Introduction to Statistical and Computational GenomicsGENOME 559
Department of Genome Sciences
University of Washington
Rudiments of statistical and computational genomics. Emphasis on basic probability and statistics, introduction to computer programming and relevant web databases. This course is intended to introduce students with non-computer science backgrounds to the major concepts of programming and statistics.
- William Stafford Noble
- Elhanan Borenstein
- Teaching assistant: Lindsay Pino
Please click on the links above for email addresses and office locations.
Meeting times and locations
- Classes: Monday and Wednesday, 10:30 - 11:50 am, Foege Building S110.
- Office hours: Friday, 11:30 am - 12:30 pm, Foege Building S110.
Substantial background in molecular and cellular biology, genetics, biochemistry, or related disciplines.
Readings for the Python parts of the class will be assigned from the free online textbook Think Python. A listing of additional reference material can be found here.
- The course involves hands-on programming during class time, so students should bring a laptop to class.
- All problem sets are due by the start of class on the date listed.
- Late homework will be accepted, but penalized. Specifically, each assignment is worth 100 points, from which 10 points will be deducted for each day (or fraction thereof) that you turn it in late. The maximum deduction for being late is 60 points (even if you are more than 6 days late).
- You are welcome to talk to classmates about principles for solving problems, but please do not solve specific problems together. In many ways, the problem solving is where you will learn the most for this class, especially the programming.
There will be an in-class final exam on the last day of class.
Grades will come 80% from problem sets and 20% from one final exam. There will be no mid-term exam.
- The course home pages can be found at http://noble.gs.washington.edu/~noble/genome559 and http://elbo.gs.washington.edu/courses/GS_559_18_wi.
- A Canvas page is at https://canvas.uw.edu/courses/1128791.
Date Lecture Topic Programming Topic Reading Homework
Wed Jan 3 Course overview. Intro to sequence comparison Introduction to Python Chapters 1-2 Mon Jan 8 Sequence comparison — dynamic programming Strings Chapter 8 Homework 1 Wed Jan 10 Sequence comparison — traceback and local alignment Numbers, lists and tuples Chapters 10, 12 Mon Jan 15 Martin Luther King Day Wed Jan 17 Significance of similarity scores File I/O Chapter 14 Homework 2 Mon Jan 22 Multiple testing correction Conditionals Chapter 5 Wed Jan 24 Motif search Loops (matrix.txt) Chapter 7 Homework 3 Mon Jan 29 Motif p-values While loops (small.fasta large.fasta) Wed Jan 31 Motif discovery Dictionaries (small-scores.txt large-scores.txt unique-scores.txt) Chapter 11 Homework 4 The remainder of the lecture schedule is here.