Skip Navigation

Course Directory

Analysis of Biological Sequences

East Baltimore
2nd term
3 credits
Academic Year:
2014 - 2015
Instruction Method:
Class Times:
  • Tu Th,  3:30 - 4:50pm
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
  • Sarah Wheelan
Wheelan, Sarah

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 70%, written critique of a publication 30%

Instructor Consent:

No consent required