Biostatistics & Medical Informatics 776
Computer Sciences 776
Advanced Bioinformatics (Spring 2002)

General Course Information

Instructor: Mark Craven Office: 6730 Medical Sciences Center (corner of Charter and University) Office Hours: 2:00-3:00pm Tuesday, 2:30-3:30 Wednesday, or by appointment TA: Wei Luo luo@biostat.wisc.edu Office: 6749 Medical Sciences Center Office Hours: 3:00-4:00pm Tuesday & Thursday, or by appointment Meeting Time and Location: 1:00-2:15 Monday & Wednesday 2317 Engineering Readings: required text: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Cambridge University Press, 1998. recommended text: Introduction to Computational Molecular Biology. J. Setubal and J. Meidanis. PWS Publishing, 1997. other readings from the primary literature Final Exam: Thursday May 9, 6-8:00pm in Psychology 115

Course Overview

Bioinformatics (computational molecular biology) involves developing and applying computational methods for managing and analyzing information about the sequence, structure and function of biological molecules and systems.

The goals of this course are to provide an understanding of:

• the types and sources of data available for computational biology,
• the fundamental computational problems in molecular biology and genomics,
• a core set of widely used algorithms in computational biology,
• a set of algorithms that have important applications in computational biology, but which have key applications outside of biology as well.

Course Requirements

• homework assignments: ~40%
• project: ~20%
• final exam: ~35%
• class participation: ~5%

Syllabus, Readings and Lecture Notes

• Introduction to Molecular Biology, Genomics and Bioinformatics
  • required reading
    • L. Hunter. Molecular Biology for Computer Scientists. In Artificial Intelligence and Molecular Biology, L. Hunter editor, 1993, AAAI Press.
    • DOE Primer on Molecular Genetics
    • E. Pennisi. Finally, the Book of Life and Instructions for Navigating It. Science 288:2304-2307, 2000.
  • recommended reading
    • Human Genome Project: Frequently Asked Questions
  • lecture notes
    • General Course Information (1/23)
    • Introduction to Molecular Biology (1/28)
  • resources
    • Genbank
    • SWISS-PROT
    • Protein Data Bank
    • PubMed
    • List of Molecular Biology Databases (from Nucleic Acids Research 29(1), 2001)
• Sequence Alignment
  • required reading
    • Chapter 2. R. Durbin, S. Eddy, A. Krogh, G. Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press. 1998.
    • S. Altshul, T. Madden, A. Schaffer, J. Zhang, Z. Zhang, W. Miller and D. Lipman. Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs. Nucleic Acids Research 25(17):3389-3402, 1997.
    • Chapter 6 (through section 6.4). R. Durbin et al.
    • A. Delcher, S. Kasif, R. Fleischmann, J. Peterson, O. White and S. Salzberg. Alignment of Whole Genomes. Nucleic Acids Research 27(11):2369-2376, 1999.
  • recommended reading
    • S. Altschul, W. Gish, W. Miller, E. Myers, D. Lipman. Basic Local Alignment Search Tool. Journal of Molecular Biology 215:403-410, 1990.
  • lecture notes
    • Pairwise Sequence Alignment (1/30, 2/4)
    • Heuristic Methods for Sequence Database Searching (2/6, 2/11)
    • Substitution Matrices and Alignment Statistics (2/11, 2/13)
    • Whole Genome Alignment (2/13)
• Probabilistic Sequence Models
  • required reading
    • Chapter 3, R. Durbin et al.
    • S. Salzberg, A. Delcher, S. Kasif, and O. White. Microbial Gene Identification Using Interpolated Markov Models. Nucleic Acids Research 26(2):544-548, 1998.
    • T. Bailey and C. Elkan. The value of Prior Knowledge in Discovering Motifs with MEME. In Proceedings of the 3rd International Conference on Intelligent Systems for Molecular Biology, pp. 21-29, 1995.
    • C. Lawrence, S. Altschul, M. Boguski, J. Liu, A. Neuwald, and J. Wootton. Detecting Subtle Sequence Signals: A Gibbs Sampling Strategy for Multiple Alignment. Science 262:208-214, 1993. (copies handed out in class)
    • A. Krogh. An Introduction to Hiden Markov Models for Biological Sequences. In S. Salzberg et al., eds., Computational Methods in Molecular Biology, 1998. Elsevier.
  • lecture notes
    • Markov Chain Models (2/25, 2/27)
    • Interpolated Markov Models for Gene Finding (2/27)
    • Learning Sequence Motifs Using EM and Gibbs Sampling (3/4, 3/6, 3/11) (revised 3/15)
    • Hidden Markov Models (3/11, 3/13, 3/18) (revised 3/20)
    • More on HMMs and Multiple Sequence Alignment (3/20)
• Gene Expression Analysis
  • required reading
    • P. Brown and D. Botstein Exploring the New World of the Genome w ith DNA Microarrays. Nature Genetics Supplement 21:33-37, 1999.
    • M. Eisen, P. Spellman, P. Brown, D. Botstein. Cluster Analysis and Display of Genome-Wide Expression Patterns. Proceedings of the National Academy of Sciences, 95:14863-14868, 1998.
    • N. Friedman, . Linial, I. Nachman, and D. Pe'er. Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology, 7:601-620, 2000.
    • C. Manning and H. Schutze. Chapter 15: Clustering. Foundations of Statistical Natural Language Processing, MIT Press, 1999.
    • A. Brazma, I. Jonassen, J. Vilo and E. Ukkonen. Predicting Gene Regulatory Elements in Silico on a Genomic Scale. Genome Research, 8:1202-1215, 1998.
  • lecture notes
    • Introduction to Microarrays (4/1)
    • Inferring Regulatory Networks from Gene Expression Data (4/3)
    • Clustering Gene Expression Data (4/8, 4/10)
    • Inference with Gene Expression and Sequence Data (4/15)
• Biomedical Text Analysis
  • required reading
    • H. Shatkay, S. Edwards, W. J. Wilbur and M. Boguski. Genes, Themes and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis. In Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology, pp. 317-328, 2000.
  • lecture notes
    • Biomedical Text Analysis (4/17, 4/24) (revised 5/1)
• Protein Structure Prediction
  • lecture notes (guest lecture by Prof. Thomas Anantharaman)
    • Protein Structure Prediction (4/22)
• RNA Structure Modeling
  • required reading
    • Chapter 9 and section 10.1 of Durbin et al.
  • lecture notes
    • Stochastic Context Free Grammars for RNA Modeling (4/29, 5/1) (revised 5/6)
• Modeling Cellular Systems

Guest Lectures

• Prof. Christina Kendziorski, UW Biostatistics & Medical Informatics
  The Identification and Homology Mapping of Quantitative Trait Loci (2/18)
• Prof. Rich Maclin, Univ. of Minnesota, Duluth
  Extracting Keyphrases to Annotate Biological Objects of Interest (2/20)

Last modified: Mon Oct 21 10:19:07 CDT 2002