Biostatistics & Medical Informatics 776
Computer Sciences 776
Advanced Bioinformatics (Spring 2020)

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

Syllabus, Readings and Lecture Notes Course Overview lecture notes Course Overview (PDF, PPTX) (1/21) Motif and cis-Regulatory Module (CRM) Modeling topics: learning motif models, learning models of cis-regulatory modules, Gibbs sampling, Dirichlet priors, parameter tying, sequence entropy, 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. M.T. Weirauch et al. Evaluation of methods for modeling transcription factor sequence specificity. Nature Biotechnology 31(2): 126-134, 2013. C.B. Do and S. Batzoglou. What is the expectation maximization algorithm? Nature Biotechnology 26(8): 897-899, 2008. P. Resnik and E. Hardisty. Gibbs sampling for the uninitiated. UMIACS-TR-2010-04, 2010. optional viewing Seeing Single Molecules Move lecture notes Learning Sequence Motif Models using EM (PDF, PPTX) (1/23, 1/28) Learning Sequence Motif Models and Gibbs Sampling (PDF, PPTX, Gamma example, Dirichlet example) (1/30) Inferring Models of cis-Regulatory Modules using Information Theory (PDF, PPTX) (2/4, 2/6) Genotype Analysis topics: haplotype inference, genome-wide association studies (GWAS), quantitative trait loci (QTL) mapping, multiple hypothesis testing, convolutional neural networks, interpreting noncoding genetic variants required reading J. Storey and R. Tibshirani. Statistical Significance for Genomewide Studies. Proceedings of the National Academy of Sciences 100(16):9440-9445, 2003. 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 W. Noble. How does multiple testing correction work?. Nature Biotechnology 27(12):1135-1137, 2009. L.D. Ward and M. Kellis. Interpreting noncoding genetic variation in complex traits and human disease. Nat Biotechnol 30(11): 1095-1106, 2012. C. Angermueller, T. Parnamaa, L. Parts, and O. Stegle. Deep learning for computational biology. Mol Syst Biol 12(7):878, 2016. lecture notes Linking Genetic Variation to Phenotypes (PDF, PPTX) (2/11) GWAS, multiple testing correction and QTLs (PDF, PPTX) (2/13, 2/18) Interpreting noncoding variants (PDF, PPTX) (2/18, 2/20) Epigenomics topics: epigenomic data types, DNase I hypersensitivity, Gaussian processes required reading R.I. Sherwood, T. Hashimoto, C.W. O'Donnell, S. Lewis, A.A. Barkal, J.P. van Hoff, V. Karun, T. Jaakkola, and D.K. Gifford. Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nat Biotechnol 32(2):171–-178, 2014. J. Lever, M. Krzywinski, and N. Altman. Points of Significance: Classification evaluation. Nat Methods 13(8):603-604, 2016. optional reading Roadmap Epigenomics, etc. Integrative analysis of 111 reference human epigenomes. Nature 518: 317-330, 2015. lecture notes Introduction to epigenetics (PDF, PPTX) (2/25) Epigenetics - Predicting TF binding with DNase-Seq and PIQ (PDF, PPTX, Gaussian process introduction) (2/27, 3/3, 3/5) Mass Spectrometry topics: peptide and protein identification with mass spectrometry required reading 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 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 Mass spectrometry (PDF, PPTX) (3/10) Biological Network Analysis topics: protein interactions, pathway identification, linear programming, min cost flow required reading 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. J.W. Chinneck. Practical Optimization: A Gentle Introduction. lecture notes Identifying signaling pathways (PDF, PPTX) (3/24, 3/26) Machine Learning Modeling topics: unsupervised learning, partitioning vs. hierarchical clustering, classification, support vector machine required reading M. B. Eisen, P. T. Spellman, P. O. Brown, D. Botstein. Cluster analysis and display of genome-wide expression patterns . PNAS 95(25):14863-14868, 1998. W. S. Noble. What is a support vector machine? . Nat Biotech 24:1565-1567, 2006. optional reading Machine Learning from Nature Methods. K. Y. Yip, C. Cheng, M. Gerstein. Machine learning and genome annotation: a match meant to be?. Genome Biology 14:205, 2013. P. Langfelder, S. Horvath. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559, 2008. lecture notes Machine learning applications in Bioinformatics (PDF, PPTX) (3/31, 4/2) RNA-seq and Transcript assembly topics: RNA-seq technology, transcript quantification, alternative splicing, splice graphs, transcript assembly 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. 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. optional reading Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28: 511–515. Z. Wang, M. Gerstein, and M. Snyder. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1): 57-63, 2009. A. Conesa, P. Madrigal, S. Tarazona, D. Gomez-Cabrero, A. Cervera, A. McPherson, M.W. Szczesniak, D.J. Gaffney, L.L. Elo, X. Zhang, and A. Mortazavi. A survey of best practices for RNA-seq data analysis. Genome Biology 17(13), 2016. Pertea M, Pertea GM, Antonescu CM, Chang T-C, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33: 290-295. lecture notes Transcript quantification and Analysis of alternative splicing with RNA-Seq (PDF, PPTX) (4/7,4/9) RNA Structure Analysis topics: predicting RNA secondary structure, dynamic programming, Nussinov/energy-minimization algorithms, stochastic context free grammars required reading Chapter 9 in the recommended text, Durbin et al. (alternatively: Manning and Schutze, Chapter 11, Probabilistic Context Free Grammars) Sections 10.1, 10.2 in the recommended text, 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 Modeling and Prediction (PDF, PPTX) (4/14) (RNA folding activity, Nussinov algorithm activity) Stochastic Context Free Grammars for RNA Structure Modeling (PDF, PPTX) (4/16) (Context-Free Grammar Modification Activity) Gene Finding topics: gene finding, interpolated Markov models, generalized HMMs, pair HMMs 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 3.1, 3.5 in Durbin et al. optional reading Sections 3.4, 4.1 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. 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. J.M. Mudge and J. Harrow. The state of play in higher eukaryote gene annotation. Nat Rev Genet 17(12):758-772, 2016. lecture notes Gene Finding (PDF, PPTX) (4/21, 4/23) Large-Scale Sequence Alignment topics: large-scale alignment, suffix trees, k-mer tries, longest increasing subsequence problem, MUMmer 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. A. Darling, B. Mau, F. Blattner and N. Perna. Mauve: Multiple Alignment of Conserved Genomic Sequence with Rearrangements. Genome Research 14:1394-1403, 2004. 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) (4/28, 4/30) Lecture Notes Thank you to Professors Mark Craven, Tony Gitter and Colin Dewey for providing lecture material. These slides, excluding third-party material, are licensed under CC BY-NC 4.0 by Mark Craven, Colin Dewey, Anthony Gitter and Daifeng Wang.