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ECS 124 THEORY AND PRACTICE OF BIOINFORMATICS (4) II

Lecture: 3 hours

Laboratory: 1 hour

Prerequisites: Course 10 or 30 or Engineering 6; Statistics 12 or 13 or 32 or 131A or Mathematics 135A; Biological Science 1A or Molecular and Cellular Biology 10

Grading: Letter; 5-7 homework/laboratory sets (60%), final (40%)

Catalog Description:
Fundamental biological, mathematical and algorithmic models underlying bioinformatics; sequence analysis, database search, gene prediction, molecular structure comparison and prediction, phylogenetic trees, high throughput biology, massive datasets; applications in molecular biology and genetics; use and extension of common bioinformatics tools.

Expanded Course Description:

  1. Initial examples of the power of bioinformatics in modern biology
    1. The importance of sequence and structure comparison and of database search
    2. The use of sequence analysis in laboratory protocols
    3. The use of phylogenetics in evolution and non-evolutionary areas of biology
  2. Sequence analysis
    1. Probabilistic and biological models underlying sequence alignment
    2. Computational efficiency and the need for compromises in the models
    3. The general technique of Dynamic Programming
    4. Pairwise sequence alignment - algorithms for global, local alignment and variations
    5. Algorithms for multiple sequence alignment and the identification/use of motifs
    6. Database search - FASTA, BLAST, PSI-BLAST, scoring matrices, statistical significance and its significance
    7. Creation and use of motif models
    8. Novel uses of sequence analysis in studying DNA, RNA and proteins
    9. Sequence analysis in genomics and high throughput biology
  3. Phylogenetic algorithms
    1. Probabilistic and ideal-data models underlying phylogenetic algorithms
    2. Distance-based methods
    3. Character/parsimony-based methods
    4. Maximum-likelihood methods
    5. PAUP, PHYLIP
    6. Evolutionary and non-evolutionary uses for phylogenetics
    7. The interaction of phylogenetics and sequence analysis
  4. Protein and RNA structure comparison and prediction
    1. Ideal-data models underlying structure comparison and prediction
    2. Algorithms for RNA folding
    3. Methods and problems in protein structure comparison and prediction
    4. Biological use of structure prediction and comparison tools
    5. Overview of common bioinformatics utilities and web-based resources such as GCG and Entrez

Textbooks:

Required Text
A. Johnson, Elements of Programming in Perl, Manning Publications, 2000
D. Gusfield, K. Stevens, Notes for an Undergraduate Course on Bioinformatics, distributed online, 200?
Additional library readings available online.

Supplemental Text
R. Durbin et al., Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridege Press, 1998.
A. Baxevanis and B. Ouellete, Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Wiley-Interscience, 1998.
M. Bishop and C. Rawlings, DNA and Protein Sequence Analysis: A Practical Approach, IRL Press, 1997.
D. Gusfield, Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology, Cambridge Press, 1997.

Homework:
Each homework set includes creative problems as well as recitation problems to strengthen understanding and discover new material.

Computer Usage:
The lab portion of the class will emphasize practical computer exercises using both established bioformatics software and writing simple programs in Perl or Java.

ABET Category Content:

Engineering Science: 1 unit
Engineering Design: 0 unit

Goals:
Students will:

Student Outcomes:

Instructors: Dan Gusfield, V. Filkov

Prepared by: Dan Gusfield (October 2006)

Overlap Statement:
The laboratory section of ECS 124 will overlap to some amount with Animal Science 212 (taught by J. Madreno) a graduate course offered every other year. The overlap in the laboratory section is only partial, as AS 212 looks at more sequence analysis packages than in ECS 124; while the lab portions of ECS 124 will also look at packages for phylogenetic analysis, which is not covered in AS 212, and will involve some computer programming in Perl or Java, while no programming is involved in AS 212. The theoretical parts of ECS 124 (the lecture part of the course) will have no essential intersection with AS 212, being either on different material entirely, or being a much more mathematical and algorithmic treatment of the material, i.e., fully explaining and developing the logic of the techniques, rather than focusing on learning to use these techniques in the form of packaged computer programs. A good analogy to explain the partial intersection is that AS 212 is a course on "flying an airplane" while ECS 124 will be on the "physics of flight" with exercises in flying to make the ideas concrete.

ECS 124 intersects EVE 298 (taught by M. Sanderson and S. Nadler) in the subarea of phylogenetics. However, the emphasis of the two courses in that overlapping subarea is again different. EVE 298 is oriented towards teaching biology graduate students to use computerized phylogenetic tools effectively in their biological (phylogenetic) research, while ECS 124 will have a more algorithmic and mathematical orientation.

ECS 124 intersects GGG 298D (taught by C. Warden and M. Syvanen) in the subarea of sequence analysis. Again the emphasis is quite different. GGG 298D is oriented towards teaching biology graduate students to use computerized sequence analysis tools effectively in their (biological sequence analysis) research, while ECS 124 will have a more algorithmic and mathematical orientation. There are no undergraduate courses on campus that have any substantial intersection with ECS 124. There are no undergraduate courses on campus that have any substantial intersection with ECS 124.

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