BioE 594
-- Design and Implementation of Computational Methods in Bioinformatics

12:30am-13:45pm Tuesdays and Thursdays
Room : 419 CMW (College of Medicine West)
Instructor: Yang Dai (yangdai@uic.edu)
Office Hours : 2:00pm -3:00pm, Tuesdays
Office Location :
Room W103-164E
Clinic Science North
820 S. Wood St.
Tel : (312)413-1487

Quick Links
Topics Reading Schedule Grading Analysis tools Databases Microarray analysis papers
Course Description
One of the most important current interests in biology is the mechanism of gene regulation and the production of the main functional building blocks of proteins and cells. Modern DNA microchips enable measurement of the activity state of tens of thousands of genes in a cell, and related techniques are being developed for measuring the protein contents. Bioinformatics and computational genomics utilize modern statistical and computational methods to understanding biological processes. This course emphasizes studies of gene and cell function made possible by recent advances in measurement technology, statistical and computational methods.

Course objectives
The course is mainly intended for advanced students of bioinformatics, but students from other fields are welcome as well. We will start the course by a brief overview of the relevant biological, statistical and computational background needed for modeling large biological data sets. Basics on molecular genetics, microarray technology and experiments will be introduced. After that a set of main current issues in computational functional genomics and state-of-the art methods used for studying them are introduced and discussed. The course will be concluded by a project work on (a simplified version of) one of the research problems.

Course Requirements
Required background includes ability to program in a high-level language. R or Matlab experience may also be helpful. We will use Bioconductor for homework. Homework assignments may include reading, mathematical analysis, programming, and the use of software tools. Student will be asked to present papers from the literature. Students will also need to complete a term project, and produce both a written final report and a class presentation.

Grading Policy
 Class participation and discussion (10%)
Homework assignments (35%)
Term project (30%)
One exam (25%)
final project presentation on April 21, 25
on April 27

Required Software (Base, upon which analysis tools will be used and built)
    Please make sure that you are familiar with them. Homework assignments are based on them.

Topics
  1. Primer on Molecular Genetics
  2. Microarray Technology
  3. Microarray experiments
  4. Preprocessing of Microarray data
  5. Resampling and bootstrap
  6. R and Bioconductor
  7. Multiple testing
  8. Distances and expression measures
  9. Feature selection techniques
  10. Cluster analysis in microarray experiments
  11. Classification in microarray experiments
  12. Probabilistic modeling
  13. Identification of transcriptional binding sites
  14. Inferring genetic network
  15. Computational discovery of genetic functional modules

 Schedule and materials presented

Reading
    Reference Books
    (1) Microarray Bioinformatics, by Dov Stekel, Cambridge University Express, 2003.
    (2) Microarray for an Integrative Genomics, by Issac S, Kohane et al., The MIT Press, 2003. ISBN 0-262-11271-X.
    (3) Biostatistics : a fundation for analysis in the health sciences, 7th edition, by W. Daniel, John Wiley, 1999.
    (4) Pattern Recognition, by S. Theodoridis and K. Koutroumbas, Academic Press, 1999.

    Additional Online Reading Materials
    1. Statistics
    2. R References
    3. Molecular Biology
    4. DNA Chip Technology
    5. Support Vector Machine

Major microarray databases

  • Stanford microarray database

    • Analysis tools for microarray data

  • SMD Microarray Links : Software & Tools
  • Bioconductor

    • Articles on Microarray Datamining:

    Wentian Li of Rockefeller University maintains a list of papers on data analysis: