CSE 527 Computational Biology

Lecture notes

Lecture 1: Course logistics, short intro to molecular biology, example project topics [PPT] [PDF]

Lecture 2: Introduction to Bayesian networks for computational biology [PPT] [PDF]

Lecture 3: Maximum Likelihood Estimation, Expectation Maximization [PPT] [PDF]

Lecture 4: Genetic basics, QTL mapping, Association studies [PPT] [PDF]

Lecture 5: QTL mapping, haplotypes [PPT] [PDF]

Lecture 6: Haplotype reconstruction [PPT] [PDF]

Lecture 7: Disease association studies [PPT] [PDF]

Lecture 8: Linkage analysis [PPT] [PDF]

Lecture 9: Inferring transcriptional regulatory networks I [PDF] [PPT]

Lecture 10: Inferring transcriptional regulatory networks II [PDF] [PPT]

Lecture 11: Advanced topics in inferring regulatory networks [PDF]

Lecture 12: Regulatory motif finding I [PDF]

Lecture 13: Regulatory motif finding II [PDF]

Lecture 14: Inferring the signaling networks I [PDF]

Lecture 15: Inferring the signaling networks II [PDF]

Lecture 16: Sequence alignment [PDF]

Lecture 17: Scoring alignments [PDF]

Lecture 18: Local sequence alignment and heuristic local aligners [PDF]

Lecture 19: Multiple sequence alignment I [PDF]

Lecture 20: Multiple sequence alignment II [PDF]

Reading materials

Lecture 1: Course logistics, short intro to molecular biology, example project topics

Lecture 2-3: Machine learning basics

• Probabilistic Graphical Models: Principles and Techniques. Daphne Koller and Nir Friedman. Chapters 3, 5.1, 5.3, 5.5, 17, 18.1, 18.3, 18.4.

• Feature selection: An Introduction to Variable and Feature Selection. Guyon and Elisseeff. Journal of Machine Learning Research (2003).

• LASSO: Regression Shrinkage and Selection via the Lasso. Robert Tibshirani. Journal of the Royal Statistical Society (1996).

Lecture 4-5: QTL mapping

• Review article: Review of statistical methods for QTL mapping in experimental crosses. Karl W. Broman. Reprinted from Lab Animal 30(7):44-52 (2001).

• Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Lander and Botstein. Genetics (1994).

• Statistical issues in the search for genes affecting quantitative traits in experimental populations. Doerge et al. Stat. Sci. (1997).

• A review of methods for identifying QTLs in experimental crosses. Broman and Speed (1999).

• Genetics and analysis of quantitative traits. Lynch and Walsh. Sinauer Associates, Sunderland, MA, pp. 431-89 (1998).

Lecture 6: Haplotype reconstruction

• Review article: Haplotype phasing: existing methods and new developments. Browning and Browning. Nature Reviews (2011).

• Inference of haplotypes from PCR-amplified samples of diploid populations. Clark. Mol Biol Evol (1990).

• Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Excoffier and Slatkin. Mol Biol Evol (1995).

• SNPHAP: A Program for Estimating Frequencies of Large Haplotypes of SNPs. Clayton. (2002).

• Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms. Niu et al. Am J Hum Genet (2002).

• Partition-Ligation-Expectation-Maximization Algorithm for Haplotype Inference with Single-Nucleotide Polymorphisms. Qin et al. Am J Hum Genet (2002).

Lecture 7: Disease association studies

• Review article: Genome-wide association studies for common diseases and complex traits. Hirschhorn and Daly. Nature Reviews Genetics (2005).

• The future of genetic studies of complex human diseases. Risch and Merikangas. Science (1996).

• A haplotype map of the human genome. The International HapMap Consortium. Nature (2005).

• Efficiency and power in genetic association studies. de Bakker et al. Nature Genetics (2005).

• Evaluating and improving power in whole-genome association studies using fixed marker sets. Pe'er et al. Nature Genetics (2006).

• Score tests for association between traits and haplotypes when linkage phase is ambiguous. Schaid et al. Am J Hum Genet (2002)

• Testing association of statistically inferred haplotypes with discrete and continuous traits in samples of unrelated individuals. Zaytkin et al. Hum Hered (2002).

• On the allelic spectrum of human disease. Reich and Lander. Trends in Genetics (2001).

Lecture 8: Linkage analysis

• The Elston-Stewart algorithm for continuous genotypes and environmental factors. Elston et al. Human Heredity 42, 16-27 (1992).

Lecture 9-11: Inferring transcriptional regulatory networks

• Review article: Analysis of large-scale gene expression data. Gavin Sherlock. Current Opinion in Immunology (2000).

• Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Bhattacharjee et al. PNAS (2001).

• Gene expression profiling predicts clinical outcome of breast cancer. van't Veer et al. Nature (2000).

• Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Segal et al. Nature Genetics (2003).

• Identifying Regulatory Mechanisms using Individual Variation Reveals Key Role for Chromatin Modification. Lee and Pe'er et al. PNAS (2006).

• Learning a Prior on Regulatory Potential from eQTL Data. Lee et al. PLoS Genetics (2009).

• Inferring sub-networks from perturbed expression profiles. Pe'er et al. Bioinformatics (2001).

• Probabilistic Discovery of Overlapping Cellular Processes and Their Regulation. Battle et al, Journal of Computational Biology (2005).

Lecture 12-13: Regulatory motif finding

• Review article: A survey of DNA motif finding algorithms. Das and Dai, BMC Bioinformatics (2007).

• MEME: discovering and analyzing DNA and protein sequence motifs. Bailey et al. Nucl. Acids Res (2006). The MEME Suite

• Computational identification of Cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. Hughes et al. Journal of Molecular Biology (2000). [AlignACE Download Page]

• BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. Liu et al. Pacific Symposium on Biocomputing (2001). [BioProspector Download Page]

• Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Hertz et al. Bioinformatics (1999).

• FootPrinter: a program designed for phylogenetic footprinting. Blanchette and Tompa, Nucl. Acids Res (2003).

• MotifCut: regulatory motifs finding with maximum density subgraphs. Fratkin et al. Bioinformatics (2006).

Lecture 14-15: Inferring protein-signaling networks

• Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data. Sachs et al. Science (2005).

• Bayesian Network Analysis of Signaling Networks: A Primer Pe'er. Science STKE (2005).

• Learning Signaling Network Structures with Sparsely Distributed Data. Sachs et al. Journal of Comp Bio. (2009).

• A Tutorial on Learning with Bayesian Networks. Heckerman. Technical report, Microsoft Research (1995).

• A Bayesian Method for the Induction of Probabilistic Networks from Data. Cooper and Herskovits. Machine Learning, 9, 309-347 (1992).

Lecture 16-18: Pairwise sequence alignment

• Biological sequence analysis: probabilistic models of proteins and nucleic acids. Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison.

Lecture 19-20: Multiple sequence alignment

• CLUSTAL: a package for performing multiple sequence alignment on a microcomputer. Higgins DG, Sharp PM. Gene 73:237-244 (1988).

• CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positions-specific gap penalties and weight matrix choice. Thompson, J.D., Higgins, D.G. and Gibson, T.J. Nucleic Acids Research 22, 4673-4680 (1994).

• MUSCLE: a Multiple Sequence Alignment Method with Reduced Time and Space Complexity. Edgar RC. BMC Bioinformatics (2004). [MUSCLE Download Page]

• ProbCons: Probabilistic Consistency-based Multiple Sequence Alignment. Do CB, Mahabhashyam MS, Brudno M, Batzoglou S. Genome Res. 15(2):330-40 (2005).