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
Office hours: Friday, 1:30-2:30 pm
Office: Foege S220B (call from the first floor phone to access the building -- there is a directory next to the phone)

Instructor: Mary Kuhner
Email: mkkuhner@gs.washington.edu
Office: Foege S420C (call from the first floor phone to access the building -- there is a directory next to the phone)

Instructor: Larry Ruzzo
Email: ruzzo@cs.washington.edu
Office Hours: Wednesday, 11:00-1:00
Office: CSE 554

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.

Web page

The course web page is located at http://noble.gs.washington.edu/~noble/genome559/.

Prerequisites

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

Course materials

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 19, 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
Tu Jan 6	Noble	Sequence comparison-intro and motivation	Substitution matrices, gap penalties	Introduction to python
Th Jan 8	Noble	Sequence comparison-dynamic programming	Dynamic programming, Needleman-Wunsch	Strings	Wikipedia: Needleman-Wunsch; (Eddy Nat Biotech 2004); Lutz: ch. 1-4, 7	Hw1 assigned
Tu Jan 13	Noble	Sequence comparison-more dynamic programming		Numbers lists and tuples	(Nicholas Biotechniques 2000); Lutz: ch. 5, 8
Th Jan 15	Noble	Sequence comparison-local alignment	Smith-Waterman	File I/O, if-then-else	Wikipedia: Smith-Waterman; Lutz: ch. 9-12	Hw1 due Hw2 assigned
Tu Jan 20	Noble	Sequence comparison-significance of similarity scores	distribution, p-value, extreme value distribution	for loops	Wikipedia: P-value; Altschul BLAST tutorial; Lutz: ch. 13
Th Jan 22	Kuhner	Phylogeny: Parsimony	heuristic search, "assumption-free" methods	while loops	Lutz 248-253 Small.txt Large.txt	Hw2 due
Tu Jan 27	Kuhner	Phylogeny: Models of the mutational process	least squares	Concepts in looping	Lutz 265-271	Hw3 assigned, dna.txt input file
Th Jan 29	Kuhner	Phylogeny: Likelihood	maximum likelihood	Dictionaries	Lutz 160-169
Tu Feb 3	Kuhner	Phylogeny: Bayesian methods and MCMC	Bayes' Theorem, Markov chains	Defining functions	Lutz 299-308	HW3 due, (solution) Hw4 assigned infile.txt input file
Th Feb 5	Kuhner	Phylogeny: Validating phylogenies	likelihood ratio test, bootstrap	Sorting
Tu Feb 10	Ruzzo	Motifs	Likelihood, LRT, MLE	Regular expressions Testre.txt	Ref 1 below; Lutz 75; Tutorial; Library reference	HW4 due (solution)
Th Feb 12	Ruzzo	Motifs	Entropy	Regular expressions
Tu Feb 17	Ruzzo	BLAST	Heuristics; BLAST strengths & weaknesses	Regular Expressions; "Comprehensions"	Ref 2 below. Lutz 78, 272-275	Hw5
Th Feb 19	Ruzzo	Multiple Alignment	Progressive Alignment	Objects, I	Wikipedia: Multiple sequence alignment. Lutz Ch 22-23
Tu Feb 24	Ruzzo	Gene Prediction	Markov models	Objects, II	Ref 3 below.	Hw5 due (solution)
Th Feb 26	Ruzzo	Gene Prediction	Data Integration	Objects, III Date.py		Hw6
Tu Mar  3	Ruzzo	Probabilites on Pedigrees	LOD Scores	Biopython, I	Wikipedia: Genetic linkage plus the section of Strachan & Read cited therein. Biopython, esp Tutorial&Cookbook
Th Mar  5	Ruzzo	Association	LD & Chi-square	Biopython, II	Wikipedia: Pearson's chi-square test. Mathworld: Bonferroni Correction	Hw6 due (solution) Hw7 assigned
Tu Mar 10	Ruzzo	Association	LD & Chi-square	Biopython, III
Th Mar 12	Ruzzo	RNA	Compensatory mutation; Mutual information	Exceptions Ex1.py Ex2.py	Either ref 4 or 5 below.	Hw7solutions

  Electronic access to journals is generally free from on-campus computers. For off-campus access, follow the "[offcampus]" links or look at the library "proxy server" instructions.  

References

1. GD Stormo, "DNA binding sites: representation and discovery." Bioinformatics, 16, #1 (2000) 16-23. PMID: 10812473 [offcampus]
2. A Pertsemlidis, JW Fondon, "Having a BLAST with bioinformatics (and avoiding BLASTphemy)." Genome Biol., 2, #10 (2001) REVIEWS2002. PMID: 11597340 [offcampus]
3. Harrow J, Nagy A, Reymond A, Alioto T, Patthy L, Antonarakis SE, Guigo R. "Identifying protein-coding genes in genomic sequences." Genome Biol. 2009 Jan 30;10(1):201. [Epub ahead of print] PMID: 19226436 [offcampus]
4. Amaral PP, Dinger ME, Mercer TR, Mattick JS, "The eukaryotic genome as an RNA machine." Science. 2008 Mar 28;319(5871):1787-9. PMID: 18369136 [offcampus]
5. Breaker RR, "Complex riboswitches." Science. 2008 Mar 28;319(5871):1795-7. PMID: 18369140 [offcampus]