CS 234, Fall 2006: Computational Methods for the Analysis of Biomolecular Data

A staggering wealth of data has being generated by genome sequencing projects and other efforts to determine the structures and functions of biological systems. This advanced graduate course will focus on a selection of computational problems aimed at automatically analyze, cluster and classify biomolecular data.

Class Meeting

TR, 2:10-3:30pm, Engineering II room 139

Office hours

TF, 11:10am-12noon, Engineering II room 317

Preliminary list of topics

  • overview on probability and statistics
  • intro to molecular and computational biology
  • analysis of 1D sequence data (DNA, RNA, proteins)
  • combinatorial algorithms and statistical methods for pattern discovery and sequence alignment
  • sequence alignment and hidden Markov models (HMM)
  • analysis of 2D time series data (gene expression data)
  • clustering algorithms
  • classification algorithms
  • subspace clustering/bi-clustering
  • analysis of other sources of biological data [time permitting]
  • protein-protein interaction graphs
  • TBA
  • Prerequisites

    CS141 (Algorithms) or CS218 (Design and Analysis of Algorithms) or equivalent knowledge. Some programming experience is expected. Students should have some notions of probability and statistics. No biology background is assumed.

    Course Format

    The course will include lectures by the instructor, guest lectures, and possibly discussion sessions on special problems. Students are expected to study the material covered in class. In addition to selected chapters from some of the books listed below, there may be handouts of research papers. There will be three/four assignments, mostly of theoretical nature -- although some may require programming. The actual format of the course will ultimately depend on the number and the background of the students enrolled.

    Relation to Other Courses

    This course is intended to complement "CS238: Algorithms in Computational Molecular Biology", and "CS235: Data Mining Concepts".


  • Richard Durbin, A. Krogh, G. Mitchison, and S. Eddy, Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press, 1999.
  • Dan Gusfield, Algorithms on Strings, Trees and Sequences - Computer Science and Computational Biology, Cambridge University Press, 1997.
  • Pierre Baldi, Soren Brunak, Bioinformatics: the machine learning approach, MIT press, 1998.
  • Joćo Setubal and Joćo Carlos Meidanis Introduction to Computational Molecular Biology, PWS Publishing Co., 1997.
  • Jason Wang, Bruce A. Shapiro, and Dennis Shasha, Pattern Discovery in Biomolecular Data Tools, Techniques, and Applications, Oxford University Press, 1999.
  • David Mount, Bioinformatics: Sequence and Genome Analysis Cold Spring Harbor Laboratory Press, 2002
  • Dan E. Krane, Michael L. Raymer, Fundamental Concepts of Bioinformatics, Benjamin Cummings 2002
  • Warren J. Ewens, Gregory R. Grant, Statistical Methods in Bioinformatics: An Introduction, Springer, 2001
  • An Introduction to Bioinformatics Algorithms, Neil C. Jones and Pavel Pevzner, the MIT Press, 2004.
  • Papers

  • Anders Krogh, "An introduction to hidden Markov models for biological sequences" [PDF format]
  • Brona Brejova, Chrysanne DiMarco, Tomas Vinar, Sandra Romero Hidalgo, Gina Holguin, Cheryl Patten. "Finding Patterns in Biological Sequences". Unpublished TR. University of Waterloo, 2000 [PDF format]
  • Alberto Apostolico, Mary Ellen Bock, Stefano Lonardi, Xuyan Xu, "Efficient Detection of Unusual Words", Journal of Computational Biology, vol.7, no.1/2, pp.71-94, 2000 [PDF format]
  • Gesine Reinert, Sophie Schbath, Michael S. Waterman, "Probabilistic and Statistical Properties of Words: An Overview", Journal of Computational Biology, vol.7, no.1/2, 2000 [PDF format]
  • Todd Mood, "The Expectation-Maximization Algorithm", IEEE Signal Processing Magazine, Nov 1996 [PDF Format]
  • Jeff A. Bilmes, "A Gentle Tutorial of the EM Algorithm and its Applications to Parameter Estimation for Gaussian Mixture and HMM", UC Berkley, TR-97-021 [PDF Format]
  • C. E. Lawrence, S. F. Altschul, M. S. Boguski, J. S. Liu, A. F. Neuwald, J. C. Wootton, "Detecting subtle sequence signals: A Gibbs sampling strategy for multiple alignment", Science 262, 1993 [PDF Format]
  • Jun S. Liu, Andrew F. Neuwald, Charles E. Lawrence, "Bayesian Models for Multiple Local Sequence Alignment and Gibbs Sampling Strategies", Journal of the American Statistical Association, 90(432), 1995 [PDF Format]
  • Analysis of microarray gene expression data. W. Huber, A. v.Heydebreck, M. Vingron. In Martin Bishop et al.(editors), Handbook of Statistical Genetics, 2nd Edition. John Wiley & Sons, Ltd., Chichester, UK, 2003.[PDF format]
  • Slides

  • Slides [PDF Format 2slides/page] (Course Overview)
  • Slides [PDF Format 2slides/page] (Intro to Mol Biology)
  • Slides [PDF Format 2slides/page] (Some basic probability)
  • Slides [PDF Format 2slides/page] (Intro to Pattern Discovery)
  • Slides [PDF Format 2slides/page] (Discovery of Rigid Patterns)
  • Slides [PDF Format 2slides/page] (HMM)
  • Slides [PDF Format 2slides/page] (Microarrays)
  • Slides [PDF Format 2slides/page] (Biological networks)
  • Resources

  • The inner life of a Cell
  • DNA Molecular animation
  • A bioinformatics glossary
  • What's a Genome (on-line book)
  • DNA interactive
  • Primer on Molecular Genetics
  • Daily news about bioinformatics
  • PMP Resources
  • Projects

  • Craig Boucher's CS234 webpage
  • Guanqun Shi's CS234 webpage
  • Anna Charisi's CS234 webpage
  • Jin Shieh's CS234 webpage
  • Daniel Jordan's CS234 webpage
  • Theodoros Lappas's CS234 webpage
  • Danhua Guo's CS234 webpage
  • Shashwati Kasetty's CS234 webpage
  • Elena Harris's CS234 webpage
  • Jose Medina's CS234 webpage
  • Vincent Peng's CS234 webpage
  • Wei-Bung (Bob) Wang's CS234 webpage
  • Homework

  • Homework 1 (posted Oct 5, due Oct 19)
  • Homework 2 (posted Oct 20, due Nov 2)
  • Homework 3 (posted Nov 3, due Nov 16)
  • Homework 4 (posted Nov 17, due Dec 5)
  • Presentation

  • choose a slot 1-13 below and send me your choice
  • choose a paper among RECOMB 2006 or ISMB 2006 proceedings and send the title to me
  • send the Powerpoint file to me the day before the presentation (before 5pm)
  • give the 15 minutes presentation (make sure you time it correctly, I will stop you after 15mins)
  • Calendar of Lectures

  • Sep 28: Intro, Molecular Biology [slides 1-26]
  • Oct   3: Molecular Biology [slides 27-46]
  • Oct   5: Molecular Biology [slides 47-80]
  • Oct 10: Molecular Biology [slides 81-106]
  • Oct 12: Molecular Biology [slides 107-end], Intro to Probability [slides 1-17]
  • Oct 17: Intro to Probability [slides 18-end], Intro to Pattern Discovery [slides 1-8]
  • Oct 19: Intro to Pattern Discovery [9-40](hw1 due)
  • Oct 24: Intro to Pattern Discovery [41-84]
  • Oct 26: Intro to Pattern Discovery [85-end], Discovery of Rigid Patterns [1-44]
  • Oct 31: Discovery of Rigid Patterns [45-78]
  • Nov   2: Discovery of Rigid Patterns [79-end], HMM [1-15] (hw2 due)
  • Nov   7: HMM [16-78, skipped 34-76]
  • Nov   9: MIDTERM (in class, closed books, closed notes)
  • Nov 14: HMM [79-end], Microarrays [1-20]
  • Nov 16: Microarrays [21-end] (hw3 due)
  • Nov 21: Networks [1-end]
  • Nov 23: Thanksgiving

  • Nov 28: Presentations. (deadline for the PPT file is Nov 27th, 5PM)
    1: Craig (Assessing Significance of Connectivity..., RECOMB'06)
    2: Elena (Clustering Near-Identical Sequences..., RECOMB'06)
    3: Jin (Rapid knot detection and application to protein..., ISMB'06)
    4: Shi (Efficient identification of DNA hybridization ..., ISMB'06)

  • Nov 30: Guest Lecture by C. Shelton

  • Dec   5: Presentations. (deadline for the PPT file is Dec 4th, 5PM)
    5: Daniel (Apples to apples: improving the performance ..., ISMB'06)
    6: Jose (PROXIMO -- a new docking algorithm to model ..., ISMB'06)
    7: Shashwati (Protein classification using ontology .., ISMB'06)
    8: Wei-Bung (Sorting by Weighted Reversal ..., RECOMB'06)

  • Dec   7: Presentations. (deadline for the PPT file is Dec 6th, 5PM)
    9: Anna (Distance based algorithms for small biomolecule..., ISMB'06)
    10: Danhua (Create and assess protein networks through..., ISMB'06)
    11: Theodoros (Simple and Fast Inverse Alignment, RECOMB'06)
    12: Meng-Chih (Finding the evidence for protein-protein ..., ISMB'06)
  • Project Demo (in my office, please bring your laptop)

  • Dec 11: 10:00 Bob
              11:00 Daniel
              11:30 Theodoros
  • Dec 13: 10:00 Elena
              10:30 Jose
              11:00 Guanqun
              11:30 Anna
  • Dec 15: 9:30 Shashwati
              10:00 Craig
              10:30 Meng-Chih
              11:00 Jin
              11:30 Danhua