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UCLA MCDBIO 172 - mcdbio172_syl11w

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Molecular, Cell and Developmental Biology 172 Genomics and Bioinformatics Course Instructor: Matteo Pellegrini [email protected] Office: Life Sciences 4219 Extension: 5-0012 Units: 5 Grading basis: Letter grade only Instructional format: Lecture: 3 hours per week (Kinsey Pavillion 1200B) Discussion section: 1 hour per week (Young Hall 4340) Pre-requisites: Two upper division MCDB courses Description of course: Traditionally, biologists have applied a reductionist approach to uncover the detailed function of a limited number of molecules. However, technological progress during the past few decades has enabled a new field of biology, called genomics, which focuses on studying the complete repertoire of molecules in a cell. The most visible example of this approach was the completion of the human genome sequence. This course will review some the most significant developments in the field of genomics. In parallel, students will be introduced to some of the fundamental concepts of bioinformatics that are necessary to interpret genomic data. The course will cover the following topics: (a) the human genome, and genetic approaches to study the function of individual genes, (b) a discussion of fundamental bioinformatics algorithms that are used to study the relationship between nucleotide and protein sequences, and to reconstruct their evolution, (c) sequencing and microarray technologies, and their use in measuring changes in gene expression, (d) the analysis of microarray data, including such topics as clustering and promoter analysis, (e) topics in the field of proteomics including the study of protein expression and interactions, (f) a review of epigenomics, the study of DNA methylation and chromatin modifications and finally (g) we will conclude with a discussion of systems biology, or computational approaches to integrate varied genomic data in order to gain a more complete understating of cellular biology.The course material will rely heavily on scientific publications rather than a textbook. As a result, students will also develop the ability to read and understand scientific reports published in the top journals. Office Hours Thursday 2:00 to 4:00, 300C Terasaki Life Sciences Building Web site: Course requirements: 5 homework problem sets Lab project including initial description, midterm report and final report as well as a final presentation 2 Midterms 1 Final Exam (cumulative) Textbook: None. Reading material and copies of research papers will be posted on the course web site. Grading: Homework – 10% Midterms – 40% (20% each) Final – 30% Lab assignments – 20% Effective Date: Winter 2011LECTURE SCHEDULE FOR MCDB XXX Week 1 1 Jan 3, Course overview 2 Jan 5, The human genome 3 Jan7, Population Genetics Week 2 4 Jan 10, Sequence alignments: Needleman-Wunsch 5 Jan 12, Sequence alignments: Substitution matrices and BLAST 6 Jan 14, Genome analysis: Phylogentic profiles, operons and genes fusions Week 3 7 Jan 17 MLK Holiday 8 Jan 10, Genetic screens in yeast 9 Jan 21, Genetic screens in yeast Week 4 10 Jan 24, E-map and Gene Ontology 11 Jan 26, MIDTERM 12 Jan 28, RNAi screens 13 Week 5 14 Jan 31, Expression Microarrays and tiling arrays 15 Feb 2, RNA-seq 16 Feb 4, Proteomics: techniques (MS, 2-hybrid) Week 6 17 Feb 7, Phosphoproteomics 18 Feb 9, Proteomics II – protein abundances and interactions 19 Feb 11, Clustering Week 7 20 Feb 14, MIDTERM 21 Feb 16, Epigenomics: NOVA episode ghost in your 22 Feb 18, Epigenomics: DNA methylation Week 8 23 Feb 21, President’s day 24 Feb 23 Epigenomics: nucleosome positioning 25 Feb 25 Transcriptional Regulation Maps Week 926 Feb 28, Encode 27 March 2, Network Motifs 28 March 4, Systems biology: yeast metabolic cycle Week 10 29 March 7, Population Genetics: population structures and PCA 30 March 9, Flux Balance Analysis 31 March 11, Microbiome Week 11 32 Finals Course Reading List: Textebook: none Additional reading: Week 1 Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860-921 (2001). Week 2 Needleman, S. B. & Wunsch, C. D. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48, 443-53 (1970). Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J Mol Biol 215, 403-10 (1990). Dayhoff, M.O., Schwartz, R.M. & Orcutt, B.C. A model of evolutionary change in proteins. Atlas of protein sequence and structure 1978. Sneath, P. H. & Sokal, R. R. Numerical taxonomy. Nature 193, 855-60 (1962). Week 3 Pellegrini, M., Marcotte, E. M., Thompson, M. J., Eisenberg, D. & Yeates, T. O. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci U S A 96, 4285-8 (1999)Bowers, P. M. et al. Prolinks: a database of protein functional linkages derived from coevolution. Genome Biol 5, R35 (2004). Lee, I., Date, S. V., Adai, A. T. & Marcotte, E. M. A probabilistic functional network of yeast genes. Science 306, 1555-8 (2004). Week 4 Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387-91 (2002). Giaever, G. et al. Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. Proc Natl Acad Sci U S A 101, 793-8 (2004). Kamath, R. S. et al. Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature 421, 231-7 (2003). Tong, A. H. et al. Global mapping of the yeast genetic interaction network. Science 303, 808-13 (2004). Week 5 Hughes, T. R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109-26 (2000). Yamada, K. et al. Empirical analysis of transcriptional activity in the Arabidopsis genome. Science 302, 842-6 (2003). Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95, 14863-8 (1998). Segal, E., Friedman, N., Koller, D. & Regev, A. A module map showing conditional activity of expression modules in cancer. Nat Genet 36, 1090-8 (2004). Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34, 267-73 (2003). David L, Huber W, Granovskaia M, Toedling J, Palm CJ, Bofkin L,

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