10-810 Computational Genomics

Syllabus and (tentative) Course Schedule

Date	Lecture	Topics	Readings	Handouts
Module 1: Introduction
Mon 1/12	1. Intro to Molecular Biology Slides		The Cartoon Guide to Genetics (buy, additional material) Introduction to Biology book by William Cohen (book, additional material)
Wed 1/14	2. Intro to Cell Biology & Genetics Slides		The Cartoon Guide to Genetics (buy, additional material) Introduction to Biology book by William Cohen (book, additional material)
Mon 1/19	No class
Module 2: Molecular Genomics: biological sequence analysis
Wed 1/21	3. DNA sequencing and genome assembly Slides	Sequencing strategies Assembly algorithms	ARACHNE: A whole-genome shotgun assembler Human whole genome shotgun sequencing - Weber & Myers Genomic mapping by fingerprinting random clones - Lander & Waterman Homepage of the ARACHNE assembler
Mon 1/26	4. Gene recognition (1) Slides Annotated Slides	Probability distributions on sets of sequence The structure of the genome Hidden Markov model	GeneScan:Prediction of Complete Gene Structures in Human Genomic DNA (Server) TwinScan:Integrating Genomic Homology into Gene Structure Prediction (Server)
Wed 1/28	5. Gene recognition (2) Slides Annotated Slides	HMM algorithms: viterbi forward-backford algorithm		hw1 out
Mon 2/2	6. Prediction of miRNAs and their targets (by Bino John) Slides		RNAi explained Video on biogenesis and regulation of miRNA
Wed 2/4	7. HMM continue Slides 8. Intro to motif detection:	HMM algorithms: supervised training of HMM unsupervised training of HMM: Baum-Welsh algorithm Generalized HMM Higher-order HMM The Gene Scan program
Mon 2/9	8. Basic motif detection Slides	Position weight matrix and logo Motif search vs. de novo motif finding String-based methods Probablistic methods: MEME, an EM approach Sampling algorithms	MEME : Bailey and Elkan, Machine Learning 1995 Gibbs Motif Sampler : Lawrence et al, Science 1993
Wed 2/11	9. Advanced models for motif detection Slides Annotated Slides	Advanced probablistic methods (continue): motif prior Gibbs sampling Cis-regulation and structure of cis-regulatory modules HMM-based approach for CRM finding Basic (model-free) approaches for comparative genomic motif detection intro	CISTER : Frith et al, Bioinformatics 2001 Gibbs Sampling & Local Sequence Alignment : Liu et al, JASA 1995
Module 3: Genome Evolution and Comparative Genomics
Mon 2/16	10. Molecular evolution (by Bino John)	Nucleotide substitution models Selectional analysis (incl M-K test)	Chaps 8.2, 8.5 (Durbin et al)	hw1 due hw2 out
Wed 2/18	11. Sequence alignment (by Bino John) Slides	Pairwise alignment Multiple sequence alignment	Chap 2,4,6 (Durbin et al) Chap 7 (Waterman) BLAST paper : Altschul et al Blosum summary
Mon 2/23	12. Phylogenetic Inference I (by Bino John) Slides	Parsimony phylogeny methods Distance methods Least squares fitting of trees to matrices of distance Neighbor-joining and UPGMA methods	Chaps 2,3,11 Inferring Phylogenies, J. Felsenstein, Amazon Hillman
Wed 2/25	13. Phylogenetic Inference II (by Bino John) Slides	Likelihood on phylogenies. Algorithm for computing likelihoods on a tree Modelling variation of rates of evolution from site to site Hidden Markov Model for rates	Chaps 11, 16 Inferring Phylogenies, J. Felsenstein, Amazon Hillman	Project proposal due, hw2 due
Mon 3/2	14. Model based comparative genomic inference Slides	Evolutionary mixture models Phylogenetic HMMs Comparative genomic motif finding	EMNEM: Moses et al Phylo-HMM: Siepel et al
Module 4: Gene Expression and Functional Genomics
Wed 3/4	15. Microarray overview and Normalization Slides			hw3 out
Mon 3/9	Spring Break
Wed 3/11	Spring Break
Mon 3/16	16. Functional genomics: differential analysis Slides	Differentially expressed genes Univariate hypothesis testing Multiple hypothesis testing FWER and FDR Bonferroni, minP, step-down and step-up adjustments.
Wed 3/18	17. Classifying and clustering expression patterns (by Bino John) Slides			hw3 due
Mon 3/23	Midterm Exam			Solutions
Wed 3/25	18. Time series models for gene expression Slides
Module 5: Statistical Genetics and Genome-Phenome Associations
Mon 3/30	19. Genome variation and coalescent theory Slides		Introduction: Tavare, Neuhauser Mathematics of the n-coalescent: Kingman Coalescents under mutation, selection, recombination and migration: Hudson Ewens sampling formula: Tavare, Ewens Textbook: Chap 13, Deonier, Tavare, & Waterman
Wed 4/1	20. Meiosis and recombination Slides			Project midway report due
Mon 4/6	21. Haplotype Inference Slides		[url] Stephens, M., Smith, N., and Donnelly, P. (2001). A new statistical method for haplotype reconstruction from population data. American Journal of Human Genetics, 68, 978--989 [url] T. Niu, Z.S. Qin, X. Xu, and J. Liu (2002) Bayesian Haplotype Inference for Multiple Linked Single Nucleotide Polymorphisms. Am. J. Hum. Genet [url]Stephens, M., and Donnelly, P. (2003). A comparison of Bayesian methods for haplotype reconstruction from population genotype data. American Journal of Human Genetics, 73:1162-1169. [url] E.P. Xing, R. Sharan and M.I Jordan, Bayesian Haplotype Inference via the Dirichlet Process. Proceedings of the 21st International Conference on Machine Learning (ICML2004)	hw4 out
Wed 4/8	22. Pedigree and Linkage analysis Slides
Mon 4/13	23. QTL and eQTL Slides
Wed 4/15	24. Population stratification from genetic data Slides			Lecture given by Suyash Shringarpure
Module 6: Systems Biology
Mon 4/20	25. Biological networks Slides	Overview of global network characteristics General algorithms of network analysis		hw 4 due
Wed 4/22	26. Inferring gene regulatory networks Slides	Graphical models and Bayesian network approaches
Mon 4/27	27. Module networks Slides
Wed 4/29	28. Physical networks: Protein-Protein and Protein-DNA interaction Slides	Two-hybrid and CHIP-Seq experiments Algorithms for pattern analysis Network alignment and network evolution
Addnal topic	29. The era of integrative genomics: genome-phenome-transcriptome association
Fri May 1 3:30 pm	Poster Session			Venue: NSH 3002
Mon May 4	No class			Project report due