Syllabus, Readings and Lecture Notes

• Background
  • topics: Why computational biology, biological information, the Central Dogma, challenges in computational biology, general topics in class.
  • required reading
    • L. Hunter. Life and Its Molecules: A Brief Introduction. AI Magazine 25(1):9-22, 2004.
  • recommended reading
    • Nucleic Acids Research Web Server Issue
    • Genomics and the future
    • The Genomic Landscape circa 2012
  • lecture topics
    • Course Overview pdf ppt 9/4
    • Introduction to Molecular Biology pdf ppt 9/6

• Assembling Genomes
  • topics: Fragment assembly, Greedy algorithms, Overlap-layout-consensus, Graph-theoretic concepts, Debruijn graphs, Hamiltonian path, Euler path
  • required reading:
    • M. Pop. Genome assembly reborn: recent computational challenges. Briefings in Bioinformatics 10(4):354-366, 2009.
  • recommended reading:
    Jay Shendure and Hanlee Ji. Next generation DNA sequencing
  • lecture notes
    • Sequence Assembly pdf ppt 9/11, 9/13
    • Algorithms for assembly: Velvet.

• Aligning and Comparing Genomes I
  • topics: dynamic programming methods for global and local alignment, linear and affine gap penalty functions, the BLAST algorithm
  • required reading
    • Sections 2.1-2.3. Durbin Chapter 2
    • 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.
    • D. R. Zerbino, B. Paten, D. Haussler Integrating Genomes
  • lecture notes
    • Pairwise alignment, dynamic programming, global and local alignment pdf pptx 9/18 (Updated 9/25). Global alignment example
    • Affine Gap penalty pdf pptx (9/25)
    • BLAST algorithm, heuristic methods for searching sequence database pdf pptx (9/27)
    • Probability Primer pdf pptx (10/02)

• Aligning and Comparing Genomes II
  • topics: dynamic programming for MSA, heuristic methods for MSA, probability distributions, data likelihood, heuristic and probabilistic methods of phylogenetic tree construction, Felsentein's algorithm identifying genetic variation
  • required reading
    • Chapter 6, Sections 6.1-6.4 in Durbin et al.
    • Chapter 7 in Durbin et al.
  • recommended reading
    • Population genetics inference from sequence variation.
  • lecture notes
    • Multiple sequence alignment (MSA) problem, dynamic programming and heuristic methods. pdf pptx (10/04)
    • Introduction to phylogenetic trees. pdf pptx (10/09,10/11)
    • Parsimony methods for phylogenetic tree construction. pdf pptx (10/11,10/16)
    • Probabilistic methods for phylogenetic trees construction. pdf pptx (10/16,10/18)

• Annotating Genomes
  • topics: Markov chains, high-order 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.
  • recommended reading
    ChromHMM
  • lecture notes
    • Markov models, identifying CpG islands pdf pptx
    • Hidden Markov models pdf pptx (10/25,10/30,11/1: updated)

• Analyzing ``Omics'' datasets.
  • topics: high-throughput technologies, clustering algorithms, biclustering
  • required reading
    • C. Manning, P. Raghavan and H. Schutze. Chapter 16: Flat Clustering. Introduction to Information Retrieval, Cambridge University Press, 2008.
    • C. Manning, P. Raghavan and H. Schutze. Chapter 17: Hierarchical Clustering. Introduction to Information Retrieval, Cambridge University Press, 2008.
    • C. Manning and H. Schutze. Chapter 15: Clustering. Foundations of Statistical Natural Language Processing,
  • recommended reading
    • 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.
    • Bernard Paulson's paper on systems biology
  • lecture notes
    • Introduction to functional genomics/omics datasets pptx pdf
    • Clustering methods: Hierarchical, K-means clustering, Gaussian mixture models pptx pdf 11/13, 11/15, 11/20. Example expression data for demo from Gasch et al. and Segal et al.

• Inference and Analysis of networks
  • topics: biological networks, Bayesian networks, module networks, parameter and structure learning, linear regression, regression-based network inference
  • required reading
    • N. Friedman. Inferring cellular networks using probabilistic graphical models. Science 303(5659): 799-805, 2004.
    • 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.
    • 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
    • Module networks.
    • Dependency networks
    • recommended reading
    • TIGRESS
    • Lasso (Quite technical)
    • Meinshausen&Buhlman (Also quite technical)
  • lecture notes
    • Introduction to Molecular Networks pptx pdf
    • Probabilistic graphical models: Bayesian networks pptx pdf
    • Probabilistic graphical models: Bayesian networks 2 pptx pdf
    • Linear regression, Lasso, Regression-based networks pptx pdf
• Class Review pptx pdf

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