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Course Catalog

140.638.01 ANALYSIS OF BIOLOGICAL SEQUENCES

Department:
Biostatistics
Term:
2nd term
Credits:
3 credits
Academic Year:
2012 - 2013
Location:
East Baltimore
Class Times:
  • Tu Th,  3:30 - 4:50pm
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:

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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.

Learning Objectives:

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

  1. Discuss concepts in basic molecular biology and probability
  2. Be familiar with classic and modern pairwise alignment algorithms, including BLAST
  3. Discuss the statistical significance of alignment scores and the interpretation of alignment algorithm output
  4. Discuss the mechanism and the use of dynamic programming
  5. Be familiar with multiple alignment
  6. Discuss the different assumptions about evolution made by different models and algorithms
  7. Discuss the likelihood approach to phylogenetic reconstruction, and multiple alignment as applied to phylogenetic tree construction
  8. Discuss Markov models and hidden Markov models (HMM) in the genomic context, and essential algorithms for analyzing HMMs
  9. Discuss HMMs as applied to gene finding
  10. Be familiar with other algorithms in gene finding
  11. 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
Methods of Assessment:

Homework 60%, presentation plus written critique 30%, attendance 10%

Instructor Consent:

No consent required