David Haussler Research Group

The Computational Genomics / Haussler Lab develops statistical and algorithmic methods to analyze genome, transcriptome, and comparative genomics data to study gene regulation and molecular evolution. Researchers apply hidden Markov models, stochastic grammars, and discriminative kernel methods to detect genomic patterns and cancer biomarkers across genomes. The lab uses CRISPR, human cerebral cortex organoids, and single-cell RNA-seq to functionally characterize neurodevelopmental genes altered in human evolution. They build and maintain web tools (UCSC Genome Browser, Xena) for large-scale genomic data sharing and comparative analysis across species.