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| + | ==PI== |
| − | <metadesc>Computational Regulatory Genomics for graduate PhD and postdoctoral research in BME and IGM at Johns Hopkins, top ranking programs modeling DNA and systems biology.</metadesc>
| + | [[Users:Mbeer|Mike Beer (with short hair)]] |
| − | <!-- <h2>Computational Regulatory Genomics</h2> -->
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| − | <h3>[[Recent News ]] [[Lab Members ]] [[Publications]] [[Postdoctoral Positions Available]] [[Resources]]</h3>
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| − | [[File:Beer_Michael_small.jpg|link="http://www.bme.jhu.edu/people/primary.php?id=384"]] [[File:EncodeNatureGraphic_small.png]] [[File:dyn_net.gif]] | + | [[File:Beer_m.gif]] [[File:group_pic.jpg|350px]] |
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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/michael-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 '''[https://igm.jhmi.edu/faculty/michael-beer Human Genetics.]'''
| + | ==Postdocs== |
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| + | * Ayoti Patra |
| − | <h3>Research Interests: </h3> The ultimate goal of our research is to understand how gene regulatory information is encoded in genomic DNA sequence.
| + | [[File:Ayoti.png|80x160]] |
| − | 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.nature.com/ng/journal/vaop/ncurrent/full/ng.3331.html A method to predict the impact of regulatory variants from DNA sequence.]''' Lee D, Gorkin DU, Baker M, Strober BJ, Asoni AL, McCallion AS, Beer, MA. Nature Genetics 2015. | + | ==Graduate students== |
| | + | * Dustin Shigaki |
| | + | * Wang Xi |
| | + | * Jin-Woo Oh |
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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. | + | ==Current Undergraduates== |
| | + | * Richard Liu |
| | + | * Alex Chang |
| | + | * James Chen |
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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 | + | ==Former graduate students== |
| | + | * Paul Michel (summer MD genomics rotation) |
| | + | * [https://scholar.google.com/citations?user=7oyAkKkAAAAJ&hl=en Dongwon Lee] (now at Boston Children's Hospital and Harvard Medical School) |
| | + | * Kipper Fletez-Brant (Hansen Lab, JHU) |
| | + | * Mahmoud Ghandi (now at Third Rock Ventures Newco) |
| | + | * [http://www.genebrew.com Rahul Karnik] (now Principal Scientist at Omega Therapeutics) |
| | + | * Jun Kyu Rhee (now at Korea Institute of Science and Technology) |
| | + | * Donavan Cheng (now Director, Population Genomics at Illumina) |
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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.
| + | ==Former Undergraduates== |
| − | | + | * Nico Eng |
| − | * '''[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. | + | * Michael Mudgett |
| − | | + | * Felix Yu |
| − | * '''[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. | + | * Amy Xiao |
| − | | + | * Sunny Thodupunuri |
| − | * '''[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. | + | * Tianyue Ou |
| − | | + | * Ganesh Arvapalli |
| − | 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:
| + | * Zachary Heiman |
| − | | + | * Gianluca Silva Croso |
| − | * improving on the SVM methodology by including more general sequence features and constraints | + | * Kendrick Hougen |
| − | * predicting the impact of SNPs on enhancer activity (delta-SVM) and GWAS association for specific diseases | + | * Nole Lin |
| − | * experimentally assessing the predicted impact of regulatory element mutation in mammalian cells | + | * Ashutosh Jindal |
| − | * systematically determining regulatory element logic from ENCODE human and mouse data | + | * Kyle Xiong |
| − | * using this sequence based regulatory code to assess common modes of regulatory element evolution and variation | + | * Ben Strober |
| − | | + | * Alessandro Asoni |
| − | 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.
| + | * Billy Kang |
| − | | + | * John Lee |
| − | | + | * Andrew Pao |
| − | <h3>About Computational Biology in JHU Biomedical Engineering:</h3>
| + | * Tuo Li |
| − | 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.
| + | * Peter Li |
| − | | + | * Juinting Chiang |
| − | <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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