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 CSE 527, Au '03: Computational Biology
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Administrative
 Syllabus
Lecture Slides
 Overview
 Microarray Overview
 Microarray Case Study
 Clustering: Basics
 Clustering: Validation
 Clustering: PCA
 Clustering: Graph-based
 Clustering: Model-based
 MLE and EM
   MLE Example (Mathematica code)
 Entropy, EM & WMM
 Motifs
 Gibbs Sampler
 Gibbs w/ Gaps
 Parsimony
 Phylogenetic Footprinting
 Markov Models
 Gene Finding
 RNA Secondary Structure
 Secondary Structure & Splicing
Lecture Notes
 1. Overview
 2. Microarrays I
 3. Microarrays II
 4. Microarrays III
 5. Clustering: Overview
 6. Clustering: Validation
 7. Clustering: PCA
 8. Clustering: CAST
 9. Clustering: Model-Based
 10. MLE and EM
 11. EM; Weight Matrices
 12. Entropy; MEME
 14. Gibbs Sampler
 15. Gibbs w/ Gaps
 16. Phylogenetic Footprinting
 17. Hidden Markov Models, I
 18. Hidden Markov Models, II
 19. Pfam; Gene Finding
 21. RNA Secondary Structure
Assignments
 HW #1
 HW #2
 HW #3
Notes on Readings
 HW #1: Primers
 HW #2: Microarrays
 HW #3: Microarray Analysis
Project Information
 Project Description
 Project Presentations/Reports
   

Time: MW 12:00-1:20
Place: MGH 284
Instructor: Larry Ruzzo, ruzzo, 554 Allen Center, 543-6298
TA: Zizhen Yao, yzizhen,

An introduction to the use of computational methods for the understanding of biological systems at the molecular level. Intended for graduate students in biological sciences interested in learning about algorithms and computational methods, and for graduate students in computer science, mathematics or statistics interested in applications of those fields to molecular biology.

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References:

  1. Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown PO, Herskowitz I. "The transcriptional program of sporulation in budding yeast." Science. 1998 Oct 23;282(5389):699-705.
  2. Bibliography on Microarray Data Analysis http://www.nslij-genetics.org/microarray/
  3. Yeung, Haynor, Ruzzo, Validating Clustering for Gene Expression Data. Bioinformatics, 2001 v 17 #4: 309-318.
  4. Yeung and Ruzzo, Principal component analysis for clustering gene expression data. Bioinformatics,17 (9) 763-774 (2001).
  5. A. Ben-Dor, R. Shamir, Z. Yakhini, "Clustering Gene Expression Patterns", Journal of Computational Biology, v 6 # 3/4 (1999) pp 281-297.
  6. Yeung, Fraley, Murua, Raftery, and Ruzzo: Model-Based Clustering and Data Transformations for Gene Expression Data.
  7. Timothy L. Bailey and Charles Elkan, Fitting a mixture model by expectation maximization to discover motifs in biopolymers, Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994.    [See also http://meme.sdsc.edu/meme/website/papers.html for many related papers.]
  8. Charles E. Lawrence; Stephen F. Altschul; Mark S. Boguski; Jun S. Liu; Andrew F. Neuwald; John C. Wootton, "Detecting Subtle Sequence Signals: A Gibbs Sampling Strategy for Multiple Alignment", Science, New Series, Vol. 262, No. 5131. (Oct. 8, 1993), pp. 208-214.
  9. Roth, F. P., Hughes, J. D., Estep, P. W. & Church, G. M. Finding DNA regulatory motifs within unaligned non-coding sequences clustered by whole-genome mRNA quantitation. Nature Biotechnol. 16, (1998) 939-945.
  10. Emily Rocke and Martin Tompa An Algorithm for Finding Novel Gapped Motifs in DNA Sequences RECOMB98: Proceedings of the Second Annual International Conference on Computational Molecular Biology, New York, NY, March 1998, 228-233.
  11. Mathieu Blanchette, Benno Schwikowski and Martin Tompa Algorithms for Phylogenetic Footprinting Journal of Computational Biology, vol. 9, no. 2, 2002, 211-223.
  12. Mathieu Blanchette and Martin Tompa FootPrinter: a Program Designed for Phylogenetic Footprinting Nucleic Acids Research, vol. 31, no. 13, July 2003, 3840-3842.
  13. Durbin, Richard and Eddy, Sean R. and Krogh, Anders and Mitchison, Graeme, "Biological Sequence Analysis: Probabilistic models of proteins and nucliec acids, Cambridge,1998.
  14. JM Claverie (1997) "Computational methods for the identification of genes in vertebrate genomic sequences", Human Molecular Genetics, 6(10)(review issue): 1735-1744.
  15. M Burset, R Guigo (1996), "Evaluation of gene structure prediction programs", Genomics, 34(3): 353-367.
  16. C Burge, S Karlin (1997), "Prediction of complete gene structures in human genomic DNA", Journal of Molecular Biology , 268: 78-94.
  17. Lyngso RB, Zuker M, Pedersen CN. Fast evaluation of internal loops in RNA secondary structure prediction. Bioinformatics. 1999 Jun;15(6):440-5.
  18. J. McCaskill. The equilibrium partition function and base pair bindings probabilities for RNA secondary structure. Biopolymers, 29:1105-1119, 1990.
  19. Patterson, Yasuhara, and Ruzzo: Pre-mRNA Secondary Structure Prediction Aids Splice Site Prediction. Pacific Symposium on Biocomputing, Kauai, Hawaii, Jan., 2002, pp. 223-234. Preprint: Abstract PDF

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