David Haussler

David Haussler

Personal genomics

Soon scientists and clinicians will use new DNA technologies to detect mutations driving cancer and other diseases, identify new strains of pathogens, track subtle changes in our immune repertoire, predict drug response, and make innumerable other contributions to our health. The scale and complexity of the data will vastly exceed anything the medical community has faced before. David Haussler's group at UC Santa Cruz is tackling this challenge by applying advanced engineering and new computer algorithms to revolutionize medicine through deeper, ubiquitous use of DNA information.

• UCSC: 27 Years of Leadership in Genomics

UCSC Genome Browser

Haussler’s group assembled and posted the first working draft of the human genome on the Internet, resulting in what is now an essential tool for biomedical research, the UCSC Genome Browser. A decade after the human genome went on the web, his group produced the UCSC Cancer Genomics Browser, 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 UCSC Genome Browser serves as the platform for several large-scale genomics projects, including NHGRI’s ENCODE project to use omics methods to explore the function of every base in the human genome (for which UCSC serves as the Data Coordination Center), NIH’s Mammalian Gene Collection, and NHGRI’s 1000 genomes project to explore human genetic variation.and the Genome 10K project, co-founded by Haussler to assemble a genomic zoo—a collection of DNA sequences representing the genomes of 10,000 vertebrate species. A new project in this area is to adapt the UCSC Genome Browser to display and analyze stem cell data.

Cancer genomics at UCSC

The Haussler lab's informatics work on cancer genomics, including the UCSC Cancer Genomics Browser, provides a complete analysis pipeline from raw DNA reads through the detection and interpretation of mutations and altered gene expression in tumor samples. Haussler's group collaborates with researchers at medical centers nationally, including members of the Stand Up To Cancer “Dream Teams” and the Cancer Genome Atlas (TCGA), to discover molecular causes of cancer and pioneer a new personalized, genomics-based approach to cancer treatment.

The UCSC Cancer Genomics Hub (CGHub), a product of the Haussler lab, is a secure repository for storing, cataloging, and accessing cancer genome sequences, alignments, and mutation information from TCGA—a pioneering project involving more than 20 cancer types, from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project—which focuses on the five most severe childhood cancers, and from other related projects. The current planned capacity of this data center is five petabytes. We anticipate that the CGHub and will serve as a platform to aggregate other large-scale cancer genomics information, growing to provide the statistical power to attack the complexity of cancer.

Genome 10K project

The Genome 10K project was co-founded by Haussler to assemble a genomic zoo—a collection of DNA sequences representing the genomes of 10,000 vertebrate species—to capture genetic diversity as a resource for the life sciences and for worldwide conservation efforts.

Validating computational predictions Via Molecular biology and Stem Cells

Research by the Haussler 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. For instance, we use embryonic and induced pluripotent stem cells to investigate neurodevelopment from a functional and evolutionary perspective. Research project areas include genome evolution, comparative genomics, alternative splicing, and functional genomics.

Early research interests

Haussler's current research stems from his early work in machine learning, statistical decision theory, pattern recognition, neural networks, algorithms, and complexity.