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.
Bioinformatics Applications in Human Disease
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.
The lab has a variety of bioinformatics projects aimed at improving understanding of the functional impact of autism risk mutations derived from exome and whole genome sequencing of the patients. We created mouse models carrying some of these mutations using CRISPR/Cas9, and also produced patient-derived cerebral organoids with autism risk mutations. We performed bulk RNA-seq from various brain regions or time periods in these models. Gene-level analyses of RNA-seq data has been completed (manuscripts in preparation). We are now pursuing isoform-level analyses of these data to better understand functional impact of autism risk mutations on splicing isoform transcriptome.
Our research focuses on molecular engineering for cellular imaging and reprogramming, and image-based bioinformatics, with applications in stem cell differentiation and cancer treatment.