Difference between revisions of "Computational Regulatory Genomics"

Latest revision as of 20:06, 20 July 2024

Computational Regulatory Genomics

Recent News Lab Members Publications Postdoctoral Positions Available Resources

We are in the Department of Biomedical Engineering and the McKusick-Nathans Department of Genetic Medicine at Johns Hopkins University. You can apply for graduate study in my lab through BME or the Ph.D. program in Human Genetics and Genomics.

Research Interests:

The ultimate goal of our research is to understand how gene regulatory information is encoded in genomic DNA sequence.

We have recently made significant progress using AI/ML to understand how DNA sequence specifies cell-type specific enhancer activity, target gene activation through looping constraints, and how dynamic enhancer-gene regulatory networks control cell-fate decisions and contribute to human disease. For details, see:

Dynamic network-guided CRISPRi screen identifies CTCF-loop-constrained nonlinear enhancer gene regulatory activity during cell state transitions. Luo R, et al. Nature Genetics 2023

Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer. Razavi-Mohseni M, et al. Genome Research 2024

Enhancer Predictions and Genome-Wide Regulatory Circuits. Beer MA, Shigaki D, Huangfu D. Ann. Rev. Genomics and Human Genetics 2020.

Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay. Shigaki D, et al. Human Mutation 2019.

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.

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.

and other Publications.

Our work uses functional genomics DNase-seq, ATAC-seq, ChIP-seq, RNA-seq, CRISPR, MPRA, Hi-C, and chromatin state data to computationally identify combinations of transcription factor binding sites which operate to define the activity of cell-type specific enhancers and their activation of target genes. We are currently focused on:

improving AI/ML methods to build more accurate models of enhancer activity

predicting the impact of mutations on enhancer activity and their contributions to disease

using AI/ML models to build and test dynamic gene regulatory networks with targeted CRISPR perturbation

using large scale datasets (ENCODE/IGVF) and CRISPR to constrain models of enhancer-promoter interactions and DNA looping mechanisms

using this sequence based regulatory code to understand mechanisms of gene regulatory network disruption in cancer and develop therapeutic strategies

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 Genomics, Bioinformatics, and Computational Biology at Johns Hopkins, including research in Systems Biology, AI, Machine Learning, and Dynamic Gene Regulatory Network Modeling.

About Computational Biology in JHU Biomedical Engineering:

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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+<h2>Computational Regulatory Genomics</h2>  __NOTOC__  __NOTITLE__
-__NOTITLE__
 <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>
-<!-- <h2>Computational Regulatory Genomics</h2> -->
 <h3>[[Recent News  ]]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[Lab Members  ]]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  [[Publications]]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [[Postdoctoral Positions Available]]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [[Resources]]</h3>
-[[File:Beer_Michael_small.jpg|link=http://www.bme.jhu.edu/people/primary]] [[File:EncodeNatureGraphic_small.png]] [[File:dyn_net.gif]]
+[[File:Beer_Michael_small.jpg|link=http://www.bme.jhu.edu/people/faculty/michael-beer/]] [[File:EncodeNatureGraphic_small.png]] [[File:dyn_net.gif]]
-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.]'''
+[[File:Igvf_logo.png|150px]] &nbsp;&nbsp; [[File:Encode_logo.png]] &nbsp;&nbsp; [[File:Nhgri_logo.jpg|250px]] &nbsp;&nbsp;[[File:hopkins_logo.jpg|200px]]
-<h3>Research Interests: </h3> The ultimate goal of our research is to understand how gene regulatory information is encoded in genomic DNA sequence.
-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:
-* '''[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.
+We are in the '''[http://www.bme.jhu.edu/people/faculty/michael-beer/ Department of Biomedical Engineering]''' and the '''[https://www.hopkinsmedicine.org/profiles/results/directory/profile/8377361/michael-beer McKusick-Nathans Department of Genetic Medicine]''' at Johns Hopkins University.  You can apply for graduate study in my lab through '''[http://www.bme.jhu.edu/people/faculty/michael-beer/ BME]''' or the Ph.D. program in '''[https://www.hopkinsmedicine.org/genetic-medicine/education-training/human-genetics-genomics-graduate-program Human Genetics and Genomics.]'''
+<h3>Research Interests: </h3> The ultimate goal of our research is to understand how gene regulatory information is encoded in genomic DNA sequence.
+We have recently made significant progress using AI/ML to understand how DNA sequence specifies cell-type specific enhancer activity, target gene activation through looping constraints, and how dynamic enhancer-gene regulatory networks control cell-fate decisions and contribute to human disease.  For details, see:
+* '''[https://rdcu.be/dhCoG Dynamic network-guided CRISPRi screen identifies CTCF-loop-constrained nonlinear enhancer gene regulatory activity during cell state transitions.]''' Luo R, et al. Nature Genetics 2023
+* '''[https://genome.cshlp.org/content/34/5/680 Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer.]''' Razavi-Mohseni M, et al. Genome Research 2024
+* '''[https://www.annualreviews.org/doi/abs/10.1146/annurev-genom-121719-010946?journalCode=genom Enhancer Predictions and Genome-Wide Regulatory Circuits.]''' Beer MA, Shigaki D, Huangfu D.  Ann. Rev. Genomics and Human Genetics 2020.
+* '''[https://onlinelibrary.wiley.com/doi/abs/10.1002/humu.23797 Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay.]''' Shigaki D, et al. Human Mutation 2019.
+* '''[https://www.nature.com/articles/ng.3331 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.
 * '''[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://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
+and other [[Publications]].
-* '''[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.
+Our work uses functional genomics DNase-seq, ATAC-seq, ChIP-seq, RNA-seq, CRISPR, MPRA, Hi-C, and chromatin state data to computationally identify combinations of transcription factor binding sites which operate to define the activity of cell-type specific enhancers and their activation of target genes.  We are currently focused on:
-* '''[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.
+* improving AI/ML methods to build more accurate models of enhancer activity
-* '''[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.
+* predicting the impact of mutations on enhancer activity and their contributions to disease
-* '''[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.
+* using AI/ML models to build and test dynamic gene regulatory networks with targeted CRISPR perturbation
-Our work uses functional genomics DNase-seq, ATAC-seq, ChIP-seq, RNA-seq, MPRA, Hi-C, 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:
+* using large scale datasets (ENCODE/IGVF) and CRISPR to constrain models of enhancer-promoter interactions and DNA looping mechanisms
-* improving on the SVM methodology by including more general sequence features and constraints
+* using this sequence based regulatory code to understand mechanisms of gene regulatory network disruption in cancer and develop therapeutic strategies
-* 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 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.
+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 Genomics,  Bioinformatics, and Computational Biology at Johns Hopkins, including research in Systems Biology, AI, Machine Learning, and Dynamic Gene Regulatory Network Modeling.
+<h3>About Computational Biology in JHU Biomedical Engineering:</h3>
-<h3>About Computational Biology in JHU Biomedical Engineering:</h3>
 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.
-<h3>[http://karchinlab.org/bme-compbio-jhu Visit Some Computational Labs at Johns Hopkins]</h3>
+<!--<h3>[http://karchinlab.org/bme-compbio-jhu Visit Some Computational Labs at Johns Hopkins]</h3>
-<h3>[http://ccb.jhu.edu/ Center for Computational Biology at Johns Hopkins]</h3>
+<h3>[http://ccb.jhu.edu/ Center for Computational Biology at Johns Hopkins]</h3> -->
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