W. Evan Johnson Lab
To develop high quality cutting edge computational algorithms for applications in personalized genomic medicine.
The development of personalized treatment regimes is an active area of current research in genomics. The focus of our research is to investigate core biological components that contribute to disease prognosis, development, and early detection and to develop latent variable models to accurately determine optimal therapeutic regimens for individual patients. Because biological processes do not act in isolation but as parts of complex interactive systems, we are computationally evaluating interactions between these systems at multiple levels. At the sequence and cellular level, we have developed latent variable models for probabilistically determining gene expression profiles that are linked to individual response to treatment. In addition, we are experimentally perturbing subcomponents of larger biological systems or pathways and linking pathway activation status to genet! ic disease risk and drug sensitivity.
Our lab’s research consists of the development of methods for analyzing a variety of genome-wide data types, currently focusing on the analysis of data from next-generation sequencing (NGS) experiments. We are developing a comprehensive and coordinated set of statistical methods for NGS data analysis and data integration that directly address many important problems in epigenetics and translational medicine. There is a great need for biologically motivated and mathematically justified methods able to efficiently handle the massive data sets generated by high-throughput experiments. Our goal is to conduct cutting-edge research, making an impact in genomics and translational science while developing straightforward computational tools to facilitate others conducting similar work. In order to ensure the methods being developed are appropriate and useful, we are aggressively working to establish and maintain strong collaborations with applied scientists in genomics and translational research. We are firmly of the opinion that the research conducted in our lab is timely, of high importance, and relevant to the current needs in these fields.
- Breast cancer susceptibility
- GNUMAP project: Probabilistic mapping of next-generation sequencing data and applications
- Whole exome sequencing data analysis (applications in breast cancer, autism, ADHD, and rare genetic disorders)
- Analysis of DNA methylation data from multiple platforms
- Rapid diagnosis of infectious diseases