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| − | <h1>Welcome to the Beer Lab!</h1>
| + | ==PI== |
| | + | [[Users:Mbeer|Mike Beer (with short hair)]] |
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| − | [[File:Beer_Michael_small.jpg]] [[File:EncodeNatureGraphic_small.png]] [[File:Beer_lab_plate_art_small.jpg]] | + | [[File:Beer_m.gif]] [[File:group_pic.jpg|350px]] |
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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.
| + | ==Postdocs== |
| − | 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:
| + | * Ayoti Patra |
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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 (in press) | + | ==Graduate students== |
| | + | * Justin Shigaki |
| | + | * Wang Xi |
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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) | + | ==Current Undergraduates== |
| | + | * Nico Eng |
| | + | * Jin-woo Oh |
| | + | * Michael Mudgett |
| | + | * Felix Yu |
| | + | * Amy Xiao |
| | + | * Ganesh Avrapalli |
| | + | * Sunny Thodupunuri |
| | + | * Richard Liu |
| | + | * Gianluca Silva Croso |
| | + | * Zachary Heiman |
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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. | + | ==Former graduate students== |
| | + | * Paul Michel (summer MD genomics rotation) |
| | + | * [https://scholar.google.com/citations?user=7oyAkKkAAAAJ&hl=en Dongwon Lee] |
| | + | * Kipper Fletez-Brant (Hansen Lab, JHU) |
| | + | * Mahmoud Ghandi (now at Broad Institute) |
| | + | * [http://www.genebrew.com Rahul Karnik] (now at Broad Institute) |
| | + | * Jun Kyu Rhee (now at Korea Institute of Science and Technology) |
| | + | * Donavan Cheng (now Director of Oncology Bioinformatics and Data Sciences at Illumina) |
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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. | + | ==Former Undergraduates== |
| − | | + | * Kendrick Hougen |
| − | * '''[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. | + | * Nole Lin |
| − | | + | * Ashutosh Jindal |
| − | 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:
| + | * Kyle Xiong |
| − | | + | * Ben Strober |
| − | * improving SVM methodology by including more general sequence features and constraints | + | * Alessandro Asoni |
| − | * predicting the impact of SNPs on enhancer activity (delta-SVM) and GWAS association for specific diseases | + | * Billy Kang |
| − | * experimentally assessing the predicted impact of regulatory element mutation in mammalian cells | + | * John Lee |
| − | * systematically determining regulatory element logic from ENCODE human and mouse data | + | * Andrew Pao |
| − | * using this sequence based regulatory code to assess common modes of regulatory element evolution and variation | + | * Tuo Li |
| − | | + | * Peter Li |
| − | 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.
| + | * Juinting Chiang |
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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>[[Visit Some Computational Labs at Hopkins http://karchinlab.org/bme-compbio-jhu/]]</h3>
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| − | [[File:bmesmall.png]]
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