Michael Beer, PhD
- Johns Hopkins School of Medicine Faculty
Languages
- English
About Michael Beer
Primary Academic Title
Professor of Biomedical Engineering
Background
Dr. Michael Beer is a professor of biomedical engineering and genetic medicine at the Johns Hopkins University School of Medicine. His research focuses on understanding how gene regulatory information is encoded in genomic DNA sequence and how regulatory variation contributes to diseases. His lab has recently developed machine-learning techniques where computer algorithms detect regulatory sequences in intergenic DNA.
Dr. Beer received his undergraduate degree from the University of Michigan. He earned his Ph.D. in astrophysical sciences from Princeton University. Dr. Beer joined the Johns Hopkins faculty in 2005.
Prior to joining Johns Hopkins, Dr. Beer was the Lewis Thomas Postdoctoral Fellow in the Department of Molecular Biology and Lewis-Sigler Institute for Integrative Genomics at Princeton University.
Dr. Beer was recognized with the Simon Ramo Award for his thesis in plasma physics. He also was awarded the DOE Fusion Energy Postdoctoral Fellowship and the National Science Foundation Graduate Fellowship, and the Searle Scholars Award for promising junior facility. He has also been recognized with the Johns Hopkins Alumni Association Excellence in Teaching Award.
Centers and Institutes
Additional Academic Titles
Professor of Genetic Medicine, Joint Appointment in Molecular Biology and Genetics, Professor of Oncology
Research Interests
Computational regulatory genomics and machine learning
Lab Website
Beer Lab - Lab Website
- The goal of research in the Beer Lab is to understand how gene regulatory information is encoded in genomic DNA sequence. Our work uses functional genomics DNase-seq, ChIP-seq, RNA-seq, and chromatin state data to computationally identify combinations of transcription factor binding sites that operate to define the activity of cell-type specific enhancers. We are currently focused on improving SVM methodology by including more general sequence features and constraints 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, and using this sequence based regulatory code to assess common modes of regulatory element evolution and variation.
Research Summary
Dr. Beer’s research focuses on understanding how gene regulatory information is encoded in genomic DNA sequence and how DNA regulatory elements control transitions between cell states in development and disease. His recent work focuses on applying these models to stem cell differentiation and cancer.
Dr. Beer's lab uses machine learning algorithms trained on DNA sequence features to detect the key combinations of transcription factor binding sites required for enhancer function and to model the impact of enhancer disruption in cellular development and cancer. Beer’s work uses functional genomics DNase-seq, ATAC-seq, TF ChIP-seq, RNA-seq, chromatin state and 3D interaction data to train models of enhancer activity.
His current research focus is on improving machine learning algorithms and building models of enhancer function through chromatin looping; predicting the impact of altered enhancer activity in developmental diseases and cancer; experimentally assessing the predicted impact of regulatory element mutation in mammalian cells; systematically determining regulatory element logic from ENCODE human and mouse data; and using this sequence based regulatory code to assess common modes of regulatory element evolution and variation.
Selected Publications
Beer MA, Shigaki D, Huangfu D. Enhancer predictions and genome-wide regulatory circuits. Annu. Rev. Genom. Hum. Genet. 2020; 21: 37-54
Lee D, Gorkin DU, Baker M, Strober BJ, Asoni AL, Beer, MA, McCallion AS. "A Method to Predict the Impact of Regulatory Variants from DNA Sequence." Nature Genetics 47 (8), 955-961. 2015
Li QV, Dixon G, Verma N, Rosen BP, Gordillo M, Luo R, Xu C, Wang Q, Soh C-L, Yang D, Crespo M, Shukla A, Xiang Q, Dundar F, Zumbo P, Witkin M, Koche R, Betel D, Chen S, Massague J, Garippa R, Evans T, Beer MA, and Huangfu D, Genome-scale screens uncover JNK/JUN signaling as a key barrier from pluripotency to human endoderm differentiation. Nature Genetics 2019; 51: 999-1010
Xi W, Beer MA. Loop competition and extrusion model predicts CTCF interaction specificity. Nature Comms. 2020 Feb;12(1):1046
Yue F, Cheng Y, Breschi A, Vierstra J, Wu W, Ryba T, Sandstorm R, Z Ma, et al. "A Comparative Encyclopedia of DNA Elements in the Mouse Genome." Nature 515 (7527), 355-364. 2014
Honors
- Lewis Thomas Postdoctoral Fellowship
- Fusion Energy Postdoctoral Fellowship, DOE
- Graduate Fellowship, National Science Foundation
- Searle Scholar Award
- Johns Hopkins Alumni Association Teaching Award
- Simon Ramo Award (now named the Marshall N. Rosenbluth Outstanding Doctoral Thesis in Plasma Physics Award), 1/1/96
Graduate Program Affiliations
Preceptor-Predoctoral Training Program in Human Genetics
Additional Training
Princeton University, Princeton, NJ, 2005, Integrative Genomics