Biostatistics & Medical Informatics 776
Computer Sciences 776
Advanced Bioinformatics (Spring 2019)

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) (1/22) 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) (1/24, 1/29) Learning Sequence Motif Models using Gibbs Sampling (PDF, PPTX, Gamma example, Dirichlet example) (1/31, 2/5) Inferring Models of cis-Regulatory Modules using Information Theory (PDF, PPTX) (2/7, 2/12) 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. 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. 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. optional reading 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. Stewart R, Rascon CA, Tian S, Nie J, Barry C, Chu L-F, et al. Comparative RNA-seq analysis in the unsequenced axolotl: the oncogene burst highlights early gene expression in the blastema. PLoS Comput Biol. 2013;9: e1002936. lecture notes Transcript quantification with RNA-Seq (PDF, PPTX) (2/12, 2/14, 2/19) Analysis of alternative splicing with RNA-Seq and probabilistic splice graphs (PDF, PPTX) (2/19, 2/21) Assembling transcriptomes from RNA-seq data (PDF, PPTX) (2/26, 2/28) Comparative RNA-seq for analysis of regeneration in axolotl (PDF, PPTX) RNA Structure Analysis topics: predicting RNA secondary structure, Nussinov/energy-minimization algorithms, stochastic context free grammars searching sequences for a given RNA secondary structure, RSEARC RNA gene recognition via comparative sequence analysis, microRNA gene/target prediction, Inside/Inside-Outside/CYK algorithms -- required reading Chapter 9 in Durbin et al. (alternatively: Manning and Schutze, Chapter 11, Probabilistic Context Free Grammars) 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) (2/28, 3/5) (RNA folding activity, Nussinov algorithm activity) Stochastic Context Free Grammars for RNA Structure Modeling (PDF, PPTX) (3/5, 3/7) (Context-Free Grammar Modification Activity) Epigenomics topics: epigenomic data types, DNase I hypersensitivity, Gaussian processes, convolutional neural networks, interpreting noncoding genetic variants 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 lecture notes Introduction to epigenetics (PDF, PPTX) (3/14) Epigenetics - Predicting TF binding with DNase-Seq and PIQ (PDF, PPTX, Gaussian process introduction) (3/26, 3/28) Genotype Analysis topics: haplotype inference, genome-wide association studies (GWAS), quantitative trait loci (QTL) mapping, multiple hypothesis testing 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. 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 Important Phenotypes (PDF, PPTX) (4/2) GWAS and multiple testing correction (PDF, PPTX) (4/4, 4/9) Interpreting noncoding variants (PDF, PPTX) (4/9, 4/11, 4/16) 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) (4/16, 4/18) 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) (4/18, 4/23) Large-Scale and Whole-Genome Sequence Alignment topics: large-scale alignment, whole-genome 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 - MUMmer (PDF, PPTX) (4/25, 4/30) Alignment of Long Sequences - LAGAN (PDF, PPTX) (4/30, 5/2) Multiple Whole Genome Alignment (PDF, PPTX) (5/2) Lecture Notes Thank you to Professors Mark Craven and Tony Gitter for providing lecture material. These slides, excluding third-party material, are licensed under CC BY-NC 4.0 by Mark Craven, Colin Dewey, and Anthony Gitter.