Meetings & Courses Program

Crossroads of Biology

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Computational Genomics (CANCELED)
December 2 - 9, 2020
Application Deadline: August 15, 2020

Instructors:

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

Co-Instructor:

Lauren Mills, University of Minnesota

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COVID-19: UPDATE August 18, 2020: It is with considerable regret that we announce the cancellation of this course for 2020 but it will be rescheduled to similar dates in 2021.

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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.

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
2019 Lecturers:

TBA

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:

  1. Using current methods more effectively for biological discovery
  2. 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.

Cost (including board and lodging): $3,005

Before applying, ensure you have:
  1. Personal statement/essay;
  2. Letter(s) of recommendation;
  3. Curriculum vitae/resume (optional);
  4. Financial aid request (optional).
    More details.

If you are not yet ready to fully apply, but wish to express interest in the course and receive a reminder two weeks prior to the application deadline, click below: