We strive to better understand how diverse members of the non-coding RNA family and their RNA binding protein partners, through their ability to modulate gene expression in the nucleus, contribute to immune pathologies in mouse models of colitis, multiple sclerosis, and cancers. Deeper understanding of how immune gene expression programs are controlled will facilitate development of new intervention strategies against inflammatory diseases.
Bioinformatics Applications in Human Disease
Bringing together Disease Research, Analytical Chemistry, Computational Methods and Drug Discovery
We have a variety of projects ranging from brain mapping to derive optimal brain atlases, integrated omic analyses to identify genetic underpinnings of the brain, to precision medicine approaches for drug response prediction and drug target identification.
We are interested in developing scalable methods for performing epidemiological analyses of large viral (primarily HIV) sequence and phylogenetic datasets. Topics of interest include large-scale phylogenetic analyses, developing novel models of sequence and tree evolution, performing epidemiological simulation experiments, and developing methods for predicting epidemic outcomes.
Our goal is to identify genes causing insulin resistance in humans in order to find new therapeutic targets for diabetes and cardiometabolic diseases. Our approach to discovery is grounded in human genetics, clarified through systematic, high throughput experimentation in human cells, and calibrated by its relevance to clinical disease. We use massively parallel genome engineering to re-create mutations identified in patients and develop high-throughput assays to interrogate function in human cell models. We apply bioinformatics and statistics to make sense of this data integrating 1) human mutations, 2) cellular function, and 3) metabolic/glycemic phenotypes of the individuals who harbor them. Using this approach, we have discovered novel missense mutations that greatly increase risk for type 2 diabetes. As a complementary aim towards precision medicine, we develop tools for clinical genome interpretation powered by high-throughput experimental data.
The main objective of the Chavez laboratory is the molecular characterization of malignant childhood cancers in order to identify drug targets and improve treatment options. Our focus is mainly on pediatric brain tumors such as medulloblastoma, glioblastoma, and ependymoma. Recently, we have demonstrated how to leverage epigenetic information such as DNA methylation and enhancer profiling in pediatric brain tumors and normal human tissues to identify clinically relevant tumor subgroups, oncogenic enhancers, transcription factors, and pathways amenable to pharmacologic targeting. To reveal regulatory circuitries disturbed in childhood brain tumors, we generate and integrate public high-dimensional data from primary tumors and patient-derived cell lines. We are specifically interested in the analysis of somatic and germline DNA mutations, chromatin and DNA modifications, transcription factor binding, and gene expression.
We are interested in the analysis and modeling of the three-dimensional chromatin structure from high-throughput sequencing experiments. We develop methods that are based in statistics, machine learning, optimization and graph theory to understand how changes in the 3D genome affect cellular outcome such as development, differentiation and gene expression. We have ongoing interests in the systems level analysis and reconstruction of regulatory networks, inference of enhancer-promoter contacts, predictive models of gene expression and integration of three-dimensional chromatin structure with one-dimensional epigenetic measurements in the context of cancer, malaria, asthma and several autoimmune diseases.