Biostatistics & Medical Informatics 576
Computer Sciences 576
Introduction to Bioinformatics (Fall 2007)

Syllabus, Readings and Lecture Notes

• Introduction to Molecular Biology, Genomics, Bioinformatics, Probability
  • topics: DNA, RNA, proteins, the Central Dogma, RNA processing in eukaryotes, networks, available genomics data, basic probability theory for discrete variables
  • required reading
    • L. Hunter. Life and Its Molecules: A Brief Introduction. AI Magazine 25(1):9-22, 2004.
  • recommended reading
    • Human Genome Project: Frequently Asked Questions
    • NAR Molecular Biology Database Collection
  • lecture notes
    • Course Overview (9/5)
    • Introduction to Molecular Biology and Genomics (9/7, 9/10)
    • Primer on Probability for Discrete Variables (9/21)

• Pairwise Sequence Alignment
  • topics: dynamic programming methods for global and local alignment, linear and affine gap penalty functions, the BLAST algorithm, alignment statistics, substitution matrices
  • required reading
    • Sections 2.1-2.4 in Durbin et al.
    • Sections 2.7, 2.8 in Durbin et al.
    • 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.
  • lecture notes
    • Pairwise Sequence Alignment (9/12, 9/14, 9/17, 9/19, 9/21
    • Heuristic Methods for Sequence Database Searching (9/24, 9/26)
    • Alignment Statistics and Substitution Matrices (9/26, 9/28)
    • Whole-Genome Alignments and Polytopes for Comparative Genomics (11/5)

• Probabilistic Sequence Models
  • topics: Markov chains, high-order Markov models, inhomogenous Markov models, hidden Markov models, Forward/Backward/Viterbi algorithms, applications to gene finding and motif modeling
  • required reading
    • Sections 3.0, 3.1, 3.5 in Durbin et al.
    • Sections 3.2, 3.3 in Durbin et al.
  • lecture notes
    • Markov Chain Models (9/28, 10/3)
    • Hidden Markov Models (10/8, 10/12, 10/15)
    • Applications of HMMs in Computational Biology (10/10)
    • HMMs Continued (10/17)

• Multiple Sequence Alignment and Phylogenetic Tree Inference
  • topics: dynamic programming for MSA, heuristic methods for MSA, profile HMMs, distance based phylogeny methods, UPGMA, neighbor-joining, parsimony based algorithms, Fitch's algorithm, weighted parsimony, nearest-neighbor interchange, branch and bound search
  • required reading
    • Chapter 6 in Durbin et al.
    • Sections 7.1-7.6 in Durbin et al.
  • lecture notes
    • Distance-Based Approaches to Inferring Phylogenetic Trees (10/17, 10/22)
    • Parsimony-Based Approaches to Inferring Phylogenetic Trees (10/24, 10/26)
    • Multiple Sequence Alignment (10/29)

• Analyzing Data from Microarray, SNP-chip, and other High-Throughput Experiments
  • topics: high-throughput technologies, detecting diffferential expression, multiple hypothesis testing and false-discovery-rate methods, clustering algorithms, biclustering, classification algorithms
  • required reading
    • Spotted Array Flash Animation Tutorial
    • J. Storey and R. Tibshirani. Statistical Significance for Genomewide Studies. Proceedings of the National Academy of Sciences, 100:9440-9445, 2003.
    • C. Manning and H. Schutze. Chapter 15: Clustering. Foundations of Statistical Natural Language Processing, MIT Press, 1999.
    • J. Nevins, E. Huang, H. Dressman, J. Pittman, A. Huang, M. West. Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction. Human and Molecular Genetics, 12:R153-R157, 2003.
  • recommended reading
    • 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.
    • A. Tanay, R. Sharan and R. Shamir. Discovering Statistically Significant Biclusters in Gene Expression Data. Bioinformatics 18(Suppl. 1):S136-S144, 2002.
    • J. Hardin, M. Waddell, D. Page, F. Zhan, B. Barlogie, J. Shaughnessy, and J. Crowley. Evaluation of Multiple Models to Distinguish Closely Related Forms of Disease Using DNA Microarray Data: an Application to Multiple Myeloma. Statistical Applications in Genetics and Molecular Biology, 3(1) 2004.
    • M. Molla, M. Waddell, D. Page and J. Shavlik. Using Machine Learning to Design and Interpret Gene-Expression Microarrays AI Magazine, 25(1):23-44, 2004.
  • lecture notes
    • Microarrays and Other High-Throughput Methods (11/2)
    • Clustering Gene-Expression Data (11/7, 11/9, 11/12, 11/14)
    • Estimating the False Discovery Rate in Genome-wide Studies (11/14, 11/16)
    • Classification with Gene Expression Data (11/16, 11/19)

• Inferring and Modeling Cellular Networks
  • topics: Bayesian networks, module networks, exact and approximate inference methods, parameter and structure learning
  • required reading
    • N. Friedman, M. Linial, I. Nachman, D. Pe'er. Using Bayesian networks to analyze expression data. Journal of Computational Biology 7(3-4):601-620, 2000.
      Alternate link
    • K. Sachs, O. Perez, D. Pe'er, D. Lauffenburger, G. Nolan. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308(5721):523-529, 2005.
    • E. Segal, M. Shapira, A. Regev, D. Pe'er, D. Botstein, D. Koller and N. Friedman. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genetics 34(2):166-176, 2003.
  • recommended reading
    • N. Friedman, I. Nachman and D. Pe'er. Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm In Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence 1999.
    • I. Nachman, A. Regev, and N. Friedman. Inferring quantitative models of regulatory networks from expression data. Bioinformatics 20(Suppl. 1):i248-i256, 2004.
  • lecture notes
    • Introduction to Biological Networks (11/19)
    • Bayesian Networks Applied to Modeling Cellular Networks (11/21, 11/26, 11/28, 11/30) (updated 11/30)
    • Inference in Bayesian Networks (12/3, 12/5, 12/7) (updated 12/7)
    • Module Networks (12/5, 12/7)

• Protein Structure Prediction
  • topics: secondary structure prediction, threading, the ROSETTA method, docking
  • required reading
    • B. Rost. Protein Structure Prediction in 1D, 2D and 3D. In The Encyclopaedia of Computational Chemistry, 3:2242-2255, 1998.
    • R. Lathrop and T. Smith. Global Optimum Protein Threading with Gapped Alignment and Empirical Pair Score Functions. Journal of Molecular Biology, 255(4):641-665, 1996.
  • recommended reading
    • C. Rohl, C. Strauss, K. Misura and D. Baker. Protein Structure Prediction using Rosetta. Methods in Enzymology, 383:66-93, 2004.
  • lecture notes
    • Introduction to Protein Structure Prediction (12/10)
    • Protein Structure Prediction: Threading and Rosetta (12/12, 12/14) (updated 12/14)

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  Last modified: Fri Dec 14 11:14:46 CST 2007