UW Genome Sciences

Welcome to the Department of Genome Sciences, which began in September 2001 by the fusion of the Departments of Genetics and Molecular Biotechnology.

Our goal is to address leading edge questions in biology and medicine by developing and applying genetic, genomic and computational approaches that take advantage of genomic information now available for humans, model organisms and a host of other species. Our faculty study a broad range of topics, including the genetics of E. coli, yeast, C. elegans, Drosophila, and mouse; human and medical genetics; mathematical, statistical and computer methods for analyzing genomes, and theoretical and evolutionary genetics; and genome-wide studies by such approaches as sequencing, transcriptional and translational analysis, polymorphism detection and identification of protein interactions.

Our department includes both faculty with primary appointments in Genome Sciences, as well as adjuncts in other departments and Seattle institutions. Ten faculty are members of the National Academy of Sciences, including 2001 Nobel Prize winner Dr. Lee Hartwell, who conducted much of his groundbreaking work in the Department of Genetics. Four training faculty are Howard Hughes Medical Institute Investigators.  Graduate research in the Department leads to a Ph.D. in Genome Sciences and students may also choose to follow one of the Data Science options or participate in the Computational Molecular Biology program. Our department has around 70 graduate students at any given time and has moved into the new William H. Foege Building. A history of the Department of Genome Sciences is available here.

Not sure what Genome Sciences is all about? Please see the basic overview of our research below.

Model Organism Genetics:  
What can we learn from a yeast cell, a roundworm, a fruit fly, or a mouse? Despite how different these organisms appear from each other and humans, they all carry out fundamental life processes like ingesting food, eliminating wastes, respiring, reproducing, and dying. Each model organism is useful experimentally for probing particular research questions. For example, yeast is a single celled organism with a short life cycle, but its cell structure is similar to that of higher organisms. Thus, it useful for studying the cell cycle, protein-protein interactions, as well as biochemical pathways. The roundworm and fruit fly are ideal for studying how animals develop from a single fertilized egg into a beautiful and complex multicellular adult. Mice are very similar to humans genetically and physiologically and thus are well suited for investigating human disease, behavior, and for unraveling the information contained in the human genome

Human and Medical Genetics:
When working with model organisms like yeast, flies, and mice, geneticists choose specific individuals with a trait of interest and mate them with other individuals to determine how the trait is passed to the next generation. In contrast, humans select their own mates, and we do not create human mutants for research. How can we study the inheritance of traits in humans? The answer lies in combining a variety of approaches, including analysis of the inheritance of genetic traits through large multi-generation families, gene sequencing, and development of animal models for human diseases

Genomics and Proteomics:
A genome is the complete DNA content of an organism, and genomics is the study of entire genomes, including all the encoded genes and their interactions. We now have the complete sequence of the human genome, as well as the genomes of several model organisms. The strings of A, C, G, and T nucleotides in DNA make up a code that creates complex three dimensional molecules called proteins. These molecules are the major components of body structures and carry out most of the biological processes inside our cells. Proteomics is the study of all the proteins in an organism and their interactions.

Computational Biology:
Computational Biology focuses on the analysis and interpretation of complex genomic and proteomic data. Advances in this field require the development of sophisticated computer tools for accessing, analyzing and managing data and the creation of algorithms to compare large data sets.