Biostatistics & Medical Informatics 776
Computer Sciences 776
Advanced Bioinformatics (Spring 2016)

General Course Information Course Overview Announcements Syllabus, Readings, Lecture Notes Schedule Homework, Project, Exam Info Past Semesters

Syllabus, Readings and Lecture Notes Course Overview lecture notes Course Overview (PDF, PPTX) (1/19) Motif and cis-Regulatory Module (CRM) Modeling topics: learning motif models, learning models of cis-regulatory modules, Gibbs sampling, Dirichlet priors, parameter tying, heuristic search, HMM structure search, sequence entropy and mutual information required reading T. Bailey and C. Elkan. The value of prior knowledge in discovering motifs with MEME. In Proceedings of the 3rd International Conference on Intelligent Systems for Molecular Biology, pp. 21-29, 1995. C. Lawrence, S. Altschul, M. Boguski, J. Liu, A. Neuwald, and J. Wootton. Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science 262:208-214, 1993. O. Elemento, N. Slonim and S. Tavazoie. A universal framework for regulatory element discovery across all genomes and data types. Molecular Cell 28(2):337-350, 2007. (Supplemental materials containing key methodological details) optional reading M. Tompa et al. Assessing computational tools for the discovery of transcription factor binding sites. Nature Biotechnology 23(1):137-144, 2005. Y. Chen and M. Gupta. EM Demystified: An Expectation-Maximization Tutorial. Technical Report, Department of Electrical Engineering, University of Washington, February 2010. C.B. Do and S. Batzoglou. What is the expectation maximization algorithm? Nature Biotechnology 26(8): 897-899, 2008. optional viewing Seeing Single Molecules Move lecture notes Learning Sequence Motif Models using EM (PDF, PPTX) (1/21, 1/26) Learning Sequence Motif Models using Gibbs Sampling (PDF, PPTX) (1/26, 1/28, 2/2) Inferring Models of cis-Regulatory Modules using Information Theory (PDF, PPTX) (2/2, 2/4) Genotype Analysis topics: haplotype inference, genome-wide association studies (GWAS), quantitative trait loci (QTL) mapping, interpreting noncoding variants, epigenomic data, neural networks, multiple hypothesis testing required reading W. Noble. How does multiple testing correction work?. Nature Biotechnology 27(12):1135-1137, 2009. J. Zhou and O. Troyanskaya. Predicting effects of noncoding variants with deep learning–based sequence model. Nat Methods 12(10): 931-934, 2015. optional reading J. Storey and R. Tibshirani. Statistical Significance for Genomewide Studies. Proceedings of the National Academy of Sciences 100(16):9440-9445, 2003. L.D. Ward and M. Kellis. Interpreting noncoding genetic variation in complex traits and human disease. Nat Biotechnol 30(11): 1095-1106, 2012. Y. LeCun, Y. Bengio and G. Hinton. Deep learning. Nature 521(7553):436-444, 2015. lecture notes Linking Genetic Variation to Important Phenotypes (PDF, PPTX) (2/9, 2/11) GWAS and multiple testing correction (PDF, PPTX) (2/11, 2/16, 2/18) Interpreting noncoding variants (PDF, PPTX) (2/18, 2/23, 2/25) RNA-Seq and Mass Spectrometry topics: RNA-Seq technology, transcript quantification with RNA-Seq, peptide and protein identification with mass spectrometry, alternative splicing, splice graphs required reading B. Li, V. Ruotti, R.M. Stewart, J.A. Thomson, and C.N. Dewey. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 26(4): 493-500, 2010. H. Steen and M. Mann. The ABC's (and XYZ's) of peptide sequencing. Nat Rev Mol Cell Biol 5(9): 699-711, 2004. optional reading Z. Wang, M. Gerstein, and M. Snyder. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1): 57-63, 2009. L.H. LeGault and C.N. Dewey. Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs. Bioinformatics 29(18): 2300-2310, 2013. A.I. Nesvizhskii. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J Proteomics 73(11): 2092-2123, 2010. J.K. Eng, B. Fischer, J. Grossmann, and M.J. MacCoss. A Fast SEQUEST Cross Correlation Algorithm. J Proteome Res 7(10): 4598-4602, 2008. lecture notes Transcript quantification with RNA-Seq (PDF, PPTX) (3/1, 3/10) Analysis of alternative splicing with RNA-Seq and probabilistic splice graphs (PDF, PPTX) (class canceled) Mass spectrometry (PDF, PPTX) (3/15, 3/17) Biological Network Analysis topics: biological network evolution, network modules, network alignment, pathway identification required reading R. Sharan and T. Ideker. Modeling cellular machinery through biological network comparison. Nat Biotech, 24(4):427-433, 2006. E. Yeger-Lotem, L. Riva, L.J. Su, A.D. Gitler, A.G. Cashikar, O.D. King, P.K. Auluck, M.L. Geddie, J.S. Valastyan, D.R. Karger, S. Lindquist, and E. Fraenkel. Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nat Genet 41(3):316-323, 2009. optional reading D-Y. Cho, Y-A. Kim, and T.M. Przytycka. Chapter 5: Network Biology Approach to Complex Diseases. PLoS Comput Biol, 8(12):e1002820, 2012. R. Sharan, S. Suthram, R.M. Kelley, T. Kuhn, S. McCuine, P. Uetz, T. Sittler, R.M. Karp, and T. Ideker. Conserved patterns of protein interaction in multiple species. Proceedings of the National Academy of Sciences 102(6):1974–1979, 2005. J.W. Chinneck. Practical Optimization: A Gentle Introduction. lecture notes Comparative network analysis (PDF, PPTX) (3/17, 3/29) Identifying signaling pathways (PDF, PPTX) (3/31, 4/5) Gene Finding topics: the gene finding task, maximal dependence decomposition, interpolated Markov models, pair HMMs, GENSCAN required reading S. Salzberg, A. Delcher, S. Kasif, and O. White. Microbial gene identification using interpolated Markov models. Nucleic Acids Research 26(2):544-548, 1998. Sections 3.4, 3.5 in Durbin et al. C. Burge and S. Karlin. Prediction of complete gene structures in human genomic DNA. Journal of Molecular Biology 268(1):78-94, 1997. Sections 4.1, 4.2 in Durbin et al. optional reading I. Korf, P. Flicek, D. Duan, and M. Brent. Integrating genomic homology into gene structure prediction. Bioinformatics 17(Suppl. 1):S140-S148, 2001. L. Pachter, M. Alexandersson and S. Cawley. Applications of generalized pair hidden Markov models to alignment and gene finding problems. Proceedings of the Fifth Annual International Conference on Computational Biology (RECOMB), 241-248, 2001. lecture notes Interpolated Markov Models for Gene Finding (PDF, PPTX) (4/5, 4/7) Eukaryotic Gene Finding: The GENSCAN System (PDF, PPTX) (4/7, 4/12) Comparative Gene Finding (abridged) (PDF, PPTX) (4/12, 4/14) Large-Scale and Whole-Genome Sequence Alignment topics: large-scale alignment, whole-genome alignment, suffix trees, k-mer tries, sparse dynamic programming, longest increasing subsequence problem, Markov random fields, MUMmer, LAGAN, Mercator required reading 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. M. Brudno, C. Do, G. Cooper, M. Kim, E. Davydov, NISC Comparative Sequencing Program, E. Green, A. Sidow, and S. Batzoglou. LAGAN and Multi-LAGAN: Efficient Tools for Large-Scale Multiple Alignment of Genomic DNA. Genome Research 13:721-731, 2003. optional reading E. Ukkonen. On-line Construction of Suffix Trees Algorithmica 14(3):249-260, 1995. Chapter 3 of C. Dewey. Whole-genome alignments and polytopes for comparative genomics. PhD thesis. University of California, Berkeley, 2006. lecture notes Alignment of Long Sequences (PDF, PPTX) (4/14, 4/19) Multiple Whole Genome Alignment (PDF, PPTX) (4/21, 4/26) RNA Structure Analysis topics: predicting RNA secondary structure, Nussinov/energy-minimization algorithms, stochastic context free grammars, Inside/Inside-Outside/CYK algorithms required reading Chapter 9 in Durbin et al. Sections 10.1, 10.2 in Durbin et al. optional reading Y. Sakakibara, M. Brown, R. Hughey, I. Mian, K. Sjolander, R. Underwood and D. Haussler. Stochastic context-free grammars for tRNA modeling. Nucleic Acids Research 22(23): 5112-5120, 1994. R. Klein and S. Eddy. RSEARCH: Finding homologs of single structured RNA sequences. BMC Bioinformatics 4(44), 2003. lecture notes RNA Secondary Structure Prediction (PDF, PPTX) (4/26, 4/28) Stochastic Context Free Grammars for RNA Structure Modeling (PDF, PPTX) (4/28) Protein Structure Prediction topics: secondary structure prediction, threading, branch and bound search, ROSETTA required reading K. Dill and J. MacCallum. The protein-folding problem, 50 years on. Science, 338(6110): 1042-1046, 2012. R. Lathrop and T. Smith.Global Optimum Protein Threading with Gapped Alignment and Empirical Pair Score Functions. Journal of Molecular Biology, 255(4):641-665, 1996. optional reading B. Rost. Protein Structure Prediction in 1D, 2D and 3D. In The Encyclopaedia of Computational Chemistry, 3:2242-2255, 1998. C. Rohl, C. Strauss, K. Misura and D. Baker. Protein Structure Prediction using Rosetta. Methods in Enzymology, 383:66-93, 2004. lecture notes Introduction to Protein Structure Prediction (PDF, PPTX) (5/3) Protein Threading (PDF, PPTX) (5/5) Lecture Notes Thank you to Professors Mark Craven and Colin Dewey for providing lecture material.