Taken from cs.wisc.edu.

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)