David Hawkins, University of Washington Seattle
William Pearson, University of Virginia, Charlottesville
James Taylor, Johns Hopkins University
Lauren Mills, University of Minnesota
Please note special application instructions:
Only ONE letter of reference is required along with your application materials and personal statement/essay
See the roll of honor - who's taken the course in the past
This course presents a comprehensive overview of the theory and practice of computational methods for the characterization of functional elements in DNA and RNA sequence data.
The course helps students achieve a deep, algorithmic understanding of the technologies and methods used to reveal genome function; the goal is to push beyond basic data analysis, and into experimental design and the development of new analysis strategies.
The course enables students to extract the maximum amount of correct information from data by a developing a broad understanding of genomic analysis approaches and their shortcomings.
The course combines lectures with hands-on exercises; students are encouraged to pose challenging problems using their own data. It is designed for biologists seeking advanced training in sequence and genome analysis, computational biology core resource directors and staff, and individuals in other disciplines (e.g., computer science) who wish to survey current research problems in biological sequence analysis. Advanced programming skills are not required.
The schedule and lectures for last year's course can be found here.
The primary focus of the Computational Genomics course is the theory and practice of algorithms in computational biology, with the dual goals of:
- Using current methods more effectively for biological discovery
- Developing new algorithms
Students more interested in the practical aspects of software development are encouraged to apply to the course on Programming for Biology.
Students who would like in-depth training in the analysis of next-generation sequencing data (e.g., SNP calling and the detection of structural variants) should apply to the course on Advanced Sequencing Technologies & Applications.
Those interested in statistical training in genomics, including programming in R/BioConductor, should apply to the course on Statistical Methods for Functional Genomics.
Support & Financial Aid:
Major support provided by: National Human Genome Research Institute.
Financial aid is available to offset tuition costs as follows-
Please indicate your eligibility for funding in the financial aid request that you submit when you apply to the course. Financial aid requests do not affect selection decisions made by the instructors.