The ultimate goal of our research is to understand how gene regulatory information is encoded in genomic DNA sequence. Recently progress has been made in understanding how DNA sequence features specify cell-type specific mammalian enhancer activity by using kmer-based SVM machine learning approaches.
Beer’s 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. Current focus is 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
- using this sequence based regulatory code to assess common modes of regulatory element evolution and variation
Dr. Beer’s lab is 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.
- PhD, Princeton University, 1995
- MA, Princeton University, 1991
- BSE, University of Michigan, 1989
June 15, 2015Up to one-fifth of human DNA act as dimmer switches for nearby genes, but scientists have long been unable to identify precisely which mutations in these genetic control regions really matter in causing common diseases.
October 30, 2013Dr. Michael Beer has received a five-year NIH grant to build a computational model to identify and predict regulatory elements in the human genome.