Michael A. Beer, Ph.D., M.A.

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.