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| − | <h1>Welcome to the Beer Lab!</h1> | + | <h3>Postdoctoral Fellowship in Computational Genomics at Johns Hopkins University </h3> |
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| − | [[File:Beer_Michael_small.jpg]] [[File:EncodeNatureGraphic_small.png]] [[File:Beer_lab_plate_art_small.jpg]]
| + | A postdoctoral position is available in the Department of Biomedical Engineering, Johns Hopkins University School of Medicine to work with Dr. Michael Beer to develop novel computational models at the forefront of regulatory genomics. Our laboratory actively analyzes and collaborates to generate functional genomic ChIP-seq, DNase-seq, and RNA-seq data to unravel the underlying DNA sequence code which specifies cell-type specific enhancer activity and the regulatory component of a wide range of human diseases. Our lab is housed in the Institute of Genetic Medicine which provides a highly collaborative and dynamic environment and opportunities to directly evaluate and inform our computational modeling of disease relevant human genetic variation. The ideal applicant should have a PhD degree and publication record in computational biology, genomics, biomedical engineering, applied mathematics or physics, or other related fields with strong quantitative training. Strong programming skills in C/C++, Python, or equivalent are required. Interested applicants should email curriculum vitae and at least two letters of recommendation to Dr. Michael Beer (mbeer@jhu.edu). Applications will be considered until the position is filled. The Johns Hopkins University is an Affirmative Action / Equal Opportunity Employer. There are no citizenship restrictions. |
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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.horizonpress.com/genomeanalysis Mammalian Enhancer Prediction.]''' Lee D, Beer MA. 2014. Genome Analysis: Current Procedures and Applications. Horizon Press
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| − | * '''[http://www.ncbi.nlm.nih.gov/pubmed/23861010 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)
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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 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.
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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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| − | [[File:bmesmall.png]]
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Postdoctoral Fellowship in Computational Genomics at Johns Hopkins University
A postdoctoral position is available in the Department of Biomedical Engineering, Johns Hopkins University School of Medicine to work with Dr. Michael Beer to develop novel computational models at the forefront of regulatory genomics. Our laboratory actively analyzes and collaborates to generate functional genomic ChIP-seq, DNase-seq, and RNA-seq data to unravel the underlying DNA sequence code which specifies cell-type specific enhancer activity and the regulatory component of a wide range of human diseases. Our lab is housed in the Institute of Genetic Medicine which provides a highly collaborative and dynamic environment and opportunities to directly evaluate and inform our computational modeling of disease relevant human genetic variation. The ideal applicant should have a PhD degree and publication record in computational biology, genomics, biomedical engineering, applied mathematics or physics, or other related fields with strong quantitative training. Strong programming skills in C/C++, Python, or equivalent are required. Interested applicants should email curriculum vitae and at least two letters of recommendation to Dr. Michael Beer (mbeer@jhu.edu). Applications will be considered until the position is filled. The Johns Hopkins University is an Affirmative Action / Equal Opportunity Employer. There are no citizenship restrictions.