Genome 373: Genomic Informatics

    Jay Shendure,
   Elhanan Borenstein,

Teaching Assistant:
    Jacob Kitzman,

Schedule: MWF, 10:30-11:20, Hitchcock 220.


This course is intended to introduce students to the breadth of problems and methods in computational analysis of genomes, arguably the single most important new area in biological research. The specific subjects will include large-scale comparative genome structure, sequence alignment and search methods, gene prediction, evolutionary relationships among genes, and next-generation sequencing. The course will include one mid-term exam and a final exam. Other graded assignments will be problem sets, due most weeks. Some problems will include computer programming and most will involve extensive use of web resources for data set mining.


» The class has filled up and we have a wait-list. If you are on the wait-list, we suggest that you come to the first day of class. Although we are not planning on expanding the class past the current count, based on our past experience there are often several individuals who drop, such that we may be able to accomodate a subset (or, ideally, all) of the wait list.


Problem sets are posted online each Wednesday and are due the following Wednesday by 5PM. Homework is a mix of written problems and programming.
Grades: 50% home assignments, 20% midterm, 30% final exam.

Test/Demo Files

The following files may be used in some of the in-class exercises and demos or in the home assignments.

Lectures and Resources:
(Note: Links to resources will become live as the course progresses)

1Mar. 26, 28, 30 Intro to Phyton; Python 1 (script, print,variables); Intro to bioinformatics; Sequence alignment; (Borenstein) Lecture 1A, Lecture 1B, Lecture 2A, Lecture 2B, Lecture 3A, Lecture 3B, Quiz section
2Apr. 2, 4, 6 Python 2 (if, loops, functions); Dynamic programming algorithm; Global alignment; Score matrices; Local alignment; (Borenstein) Lecture 1A, Lecture 1B, Lecture 2A, Lecture 3B, Quiz section
3Apr. 9, 11, 13 Genome sequencing technologies; (Shendure) Lecture 1, Lecture 2, Lecture 3, Quiz section
4Apr. 16, 18, 20 Next-generation sequencing; (Shendure) Lecture 1, Lecture 2, Lecture 3, Quiz section
5Apr. 23, 25, 27 Gene prediction, experimental and ab initio; Midterm! (Shendure) Lecture 1, Lecture 2, Lecture 3, Quiz section
6Apr. 30, May 2, 4 Gene prediction continued; Molecular evolutionary analysis; (Shendure) Lecture 1, Lecture 2, Lecture 3, Quiz section
7May 7, 9, 11 Intro to microarrays; Microarray technologies; (Shendure) Lecture 1, Lecture 2, Lecture 3, Quiz section
8May 14, 16, 18 Microarray clustering algorithms; Hierarchical clustering; K-mean clustering; GO annotation; Enrichment analysis (Borenstein) Lecture 1, Lecture 2, Lecture 3 Quiz section
9May 21, 23, 25 GSEA; Intro to phylogenetic trees; UPGMA; NJ; Parsimony (small and large); (Borenstein) Lecture 1, Lecture 2, Lecture 3, Quiz section
10May 30, Jun. 1 Parsimony cont' (search heuristics); Biological networks; Review; (Borenstein) Lecture 1, Lecture 2, Quiz section


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"PNAS 102(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.)

Jay Shendure
Department of Genome Sciences
University of Washington
Elhanan Borenstein
Departments of Genome Sciences
University of Washington