CSHL Computational Genomics Course

Instructors:

David Hawkins, University of Washington Seattle
William Pearson, University of Virginia, Charlottesville

Co-Instructor:

Lauren Mills, University of Minnesota

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.

Topics include:

  *   Protein & DNA sequence similarity, comparisons, multiple alignments, and database searches
  *   Alignment & analysis of high-throughput sequencing data, with applications from RNA-Seq & ChIP-Seq experiments
  *   Analysis environments including Galaxy, RStudio, and the UNIX command line, with a strong focus on reproducible research
  *   Statistical considerations in the design and analysis of genomic experiments
  *   Regulatory element and motif identification from conserved signals in aligned and unaligned sequences
  *   Integration of genetic and sequence information in biological databases
  *   Genome browsers and features

Financial aid is available to offset tuition costs as follows:

• U.S. applicants (National Human Genome Research Institute).
• Interdisciplinary Fellowships (transitioning from outside biology) & Scholarships (transitioning from other biological disciplines) (Helmsley Charitable Trust).
• International applicants (Howard Hughes Medical Institute).

For more information, to apply or request financial aid, please visit the Computational Genomics course website.