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Department: Biostatistics
Term: 2nd term
Credits: 3 credits
Academic Year: 2012 - 2013
Course Instructor:

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 Objective(s):
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

Methods of Assessment: Homework 60%, presentation plus written critique 30%, attendance 10%
Location: East Baltimore
Class Times:
  • Tuesday 3:30 - 4:50
  • Thursday 3:30 - 4:50
Enrollment Minimum: 10
Instructor Consent: No consent required
Grading Restriction: Letter Grade or Pass/Fail