WILLIAM EVAN JOHNSON
W. Evan Johnson, Ph.D., is an Associate Professor and Associate Chief of the Division of Computational Biomedicine in the Department of Medicine at Boston University. He received his Ph.D. in Biostatistics from Harvard University in 2007. The focus of his research is to develop computational and statistical approaches for the accurate determination of optimal diagnostic, prognostic, and therapeutic regimens for individual patients. His lab specializes in the development and application of statistical methodologies for the analysis of genomic data across a wide range of platforms, including next-generation sequencing technologies for gene expression and epigenetics. Over the years, he has received NIH funding for the development of novel computational tools and for the application of these tools in various fields, including cancer and infectious diseases. His group has published dozens of papers associated with the analysis and application of sequencing data, most of these through ongoing collaborations with multiple different researchers. His group has developed several user-friendly software tools for facilitating clinical and biomedical research applications, including algorithms for profiling transcription factors (MAT, MA2C), preprocessing and integrating of genomic data (ComBat, SCAN-UPC), aligning sequencing reads (GNUMAP), developing multi-gene biomarker signatures (ASSIGN), and metagenomic profiling (PathoScope, animalcules). His group has successfully applied their tools for precision genomics in several clinical scenarios, including the prediction of cancer drug efficacy in individual patients, metagenomic profiling in the airways of patients to identify colonizing pathogens commonly associated with disease development and exacerbation, and the identification of the genetic variants causing a rare but lethal X-linked disorder. Current work in his group focuses on the development of genomic biomarkers for multiple chronic diseases including TB and cancer, understating and visualizing host-microbe interactions, and the integration of data of multiple types, or from different technology platforms.