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

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

Instructor: Bruce Weir
Email: bsweir@u.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 8 Noble Sequence comparison: Introduction and motivation Substitution matrices, gap penalties Introduction to Python

Thu Jan 10 Noble Sequence comparison: Dynamic programming Dynamic programming, Needleman-Wunsch Strings Mount: ch. 3; Lutz: ch. 1-4, 7 HW1 assigned

Tue Jan 15 Noble Sequence comparison: More dynamic programming Numbers, lists and tuples Mount: ch. 6; Lutz: ch. 5, 8

Thu Jan 17 Noble Sequence comparison: Local alignment Smith-Waterman File I/O, if-then-else Lutz: ch. 9-12 HW1 due
HW2 assigned

Tue Jan 22 Noble Sequence comparison: Significance of similarity scores distribution, p-value, extreme value distribution for loops Mount: ch. 4; Lutz: ch. 13

Thu Jan 24 Kuhner Phylogeny: Parsimony heuristic search, "assumption-free" methods while loops, modules (large.txt, small.txt, averagefasta.py, countfasta.py, fasta2matrix.py, fastaids.py) Mount: 292-294 HW2 due

Tue Jan 29 Kuhner Phylogeny: Distance methods least squares More on loops Mount: 301-317 HW3 assigned,(dna.txt)

Thu Jan 31 Kuhner Phylogeny: Likelihood methods maximum likelihood Dictionaries Lutz: 103-107, Mount: 317-320

Tue Feb 5 Kuhner Phylogeny: Bayesian methods and MCMC Bayes' Theorem, Markov chains Defining functions Lutz: ch12 HW3 due
HW4 assigned, (infile.txt)

Thu Feb 7 Kuhner Phylogeny: Validating phylogenies, (bootstrapdata.txt likelihood ratio test, bootstrap Sorting Mount 321-322

Tue Feb 12 Kuhner Pedigree analysis: Probabilities of genes on pedigrees LOD score Regular expressions Lutz: 447-452 HW4 due
HW5 assigned

Thu Feb 14 Kuhner Pedigree analysis: Additional methods ascertainment bias Objects and classes (part 1) Lutz: ch. 19-20

Tue Feb 19 Kuhner QTLs linkage disequilibrium Objects and classes (part 2) Lutz: ch. 21 HW5 due
HW6 assigned,(sibs.txt)

Thu Feb 21 Kuhner Association studies: Detecting association between a trait and a gene chi-square, Bonferroni correction, relative risk Review: code.py, codon_rna.txt, codon_dna.txt, dna.txt

Tue Feb 26 Weir Population genetics: Categorical data analysis chi-square and multinomial distributions, testing in population genetics HW7 assigned

Thu Feb 26 Kuhner Association studies: Avoiding pitfalls in association studies bias, correlation vs. causation Biopython and exceptions HW6 due

Tue Mar 4 Weir Population genetics: Genetic Diversity Statistical approaches for quantifying DNA sequence variation; Expectation of a random variable

Thu Mar 6 Weir Whole genome association Linear regression HW7 due
HW8 assigned

Tue Mar 11 Weir Whole genome association t-test, p-value

Thu Mar 13 Weir Whole genome association family-wise error rate, false discovery rate HW8 due

Lecture	Instructor	Lecture topic	Concepts	Programming topic	Reading	Homework
Tue Jan 8	Noble	Sequence comparison: Introduction and motivation	Substitution matrices, gap penalties	Introduction to Python
Thu Jan 10	Noble	Sequence comparison: Dynamic programming	Dynamic programming, Needleman-Wunsch	Strings	Mount: ch. 3; Lutz: ch. 1-4, 7	HW1 assigned
Tue Jan 15	Noble	Sequence comparison: More dynamic programming		Numbers, lists and tuples	Mount: ch. 6; Lutz: ch. 5, 8
Thu Jan 17	Noble	Sequence comparison: Local alignment	Smith-Waterman	File I/O, `if-then-else`	Lutz: ch. 9-12	HW1 due HW2 assigned
Tue Jan 22	Noble	Sequence comparison: Significance of similarity scores	distribution, p-value, extreme value distribution	`for` loops	Mount: ch. 4; Lutz: ch. 13
Thu Jan 24	Kuhner	Phylogeny: Parsimony	heuristic search, "assumption-free" methods	`while` loops, modules (large.txt, small.txt, averagefasta.py, countfasta.py, fasta2matrix.py, fastaids.py)	Mount: 292-294	HW2 due
Tue Jan 29	Kuhner	Phylogeny: Distance methods	least squares	More on loops	Mount: 301-317	HW3 assigned,(dna.txt)
Thu Jan 31	Kuhner	Phylogeny: Likelihood methods	maximum likelihood	Dictionaries	Lutz: 103-107, Mount: 317-320
Tue Feb 5	Kuhner	Phylogeny: Bayesian methods and MCMC	Bayes' Theorem, Markov chains	Defining functions	Lutz: ch12	HW3 due HW4 assigned, (infile.txt)
Thu Feb 7	Kuhner	Phylogeny: Validating phylogenies, (bootstrapdata.txt	likelihood ratio test, bootstrap	Sorting	Mount 321-322
Tue Feb 12	Kuhner	Pedigree analysis: Probabilities of genes on pedigrees	LOD score	Regular expressions	Lutz: 447-452	HW4 due HW5 assigned
Thu Feb 14	Kuhner	Pedigree analysis: Additional methods	ascertainment bias	Objects and classes (part 1)	Lutz: ch. 19-20
Tue Feb 19	Kuhner	QTLs	linkage disequilibrium	Objects and classes (part 2)	Lutz: ch. 21	HW5 due HW6 assigned,(sibs.txt)
Thu Feb 21	Kuhner	Association studies: Detecting association between a trait and a gene	chi-square, Bonferroni correction, relative risk	Review: code.py, codon_rna.txt, codon_dna.txt, dna.txt
Tue Feb 26	Weir	Population genetics: Categorical data analysis	chi-square and multinomial distributions, testing in population genetics			HW7 assigned
Thu Feb 26	Kuhner	Association studies: Avoiding pitfalls in association studies	bias, correlation vs. causation	Biopython and exceptions		HW6 due
Tue Mar 4	Weir	Population genetics: Genetic Diversity	Statistical approaches for quantifying DNA sequence variation; Expectation of a random variable
Thu Mar 6	Weir	Whole genome association	Linear regression			HW7 due HW8 assigned
Tue Mar 11	Weir	Whole genome association	t-test, p-value
Thu Mar 13	Weir	Whole genome association	family-wise error rate, false discovery rate			HW8 due