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
- Brian Beliveau
- Teaching assistant: Anna Minkina, firstname.lastname@example.org
Please click on the links above for email addresses and office locations.
Meeting times and locations
- Classes: Tuesday and Thursday, 10:30 - 11:50 am, Foege Building, room S040.
- Office hours: Thursdays, 4:30 - 5:30 pm, Foege Building S040
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.
- The course involves hands-on programming during class time, so students should bring a laptop to class.
- 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 share code or solve specific problems together. 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 final course grade is computed using the procedure described here.
- The course home page can be found at http://noble.gs.washington.edu/~noble/genome559.
- A Canvas page is at https://canvas.uw.edu/courses/1355296.
Date Lecture Topic Programming Topic Reading Homework
Tue Jan 8 Course overview. Intro to sequence comparison Introduction to Python (Unix reference) Chapters 1-2 Thu Jan 10 Sequence comparison — dynamic programming Strings Chapter 8 Homework 1 Tue Jan 15 Sequence comparison — traceback Numbers, lists and tuples Chapters 10, 12 Thu Jan 17 Sequence comparison — local alignment File I/O Chapter 14 Homework 2 Tue Jan 22 Significance of similarity scores Conditionals Chapter 5 Thu Jan 24 Multiple testing correction For loops (matrix.txt) Chapter 7 Homework 3 (red green yellow blue) Tue Jan 29 Motif search More for loops (sample.fa) Thu Jan 31 Motif p-values compute-motif-pvalue.py int-motif.txt my-pssm.txt   Homework 4 kmer_input_seq.txt CRISPR_seq.fastq Tue Feb 4 FDR control pvalues.txt While loops (Noble 2009) Thu Feb 6 Motif discovery Dictionaries small-scores.txt large-scores.txt unique-scores.txt seq-names.txt (D'haeseleer 2006) Homework 5 decode_seq_signal.py example_chromosome21.txt for_loop_to_while_loop.py seq_errors.fastq The remainder of the lecture schedule will be posted soon. Mon Mar 16 Final exam, 10:30 am - 12:20 pm, Foege S040