David Haussler
Research Projects
David Haussler develops new statistical and algorithmic methods to explore the molecular evolution of the human genome, integrating cross-species comparative and high-throughput genomics data to study gene structure, function, and regulation. He focuses on computational analysis and classification of DNA, RNA, and protein sequences.
He leads the Genome Bioinformatics Group, which participates in the public consortium efforts to produce, assemble, and annotate the first mammalian genomes. His group designed and built the program that assembled the first working draft of the human genome sequence from information produced by sequencing centers worldwide and participated in the informatics associated with the finishing effort.
Recent findings from the Haussler laboratory include the existence of "ultraconserved" elements in the human genome that have remained essentially unchanged across millions of years of evolution Science, May 6, 2004, suggesting important function. The Haussler group has also identified genome segments that have undergone unusually rapid change in one species, such as a gene linked to brain development that has changed dramatically between chimpanzees and humans Nature, Aug 16, 2006.
UCSC Genome Browser
The UCSC Genome Browser provides interactive exploration of metazoan genome sequences. The browser fuses multiple kinds of genome-wide annotation in a web-based "genome microscope." The genomes are annotated based on high-throughput experimental projects, bioinformatics, and large human-curated data sets. These projects receive funding from the Howard Hughes Medical Institute (HHMI), the National Human Genome Research Institute (NHGRI), the National Cancer Institute (NCI), and the California Institute for Quantitative Biosciences (QB3).
UCSC Cancer Genomics Browser
The UCSC Cancer Genomics Browser provides a new way to visualize and analyze data from studies aimed at improving cancer treatment by unraveling the complex genetic roots of the disease. The browser consists of a suite of web-based tools designed to help researchers find patterns in the huge amounts of clinical and genomic data being gathered in large-scale cancer studies. Medical researchers hope to identify genetic signatures and other "biomarkers" in cancer cells that can be used to predict how individual patients will respond to different therapies throughout the course of their treatment. This project received funding from the I-SPY consortium, the TCGA consortium, the California Institute for Quantitative Biosciences (QB3), and the National Institutes of Health.
ENCODE
The ENCODE project is a scientific reconnaissance mission aimed at discovering all parts of the human genome that are crucial to biological function. The project aims to discover and fully annotate all the functional elements in these regions. The UCSC group is providing bioinformatics analysis via extensions of the UCSC Genome Browser. The NHGRI sponsors this project.
Genome 10K
The Genome 10K project aims to collect genome sequences from a fully representative set of more than 10,000 genomes, capturing much of the genomic diversity of vertebrate species. Haussler's lab is heading up the computational effort to analyze these data. The challenge is to determine the evolutionary history of each segment of DNA in the living vertebrates, down to individual bases, as they descended from the genomes of the ancestral species.
GENCODE/CCDS
A critical next step in the development of the human genome as a foundation for biomedical research is the completion of a high quality set of gene annotations. With this goal in mind, the UCSC group works with several other groups internationally to combine experimental and computational methods to define genes, and maintain a gold standard set of human and mouse gene annotations. The NHGRI funds this project.
Translational research to validate computational predictions
Research by the Genome Bioinformatics Group generates an increasing number of very specific hypotheses about the evolution and function of human genes. Through wet-lab experiments, we explore and validate predictions generated from computational genomic research. Research project areas include genome evolution, comparative genomics, alternative splicing, protein structure, and functional genomics. The HHMI funds this work.
Early research interests
His current research stems from his early work in machine learning, statistical decision theory, pattern recognition, neural networks, algorithms, and complexity.
