an exhaustive analysis at phox2b. McGaughey DM, Vinton RM, Huynh J, Al-Saif A, Beer MA, McCallion AS. Genome Res. 2008 Feb;18(2):252-60. Epub 2007 Dec 10] |
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| − | We are in the '''[http://www.bme.jhu.edu/people/primary.php?id=384 Department of Biomedical Engineering]''' and the '''[https://igm.jhmi.edu/faculty/mike-beer McKusick-Nathans Institute of Genetic Medicine]''' at Johns Hopkins University. You can apply for graduate study in my lab through '''[http://www.bme.jhu.edu/people/primary.php?id=384 BME]''' or the Ph.D. program in '''[http://humangenetics.jhmi.edu/index.php/faculty/michael-beer.html Human Genetics.]'''
| + | * [http://www.ncbi.nlm.nih.gov/pubmed/17540599 Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis. Chang TC, Wentzel EA, Kent OA, Ramachandran K, Mullendore M, Lee KH, Feldmann G, Yamakuchi M, Ferlito M, Lowenstein CJ, Arking DE, Beer MA, Maitra A, Mendell JT. Mol Cell. 2007 Jun 8;26(5):745-52. Epub 2007 May 31] |
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| − | <h3>Research Interests: </h3> The ultimate goal of our research is to understand how gene regulatory information is encoded in genomic DNA sequence.
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| − | We have recently made significant progress in understanding how DNA sequence features specify cell-type specific mammalian enhancer activity by using kmer-based SVM machine learning approaches. For details, see:
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| − | * '''[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003711 Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features.]''' Ghandi M, Lee D, Mohammad-Noori M, and Beer MA. 2014. PLOS Computational Biology. July 17, 2014. | + | * [http://www.ncbi.nlm.nih.gov/pubmed/15870260 Functional characterization of a novel Ku70/80 pause site at the H19/Igf2 imprinting control region. D. J. Katz, M. A. Beer, J. M. Levorse and S. M. Tilghman, Mol Cell Biol 25, p3855-3863 (2005).] |
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| − | * '''[http://scholar.google.com/citations?view_op=view_citation&hl=en&user=9aH8_eEAAAAJ&sortby=pubdate&citation_for_view=9aH8_eEAAAAJ:1sJd4Hv_s6UC Mammalian Enhancer Prediction.]''' Lee D, Beer MA. 2014. Genome Analysis: Current Procedures and Applications. Horizon Press | + | * [http://www.ncbi.nlm.nih.gov/pubmed/14672978 Whole-genome discovery of transcription factor finding sites by network-level conservation. M. Pritsker, Y. C. Liu, M. A. Beer, and S. Tavazoie, Genome Res. 2004 Jan;14(1):99-108. Epub 2003 Dec 12.] |
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| − | * '''[http://scholar.google.com/citations?view_op=view_citation&hl=en&user=9aH8_eEAAAAJ&sortby=pubdate&citation_for_view=9aH8_eEAAAAJ:NhqRSupF_l8C Robust k-mer Frequency Estimation Using Gapped k-mers.]''' Ghandi M, Mohammad-Noori M, and Beer MA. 2013. Journal of Mathematical Biology 69:469-500. | + | * [http://www.ncbi.nlm.nih.gov/pubmed/15084257 Predicting Gene Expression from Sequence. M. A. Beer and S. Tavazoie, Cell 117, p185-198 (2004)] |
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| − | * '''[http://www.ncbi.nlm.nih.gov/pubmed/23771147 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.
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| − | * '''[http://www.ncbi.nlm.nih.gov/pubmed/23019145 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.
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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.
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| − | 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:
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| − | * improving SVM methodology by including more general sequence features and constraints
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| − | * predicting the impact of SNPs on enhancer activity (delta-SVM) and GWAS association for specific diseases
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| − | * experimentally assessing the predicted impact of regulatory element mutation in mammalian cells
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| − | * systematically determining regulatory element logic from ENCODE human and mouse data
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| − | * using this sequence based regulatory code to assess common modes of regulatory element evolution and variation
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| − | We are located in the McKusick-Nathans Institute of 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.
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| − | <h3>[[Recent News]]</h3>
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| − | <h3>[[Lab Members]]</h3>
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| − | <h3>[[Publications]]</h3>
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| − | <h3>[[Postdoctoral Positions Available]]</h3>
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| − | <h3>About Computational Biology in JHU Biomedical Engineering:</h3>
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| − | The Department of Biomedical Engineering 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. Students with backgrounds in Physics, Mathematics, Computer Science and Engineering are encouraged to apply. Opportunities for research include: Computational Medicine, Genomics, Systems Biology, Machine Learning, and Network Modeling. Graduate students in Johns Hopkins' Biomedical Engineering programs can select research advisors from throughout Johns Hopkins' Medical Institutions, Whiting School of Engineering, and Krieger School of Arts and Sciences.
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| − | <h3>[http://karchinlab.org/bme-compbio-jhu Visit Some Computational Labs at Johns Hopkins]</h3>
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| − | <h3>[http://ccb.jhu.edu/ Center for Computational Biology at Johns Hopkins]</h3>
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