140.638.01 | AY 2013-2014 - 2nd Term | 3 Credit(s)
TTh 3:30:00 PM
  • Course Learning Objectives

    Upon successfully completing this course, students will be able to:

    • Discuss concepts in basic molecular biology and probability
    • Be familiar with classic and modern pairwise alignment algorithms, including BLAST
    • Discuss the statistical significance of alignment scores and the interpretation of alignment algorithm output
    • Discuss the mechanism and the use of dynamic programming
    • Be familiar with multiple alignment
    • Discuss the different assumptions about evolution made by different models and algorithms
    • Discuss the likelihood approach to phylogenetic reconstruction, and multiple alignment as applied to phylogenetic tree construction
    • Discuss Markov models and hidden Markov models (HMM) in the genomic context, and essential algorithms for analyzing HMMs
    • Discuss HMMs as applied to gene finding
    • Be familiar with other algorithms in gene finding
    • Identify from the literature important algorithmic/statistical advances in bioinformatics, and prepare an oral presentation of a recent bioinformatics publication that is important from either a biological or a mathematical perspective
  • Contact Information
    Sarah Wheelan
  • Course Description
    Presents an algorithmic approach to modern biological sequence analysis. Provides an overview of the core algorithms and statistical principles of bioinformatics. Topics include general probability and molecular biology background, sequence alignment (local, global, pairwise and multiple), hidden Markov Models (as powerful tools for sequence analysis), gene finding, and phylogenetic trees. Emphasizes algorithmic perspective although no prior programming experience is required. Covers basic probability and molecular biology in enough detail so that no prior probability or advanced biology classes are required.
  • Intended Audience
    Bioinformatics and biostatistics MHS students; PhD students in all departments involved in genomic research (includes most of Public Health, Medicine, and Biomedical Engineering, Computer Science, and Math. Sciences)
  • Methods of Assessment
    Homework 60%, presentation plus written critique 30%, attendance 10%
  • Course Schedule

    Please see the course Session for a full list of dates and items for this course.

    All pertinent course information is available at the course website

  • Academic Ethics Code

    Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.

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