CS284A: Representations & Algorithms for Molecular Biology

Syllabus, Readings and Lectures Notes

• Introduction to Computational biology, Molecular Biology and Genomics. Sept 25, 2008
• reading
• L. Hunter. Molecular Biology for Computer Scientists. In Artificial Intelligence and Molecular Biology, 1993.
• Human Genome Project: Frequently Asked Questions
• A Quick Introduction to Elements of Biology, a primer by Alvis Brazma et al.
• lecture notes
• Lecture 1: Introduction to Molecular Biology and Genomics
• resources
• Genbank
• USCS Genome Browser
• Ensembl Genome Browser
• SWISS-PROT
• Protein Data Bank
• PubMed
• List of Molecular Biology Databases(from Nucleic Acids Research)
• S-Star Bioinformatics Online Course Schedule, a collection of video primers.
• FAQ on bioinformatics.
• Online courses related to bioinformatics and computational biology
• Sequence analysis, regulatory motif discovery.
• reading
  • How does DNA sequence motif discovery work?, Patrik D'haeseleer, Nature Biotechnology - 24, 959 - 961 (2006)
  • Assessing computational tools for the discovery of transcription factor binding sites Tompa et al. Nature Biotechnology 23, 137 - 144 (2005)
  • A survey of motif discovery methods in an integrated framework, GK Sandve and F Drablos, Biology Direct 2006, 1:11
• lecture notes
• introductory slides
• scribe notes
• resource
• Transfac motifs
• P-value for enumeration method, binomial, hypergeometric distribution.
• lecture notes
• Binomial and hypergeometric distribution
• scribe notes
• Position weight matrix and mixture model
• reading
• Systematic determination of genetic network architecture , Tavazoie et al. Nature Genetics, 22:281-85, 1999
• lecture notes
• Mixture model
• scribe notes
• EM-algorithm, Gibbs Sampling
• reading
• Fitting a Mixture Model by Expectation Maximization to Discover Motifs in Bipolymers TL Bailey and C Elkan, UCSD
• Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment CE Lawrence, SF Altschul, MS Boguski, JS Liu, AF Neuwald, and JC Wootton, Science, 262:208-214, 1993
• Lecture note
• Mixture model
• EM-algorithm and Gibbs sampling
• Motif discovery in yeast cell cycle expression data
• Yeast cell cycle cluster #2 promoter sequence
• scribe note
• resources
• MEME
• AlignACE
• BioProspector
• Microarray analysis, gene expression analysis, clustering algorithms, Jan 28, 2008
• reading
  • Exploring the New World of the Genome w ith DNA Microarrays P. Brown and D. Botstein Nature Genetics Supplement 21:33-37, 1999
  • Cluster Analysis and Display of Genome-Wide Expression Patterns, M. Eisen, P. Spellman, P. Brown, D. Botstein. Proceedings of the National Academy of Sciences, 95:14863-14868, 1998.
  • Using Bayesian Networks to Analyze Expression Data, N. Friedman, Linial, I. Nachman, and D. Pe'er. Journal of Computational Biology, 7:601-620, 2000.
• lecture
  • K-means and hierarchical clustering
  • Microarray
  • scribe note: clustering algorithms
• resource
  • MATLAB Statistics Toolbox
• Tutorial on Python programmming (Kenny Daily)
• references
  • http://aspn.activestate.com/ASPN/Python/Cookbook/
  • http://ivory.idyll.org/articles/advanced-swc/
  • http://www.diveintopython.org/
  • http://docs.python.org/lib/
  • http://docs.python.org/ref/ref.html
  • http://www.doughellmann.com/projects/PyMOTW/
  • Sample code: FastaReader.py
• Sequence alignment, dynamic programming, Smith-Waterman algorithm
• reading
  • Eddy SR, What is dynamic programming? Nat Biotechnol. 2004, 22:909-10
  • Eddy SR Where did the BLOSUM62 alignment score matrix come from? Nature Biotechnology 22:1035-1036 (2004)
• Lecture notes: Sequence alignment
• scribe notes: Sequence alignment
• BLAST, the statistics of alignment scores
• 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.
  • S. Altschul, W. Gish, W. Miller, E. Myers, D. Lipman. Basic Local Alignment Search Tool. Journal of Molecular Biology 215:403-410, 1990.
  • Julie D. Thompson, Desmond G. Higgins+ and Toby J. Gibson, CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice Nucleic Acids Research, 1994, Vol. 22, No. 22 4673-4680
• Lecture: BLAST and statistics of BLAST scores
• Scribe notes: BLAST
• Markov chains and hidden Markov models (HMM)
• reading
  • An Introduction to Hidden Markov Models for Biological Sequences A Krogh, in S. Salzberg et al., eds., Computational Methods in Molecular Biology, 1998. Elsevier.
• Lecture notes: HMM
• Lecture: scribe notes HMM model, Viterbi, forward and backward algorithms
• Lecture: scribe notes Learning
• Lecture: Matlab code for a dishonest casino dealer problem. HMM Example
• Gene finding
• reading
  • Overview of Computational Gene Prediction, in book Computational Gene Prediction (CGP) by William H. Majoros
  • S. Salzberg, A. Delcher, S. Kasif, and O. White. Microbial gene identification using interpolated Markov models, Nucleic Acids Research 26:2 (1998), 544-548.
• Lecture: Gene discovery
• Multiple hypothesis testing
  • reading
    • Benjamini, Yoav; Hochberg, Yosef (1995). "Controlling the false discovery rate: a practical and powerful approach to multiple testing". Journal of the Royal Statistical Society, Series B (Methodological) 57 (1): 289300.
    • Benjamini, Yoav; Yekutieli, Daniel (2001). "The control of the false discovery rate in multiple testing under dependency". Annals of Statistics 29 (4): 11651188.
    • John D. Storey * {dagger}, and Robert Tibshirani (2003). Statistical significance for genomewide studies
  • Lecture: Mutiple hypothesis testing

• Final project presentations, March 17, 2008 4-6pm
• Final project presentations, March 19, 2008 3:30-5pm