GS559: Introduction to Statistical and Computational Genomics (Winter 2014)

   Jim Thomas,
   Elhanan Borenstein,

Schedule: Tues. Thurs, 3:30-4:50 (first class Jan. 7, last class Mar. 13), Hitchcock 220.



» The final exam will take place in class, Thursday, March 13 (last class of the quarter). It will have two parts: The first will focus on the bioinformatics topics covered in class and the second on programming. You are allowed to use any static resource (i.e., books, notes).


You are welcome to talk to classmates about principles for solving problems, but 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.

All problem sets are due by the start of class on the date listed. Grades will come 80% from problem sets and 20% from one final exam. There will be no mid-term exams.

Test/Demo Files

The following files are used in some of the in-class exercises and demos.


Lectures and Reading:

Lecture #Lecture TopicProgramming TopicReading
1 Overview of course. Introduction to sequence comparison. BLAST, alignment scoringPDF Introduction to Python. Interpreter, objects, types, variables, command linePDF [1, 2]
2 Sequence alignment - dynamic programmingPDF StringsPDF
3 Sequence alignment - local alignmentPDF Numbers, lists, tuplesPDF
4 Sequence alignment - protein score matricesPDF File input-ouput, if-then-elsePDF
5 Sequence alignment - signficance of similarity scoresPDF For loopsPDF
6 Signficance of similarity scores continuedPDF While loopsPDF
7 Whole genome alignmentsPDF Loops and efficient codePDF
8 Sequence trees - distance treesPDF Dictionaries (hash maps)PDF [3]
9 ParsimonyPDF FunctionsPDF
10 Small parsimonyPDF More on functions, modulesPDF
11 Gene ontology and functional enrichmentPDF Functions as arguments, sortingPDF
12 Gene set enrichment analysisPDF Classes and objectsPDF
13 Ab initio gene predictionPDF Exception HandlingPDF
14 Gene expression: Clustring PDF More on classes and objectsPDF
15 Gene expression: K-mean clustring PDF Regular expressionsPDF
16 Biological networks; Dijkstra algorithmPDF More regular expressionsPDF
17 Degree distribution and network motifsPDF More on classes, BiopythonPDF
18 RecursionPDF
19 In-class projectPDF
20 Final Exam


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.

  1. Noble, WS, "A quick guide to organizing computational biology projects." PLoS Comput. Biol. 5 (2009) e1000424. Pmid: 19649301 [Offcampus]
  2. Dudley, JT and Butte, AJ, "A quick guide for developing effective bioinformatics programming skills." PLoS Comput. Biol. 5 (2009) e1000589. Pmid: 20041221 [Offcampus]
  3. How dictionaries work (aka hash tables or hash maps)
  4. Subramanian et al., "Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles"PNAS102(43) (2005)

Python Resources:

Regular Expressions
"RegExPal" (For Javascript rather than Python, but similar and quite handy. Try it!)
Python Books
Python for Software Design: How to Think Like a Computer Scientist by Allen B. Downey. (Includes early drafts of our text book; cheaper than the published version, but less polished...)
Learning Python by Mark Lutz. O'Reilly (Very comprehensive. Much is accessible to beginners.)
Dive Into Python 3 by Mark Pilgrim. (Another online book. Based on Python 3, so some differences, and more advanced, but also free.)

Bioinformatics Books

» Biological sequence analysis: probabilistic models of proteins and nucleic acids, R. Durbin, S. Eddy, A. Krogh, and G. Mitchison, Cambridge. (Excellent reference, classics)
» Inferring Phylogenies, Joseph Felsenstein, Sinauer, 2004. (Excellent reference on this topic.)
» Introduction to Computational Genomics: A Case Studies Approach, Cristianini, Nello & Hahn, Matthew, Cambridge, 2007.
» An Introduction to Bioinformatics Algorithms, Neil C. Jones & Pavel A. Pevzner, 2004.
» Bioinformatics: Sequence and Genome Analysis, David W. Mount, Cold Spring Harbor Laboratory Press.
» Python for Bioinformatics, Sebastian Bassi, CRC Press, 2010. (A little too advanced as a progamming book for beginners, but fine now that you're experienced.)
» Python for Bioinformatics, Jason Kinser, Jones and Bartlett, 2009. (Ditto.)

James H. Thomas
Department of Genome Sciences
University of Washington
Elhanan Borenstein
Departments of Genome Sciences
University of Washington