Computational Regulatory Genomics - Revision history

MBeer at 01:24, 31 August 2025

2025-08-31T01:24:28Z

MBeer at 01:23, 31 August 2025

2025-08-31T01:23:38Z

MBeer at 20:06, 20 July 2024

2024-07-20T20:06:08Z

MBeer at 20:01, 20 July 2024

2024-07-20T20:01:48Z

MBeer at 02:13, 17 July 2024

2024-07-17T02:13:10Z

MBeer at 02:07, 17 July 2024

2024-07-17T02:07:07Z

MBeer at 02:04, 17 July 2024

2024-07-17T02:04:17Z

MBeer at 12:41, 4 April 2024

2024-04-04T12:41:54Z

MBeer at 14:57, 26 July 2023

2023-07-26T14:57:57Z

MBeer at 14:57, 26 July 2023

2023-07-26T14:57:18Z

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-<h2>Computational Regulatory Genomics, Gene Regulatory Networks, and Transcriptional Regulation in the Beer Lab</h2>  __NOTOC__  __NOTITLE__
+<h2>Computational Regulatory Genomics, Gene Regulatory Networks, and Transcriptional Regulation <br> Beer Lab</h2>  __NOTOC__  __NOTITLE__
 <metadesc>Computational Regulatory Genomics, Gene Regulatory Networks, and Transcriptional Regulation in the Beer Lab for graduate PhD and postdoctoral research in BME and IGM at Johns Hopkins, top ranking programs modeling DNA and systems biology.</metadesc>
 <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>

@@ Line 1: / Line 1: @@
-<h2>Computational Regulatory Genomics</h2>  __NOTOC__  __NOTITLE__
+<h2>Computational Regulatory Genomics, Gene Regulatory Networks, and Transcriptional Regulation in the Beer Lab</h2>  __NOTOC__  __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>
+<metadesc>Computational Regulatory Genomics, Gene Regulatory Networks, and Transcriptional Regulation in the Beer Lab for graduate PhD and postdoctoral research in BME and IGM at Johns Hopkins, top ranking programs modeling DNA and systems biology.</metadesc>
 <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>

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 [[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]]
-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/profiles/results/directory/profile/8377361/michael-beer Human Genetics.]'''
+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.

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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.
-<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> -->
 [[File:bmesmall.png]]

← Older revision		Revision as of 02:13, 17 July 2024
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	* '''[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://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://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.

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 * improving AI/ML methods to build more accurate models of enhancer activity
-* predicting the impact of mutations on enhancer activity and disease impact
+* 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

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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.
-We have recently made significant progress in understanding how DNA sequence features specify cell-type specific mammalian enhancer activity using machine learning, and how enhancer-gene networks control cell-fate decisions and contribute to human disease.  For details, see:
+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
@@ Line 25: / Line 25: @@
 and other [[Publications]].
-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:
+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 machine learning algorithms by including more general sequence features and constraints
+* improving AI/ML methods to build more accurate models of enhancer activity
-* predicting the impact of SNPs on enhancer activity (delta-SVM) and GWAS association for specific diseases
+* predicting the impact of mutations on enhancer activity and disease impact
-* experimentally assessing the predicted impact of regulatory element mutation in mammalian cells
+* using AI/ML models to build and test dynamic gene regulatory networks with targeted CRISPR perturbation
-* systematically determining regulatory element logic from ENCODE human and mouse data
+* 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 assess common modes of regulatory element evolution and variation
+* 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, 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>

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 * '''[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.
-* '''[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.
+* '''[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.

← Older revision		Revision as of 14:57, 26 July 2023
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	* '''[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://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://www.nature.com/articles/s41588-019-0408-9 Genome-scale screens identify JNK–JUN signaling as a barrier for pluripotency exit and endoderm differentiation. ]''' Li Q, et al. Nature Genetics 2019.

	* '''[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://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.

← Older revision		Revision as of 14:57, 26 July 2023
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	We have recently made significant progress in understanding how DNA sequence features specify cell-type specific mammalian enhancer activity using machine learning, and how enhancer-gene networks control cell-fate decisions and contribute to human disease. For details, see:		We have recently made significant progress in understanding how DNA sequence features specify cell-type specific mammalian enhancer activity using machine learning, and how enhancer-gene 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://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://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.