University of Wisconsin - Madison: Biostatistics & Medical Informatics 776
Computer Sciences 776
Advanced Bioinformatics (Spring 2022)

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) (1/25) Motif and cis-Regulatory Module (CRM) Modeling topics: Expectation Maximization (EM) algorithm, learning motif models, learning models of cis-regulatory modules, Gibbs sampling, 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) (1/27, 2/1) Learning Sequence Motif Models using Gibbs Sampling (PDF, PPTX, Gamma example, Dirichlet example) (2/3) Inferring Models of cis-Regulatory Modules using Information Theory (PDF, PPTX) (2/8) 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. W. Noble. How does multiple testing correction work?. Nature Biotechnology 27(12):1135-1137, 2009. optional reading GTEx Consortium Genetic effects on gene expression across human tissues. Nature 550, pages204-213, 2017. lecture notes Linking Genetic Variation to Phenotypes (PDF, PPTX) (2/10) GWAS, multiple testing correction and QTLs (PDF, PPTX) (2/15, 2/17) Epigenomics topics: Epigenomic data types, DNase I hypersensitivity, Gaussian processes, ROC curve 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 L.D. Ward and M. Kellis. Interpreting noncoding genetic variation in complex traits and human disease. Nat Biotechnol 30(11): 1095-1106, 2012. Roadmap Epigenomics, etc. Integrative analysis of 111 reference human epigenomes. Nature 518: 317-330, 2015. lecture notes Introduction to epigenetics (PDF, PPTX) (2/22) Epigenetics - Predicting TF binding with DNase-Seq and PIQ (PDF, PPTX, Gaussian process introduction) (2/24, 3/1) RNA-seq Analysis and Gene Discovery topics: RNA-seq technology, transcript quantification, gene finding, interpolated Markov models 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. 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 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. 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. 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. lecture notes RNA-Seq analysis and gene discovery (PDF, PPTX) (3/3,3/8) Network Biology topics: biological network analysis, 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 T. Ideker, and R. Nussinov. Network approaches and applications in biology. PLoS Comput Biol, 13(10):e1005771, 2017. 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. A. Barabasi, and Z. N. Oltvai. Network biology: understanding the cell's functional organization. Nat Rev Genet, 5:101-113, 2004. J.W. Chinneck. Practical Optimization: A Gentle Introduction. lecture notes Network biology (PDF, PPTX) (3/22, 3/24) Machine Learning in Bioinformatics 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. M. Libbrecht, W. S. Noble. Machine learning applications in genetics and genomics . Nat Rev Genet. 16, 321-332, 2015. K. Y. Yip, C. Cheng, M. Gerstein. Machine learning and genome annotation: a match meant to be?. Genome Biology 14:205, 2013. lecture notes Machine Learning in Bioinformatics (PDF, PPTX) (3/29, 3/31) Deep Learning Applications topics: deep learning, convolutional neural networks, interpreting noncoding genetic variants required reading C. Angermueller, T. Parnamaa, L. Parts, and O. Stegle. Deep learning for computational biology. Mol Syst Biol 12(7):878, 2016. 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 B. Alipanahi, A. Delong, M. T. Weirauch, and B. J. Frey. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nature Biotech 33:831-838, 2015. J. Jumper et al. Highly accurate protein structure prediction with AlphaFold. Nature 596:583-589, 2021. lecture notes Interpreting noncoding variants by deep learning (PDF, PPTX) (4/5, 4/7) Single-cell RNA-seq Analysis topics: single cell RNA-seq processing and analysis, cell clustering, cell-type regulatory networks, single cell deconvolution required reading M. Luecken, F. Theis. Current best practices in single-cell RNA-seq analysis: a tutorial . Mol Syst Bio. 15:e8746, 2019 L. Maaten, G. Hinton. Visualizing Data using t-SNE . J. of Machine Learning Research 9:2579-2605, 2008 B. Sande et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis . Nat Protoc. 15(7):2247-2276, 2020. optional reading L. McInnes et al. UMAP: Uniform Manifold Approximation and Projection . Journal of Open Source Software 3(29), 861, 2018 V. D. Blondel et al. Fast unfolding of communities in large networks . J. Stat. Mech. P10008, 2088 A. Pratapa et al. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data . Nature Methods 17(2):147-154, 2020. T. Stuart, R. Satija. Integrative single-cell analysis. Nat Rev Genet. 20, 257-272, 2019. A. Cobos et al. Benchmarking of cell type deconvolution pipelines for transcriptomics data . Nat Commun. 11, 5650, 2020. lecture notes single cell RNA-seq processing and analysis (PDF, PPTX) (4/12, 4/14) cell-type annotation, regulatory networks and deconvolution (PDF, PPTX) (4/19, 4/21) Multi-modal Data Analysis topics: multi-modal data, multi-view learning, manifold alignment reading C. Zhu, S. Preissl, B. Ren. Single-cell multimodal omics: the power of many . Nat Methods 17, 11-14, 2020. N. Nguyen, D. Wang. Multiview learning for understanding functional multiomics . PLoS Comput Biol. 16(4): e1007677, 2020. J. Huang, J. Sheng, D. Wang. Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics . Commun Biol 4, 1308, 2021. N. Nguyen, J. Huang, D. Wang. A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data . Nat Comput Sci 2, 38-46, 2022. Y. Hao et al. Integrated analysis of multimodal single-cell data . Cell 184(13):3573-3587, 2021. lecture notes Multi-modal data integration and analysis (PDF, PPTX) (4/26, 4/28) 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.