Syllabus, Readings and Lecture Notes

• Background
  • Topics: Why computational biology, biological information, challenges in computational biology.
  • Required reading
    • L. Hunter. Life and Its Molecules: A Brief Introduction. AI Magazine 25(1):9-22, 2004.
  • Recommended reading
    • Genomics and the future
    • The Genomic Landscape circa 2012
    • D. R. Zerbino, B. Paten, and D. Haussler. "Integrating genomes" Science, vol. 336, no. 6078, pp. 179-182, Apr. 2012.
  • Lecture notes
    • Course Overview (PDF, PPTX) (9/7)
    • Molecular Biology 101 (PDF, PPTX) (9/12)

• Sequence Assembly
  • Topics: Fragment assembly, Sequencing by hybridization, Overlap-layout-consensus
  • Required reading:
    • P. Compeau, P. Pevzner, G. Tesler How to apply de Bruijn graphs to genome assembly. Nat Biotechnol. 2011 Nov 8;29(11):987-91.
  • Recommended reading:
    • N. Nagarajan, M. Pop Sequence assembly demystified. Nat Rev Genet. 2013 Mar;14(3):157-67.
    • S. El-Metwally, T. Hamza, M. Zakaria, M. Helmy. Next-generation sequence assembly: four stages of data processing and computational challenges. PLoS Comput Biol. 2013;9(12):e1003345.
    • M. Baker De novo genome assembly: what every biologist should know. Nature Methods. 2012 9:333-337.
    • J. Simpson, M. Pop The Theory and Practice of Genome Sequence Assembly Annual Review of Genomics and Human Genetics. 2015. 16(1):153-172.
  • Lecture notes
    • Sequence assembly (PDF, PPTX) (9/14), (PDF, PPTX) (9/19), (PDF, PPTX (9/21)

• Sequence Alignment
  • Topics: Introduction to biological sequences, DNA sequence, dynamic programming methods for global and local alignment, gap penalty functions, heuristics in alignment, BLAST, pairwise sequence alignment, multiple sequence alignment
  • Required reading
    • Sections 2.1-2.5, 2.7, 2.8 (1.3 for brief background in probability)
    • Sections 6.1-6.4
  • Recommended reading
    • S.F. Altshul, W. Gish, W. Miller, E.W. Myers, D.J. Lipman, Basic local alignment search tool. J. Mol. Biol. 215, 403-410 (1990).
    • Muscle Muscle
    • Clustal ClustalW
  • Lecture notes
    • Pairwise Sequence Alignment (PDF, PPTX) (9/26, 9/28) (updated 10/4)
    • Intro to Probability for Discrete Variables (PDF, PPTX) (10/3)
    • The statistics of pairwise alignment (PDF, PPTX) (10/3, 10/5) (updated 10/14)
    • Heuristic methods for sequence database searching
    (PDF, PPTX) (10/5)
    • Multiple sequence alignment
    (PDF, PPTX) (10/10), (PDF, PPTX) (10/12)

• Phylogenetic Trees
  • Topics: Distance, parsimony, and probabilistic methods of phylogenetic tree construction, models of sequence evolution
  • Required reading
    • Chapter 7, Sections 7.1-7.5 in Durbin et al.
    • Chapter 8, Sections 8.1-8.3 in Durbin et al.
  • Recommended reading
    • J. Alfoldi, K. Toh. Comparative genomics as a tool to understand evolution and disease.
  • Lecture notes
    • Introduction to Phylogenetic trees (PDF, PPTX) (10/12)
    • Distance-based methods for phylogenetic tree reconstruction (PDF, PPTX) (10/17, 10/19)
    • Parsimony methods for phylogenetic tree reconstruction. (PDF, PPTX) (10/19)
    • Probabilistic methods for phylogenetic tree reconstruction. PDF, PPTX) (10/24, 10/26)

• Annotating genomes
  • Topics: Markov chains, high-order Markov models, Forward/Backward/Viterbi algorithms, applications to genome segmentation and annotation.
  • Required reading
    • Sections 3.0, 3.1
    • Sections 3.2, 3.3 in Durbin et al.
  • Recommended reading
    • Section 3.5
    • J. Ernst and M. Kellis. Discovery and characterization of chromatin states for systematic annotation of the human genome . Nature biotechnology, vol. 28, no. 8, pp. 817-825, Aug. 2010
    • A. Siepel and D. Haussler, Phylogenetic hidden markov models, in Statistical Methods in Molecular Evolution, 2005, pp. 325-351.0
  • Lecture notes
    • Markov models. (PDF, PPTX) (11/2, 11/7)
    • Hidden Markov models. (PDF, PPTX) (11/7, 11/9)
    • Hidden Markov model parameter estimation. (PDF, PPTX) (11/9, 11/14)
    • HMM applications. (PDF, PPTX) (11/14, 11/16)

• Clustering approaches to ``omic'' datasets.
  • Topics: high-throughput technologies, clustering algorithms, evaluation of clusters
  • 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.
  • 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.
    • A. R. Joyce and B. Palsson. The model organism as a system: integrating 'omics' data sets. Nature Molecular Cell Biology, 2006.
  • Lecture notes
    • High-throughput omic datasets and clustering. (PDF, PPTX) (11/16)
    • Flat clustering. (PDF, PPTX) (11/22)
    • Hierarchical clustering. (PDF, PPTX) (11/28)
    • Clustering evaluation. (PDF, PPTX) (11/28)

• Modeling and analysis of networks
  • Topics: Biological networks, computational problems in network biology, Bayesian networks, module networks, parameter and structure learning, regression-based network inference, network applications
  • Required reading
    • N. Friedman, M. Linial, I. Nachman, and D. Pe'er Using Bayesian networks to analyze expression data. Journal of Computational Biology 7(3-4):601-620, 2000.
    • E. Segal, D. Pe'er, A. Regev, D. Koller, N. Friedman Module networks.
  • Recommended reading
    • V. A Huynh-Thu, A. Irrthum, L. Wehenkel, P. Geurts. Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. Plos One 5(9), 2010.
    • R. D. Smet and K. Marchal. Advantages and limitations of current network inference methods. Nature Reviews Microbiology. 8:717-729, 2010
    • 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.
    • Inferring cellular networks - a review
  • Lecture notes
    • Introduction to biological molecular networks. (PDF, PPTX) (11/28, 11/30)
    • Network structure learning: Bayesian networks. (PDF, PPTX) (11/30,12/5,12/7)
    • Module networks. (PDF, PPTX) (12/7,12/12)
    • Dependency networks. (PDF, PPTX) (12/12)

• Final thoughts
  • Final thoughts. (PDF, PPTX) (12/12)

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