GENOME 559: Introduction to Statistical and Computational Genomics

Introduction to Statistical and Computational Genomics
GENOME 559
Department of Genome Sciences
University of Washington School of Medicine

Course description
Rudiments of statistical and computational genomics. Emphasis on basic probability and statistics, and an introduction to computer programming. This course is intended to introduce students with non-computer science backgrounds to the major concepts of programming and statistics.
Learning objectives
After taking this course, students will be able to describe and perform basic analysis tasks relating to biological sequence analysis, phylogenetics, pedigree analysis, genetic association studies, population genetics and microarray analysis. Students will be able to demonstrate an understanding of fundamental statistical concepts, such as p-values, t-tests, chi-squared tests and multiple testing correction. Finally, students will be able to write computer programs to perform statistical and bioinformatics analyses.
Instructional staff

Instructor: William Stafford Noble
Email: noble@gs.washington.edu

Instructor: Mary Kuhner
Email: mkkuhner@gs.washington.edu

Instructor: Larry Ruzzo
Email: ruzzo@cs.washington.edu

Meeting times and locations

Tue/Thu 3:30-4:50 pm in Hitchcock 220

The class meets in a computer lab and will involve writing computer programs during class time.

Prerequisites
Substantial background in molecular and cellular biology, genetics, biochemistry or related disciplines.
Course materials

Bioinformatics: Sequence and Genome Analysis by Mount. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 2004. Second edition.

Learning Python by Lutz. O'Reilly, 2007. Third edition.

Course requirements

Students will complete eight homework assignments during the course. Assignments will typically involve some written questions and some programming problems.

Examinations

The final exam will be open book, and will cover the entire quarter. The final exam is scheduled for Thursday, March 20, 4:30-6:20 pm in Hitchcock 220.

Course grade
10% for each homework assignment, and 20% for the final exam.
Class schedule

Lecture Instructor Lecture topic Concepts Programming topic Reading Homework

Tue Jan 6 Noble Sequence comparison: Introduction and motivation Substitution matrices, gap penalties Introduction to Python

Thu Jan 8 Noble Sequence comparison: Dynamic programming Dynamic programming, Needleman-Wunsch Strings Mount: ch. 3; Lutz: ch. 1-4, 7 HW1 assigned

Tue Jan 13 Noble Sequence comparison: More dynamic programming Numbers, lists and tuples Mount: ch. 6; Lutz: ch. 5, 8

Thu Jan 15 Noble Sequence comparison: Local alignment Smith-Waterman File I/O, if-then-else Lutz: ch. 9-12 HW1 due
HW2 assigned

Tue Jan 20 Noble Sequence comparison: Significance of similarity scores distribution, p-value, extreme value distribution for loops Mount: ch. 4; Lutz: ch. 13

Thu Jan 22 Kuhner Phylogeny: Parsimony heuristic search, "assumption-free" methods while loops, modules HW2 due

Tue Jan 27 Kuhner Phylogeny: Models of the mutational process least squares Dictionaries Lutz: pp. 103-107 HW3 assigned

Thu Jan 29 Kuhner Parsimony: Distance and likelihood maximum likelihood Defining functions Lutz: ch. 11

Tue Feb 3 Kuhner Phylogeny: Bayesian methods and MCMC Bayes' Theorem, Markov chains Printing and sorting Lutz: pp. 87-90, 455-456 HW3 due
HW4 assigned

Thu Feb 5 Kuhner Phylogeny: Validating phylogenies likelihood ratio test, bootstrap Regular expressions Lutz: pp. 447-450

Tue Feb 10 Ruzzo Pedigree analysis: Probabilities of genes on pedigrees LOD score Objects and classes (part 1) Lutz: ch. 19-20 HW4 due
HW5 assigned

Thu Feb 12 Ruzzo Pedigree analysis: Modes of inheritance ascertainment bias Objects and classes (part 2) Lutz: ch. 21

Tue Feb 17 Ruzzo Pedigree analysis: Fitting models to pedigrees nuisance parameters Biopython HW5 due
HW6 assigned

Thu Feb 19 Ruzzo Association studies: Detecting association between a trait and a gene chi-square, Bonferroni correction, relative risk

Tue Feb 24 Ruzzo Association studies: Avoiding pitfalls in association studies bias, correlation vs. causation

Thu Feb 26 Ruzzo Population genetics: Categorical data analysis chi-square and multinomial distributions, testing in population genetics HW6 due
HW7 assigned

Tue Mar 2 Ruzzo Population genetics: Genetic Diversity Statistical approaches for quantifying DNA sequence variation; Expectation of a random variable

Tue Mar 4 Ruzzo Whole genome association Linear regression HW7 due
HW8 assigned

Tue Mar 9 Ruzzo Whole genome association t-test, p-value

Thu Mar 11 Ruzzo Whole genome association family-wise error rate, false discovery rate HW8 due

Lecture	Instructor	Lecture topic	Concepts	Programming topic	Reading	Homework
Tue Jan 6	Noble	Sequence comparison: Introduction and motivation	Substitution matrices, gap penalties	Introduction to Python
Thu Jan 8	Noble	Sequence comparison: Dynamic programming	Dynamic programming, Needleman-Wunsch	Strings	Mount: ch. 3; Lutz: ch. 1-4, 7	HW1 assigned
Tue Jan 13	Noble	Sequence comparison: More dynamic programming		Numbers, lists and tuples	Mount: ch. 6; Lutz: ch. 5, 8
Thu Jan 15	Noble	Sequence comparison: Local alignment	Smith-Waterman	File I/O, `if-then-else`	Lutz: ch. 9-12	HW1 due HW2 assigned
Tue Jan 20	Noble	Sequence comparison: Significance of similarity scores	distribution, p-value, extreme value distribution	`for` loops	Mount: ch. 4; Lutz: ch. 13
Thu Jan 22	Kuhner	Phylogeny: Parsimony	heuristic search, "assumption-free" methods	`while` loops, modules		HW2 due
Tue Jan 27	Kuhner	Phylogeny: Models of the mutational process	least squares	Dictionaries	Lutz: pp. 103-107	HW3 assigned
Thu Jan 29	Kuhner	Parsimony: Distance and likelihood	maximum likelihood	Defining functions	Lutz: ch. 11
Tue Feb 3	Kuhner	Phylogeny: Bayesian methods and MCMC	Bayes' Theorem, Markov chains	Printing and sorting	Lutz: pp. 87-90, 455-456	HW3 due HW4 assigned
Thu Feb 5	Kuhner	Phylogeny: Validating phylogenies	likelihood ratio test, bootstrap	Regular expressions	Lutz: pp. 447-450
Tue Feb 10	Ruzzo	Pedigree analysis: Probabilities of genes on pedigrees	LOD score	Objects and classes (part 1)	Lutz: ch. 19-20	HW4 due HW5 assigned
Thu Feb 12	Ruzzo	Pedigree analysis: Modes of inheritance	ascertainment bias	Objects and classes (part 2)	Lutz: ch. 21
Tue Feb 17	Ruzzo	Pedigree analysis: Fitting models to pedigrees	nuisance parameters	Biopython		HW5 due HW6 assigned
Thu Feb 19	Ruzzo	Association studies: Detecting association between a trait and a gene	chi-square, Bonferroni correction, relative risk
Tue Feb 24	Ruzzo	Association studies: Avoiding pitfalls in association studies	bias, correlation vs. causation
Thu Feb 26	Ruzzo	Population genetics: Categorical data analysis	chi-square and multinomial distributions, testing in population genetics			HW6 due HW7 assigned
Tue Mar 2	Ruzzo	Population genetics: Genetic Diversity	Statistical approaches for quantifying DNA sequence variation; Expectation of a random variable
Tue Mar 4	Ruzzo	Whole genome association	Linear regression			HW7 due HW8 assigned
Tue Mar 9	Ruzzo	Whole genome association	t-test, p-value
Thu Mar 11	Ruzzo	Whole genome association	family-wise error rate, false discovery rate			HW8 due