• Logistics. Genomics. High-throughput sequencing: DNA microarrays, protein-protein interactions, chromatin-IP, global genetic analysis. ENCODE data. http://www.genome.gov/26023194

• "The ENCODE (ENCyclopedia Of DNA Elements) Project" article [pdf]
• A review article on transcription by Ernst and Smale [pdf]

• ENCODE, UCSC Genome Browser, Programming Libraries for Computational Genomics at UCSC

• Introduction to computational genomics. Mathematical Background.

• Durbin, chapters 1-2.

• Motif discovery. Probability background. Sensitivity/specificity. Classification.

• Durbin Appendices Pages 299-313, 319-325.

Haussler
Notes [pdf]

• HMMs I: Principles of HMMs, HMMs models for gene finding

• Durbin Chap 3 & 5.
• Chap 4 in Computational Methods in Molecular Biology, SL Salzberg, DB Searls, and S Kasif (Eds), Elsevier Science, 1998.
• "An introduction to Hidden markov models for biological sequences" by Anders Krogh

• Unsupervised Learning I. Clustering.

• HMMs II: Training HMMs, Viterbi algorithm, Forward & backward algorithm, Higher-order markov chains

• Durbin chap 6 pp 149-159 (skip the  first part of chap 6)

• HMMs III: pairwise & multiple sequence alignment, learning HMM parameters from observations, Expectation-Maximization with Baum-Welch, pair-HMMs for comparative genomics

• Unsupervised Learning II. Probabilistic formulation. Mixture models.

• Unsupervised learning III. Gaussian Mixtures. Biclustering.

• Supervised learning I. General principles. Loss functions. Conditional maximum likelihood.

• Supervised learning II. Linear regression, GLMs, perceptrons, Neural nets.

• Supervised learning III. SVMs.

• Evolutionary Reconstruction I. Parsimony, Stationary markov processes, Rate matrices, Maximum Likelihood, Maximum a posteriori, Felsensteins Post-order traversal

• Evolutionary reconstruction II. PhyloHMMs.

• Whole genome comparative genomics.

• Integrative Genomics I. General probabilistic graphical models. Bayesian Networks. Exact inference.

• Integrative Genomics II. Learning BNs from data. EM and structural EM.

• Genome variation and disease association.

• Grammars for RNA recognition. Stochastic context-free grammers.Application of RNA recognition. Identifying miRNAs and their targets. Discovery of new RNA genes.

1. Chapters 9 and 10 from Durbin
2. (optional) Pedersen, et al. 2006. PLoS Computational Biology

• Whole genome reconstruction.

• Final presentations and research papers due.