Syllabus

You can download the syllabus here. The syllabus includes information on grading, course goals, and policies I will use in teaching this course.

Text

We will be using An Introduction to Bioinformatics Algorithms, by Jones and Pevzner.

FAQ

This is a FAQ I wrote about this class, and the required background to take it.

Class Schedule

This is a tentative schedule for the class. I may change it depending on how the class progresses.

Week	Dates	Subject	Notes
1	Jan. 18 - 22	Introduction
2	Jan. 25 - 29	Sequence Alignment
3	Feb. 1 - 5	Sequence Alignment
4	Feb. 8 - 12	Sequence Alignment
5	Feb. 15 - 19	Phylogeny
6	Feb. 22 - 26	Phylogeny	Midterm 1
7	Mar. 1 - 5	Phylogeny
8	Mar. 8 - 12	Gene Expression
9	Mar. 15 - 19	Gene Expression
Spring Break!
10	Mar. 29 - Apr. 2	Gene Expression	Midterm 2
11	Apr. 5 - 9	Markov Models
12	Apr. 12 - 16	Markov Models
13	Apr. 19 - 23	Markov Models
14	Apr. 26 - 30	Further Topics & Wrap-Up

Units

These are the topics we will cover in class.

Introduction

Text Reading:	Ch. 1-3
Topics covered:	Introduction to molecular biology Introduction to computer science

Sequence Alignment

Paper:	Salyers et al. (2004)
Text Reading:	6.1 - 6.10
Assignment:	Align sequence from several human gut bacteria, looking for antibiotic-resistance genes. (data)
Topics Covered:	Dynamic programming methods for global & local alignments Pairwise & multiple sequence alignment Gaps, insertions/deletions Linear & affine gap penalty functions BLAST algorithm Heuristics for multiple sequence alignment Alignment statistics & substitution matrices (BLOSUM)

Phylogeny

Paper:	Lanciotti et al. (1999)
Text Reading:	10.5 - 10.11
Assignment:	Place several related West Nile Virus sequences in a phylogenetic tree. (data) (WN genomes)
Topics covered:	Rooted vs. unrooted trees Distance vs. parsimony vs. maximum likelihood UPGMA Neighbor Joining Fitch's Algorithm Searching for improved trees

Gene Expression

Papers:	Schena et al. (1995), Golub et al. (1999)
Text Reading:	10.1 - 10.4
Assignment:	Separate leukemia patients into subgroups, using gene expression data. (training data) (test data)
Topics covered:	Microarrays Distance metrics for GED Hierarchical clustering k-means Expectation maximization

Markov Models

Find the CpG islands within a stretch of DNA.

Paper:	Bird (1987)
Text Reading:	Chapter 11
Assignment:	Find the CpG islands within a stretch of DNA. (HMM data) (sequence data)
Topics covered:	Markov assumption Different orders of models Basic Markov chains Estimating model parameters Hidden Markov Models Forward, Viterbi, and Forward-Backward (Baum-Welch) Algorithms Profile HMMs

Further Topics & Wrap-Up

Assignment:	Independent project, if possible using the student's own data.
Topics covered (if time):	Fragment assembly Protein structure Time series analysis

Sample Code

These files illustrate some basic principles in Java.

• hello.java: A basic “hello world” program.
• basics.java: Lots of basic functionality examples in one program.
• functions.java: Illustrates functions and return values.
• Point.java: A basic Java object.
• pointex.java: An example using Point.java.

These files put everything together, to illustrate the Central Dogma of Molecular Biology.

• DNASequence.java: An object representing a sequence of DNA.
• RNASequence.java: Same, but for RNA.
• AASequence.java: Same, but for a polypeptide (a string of amino acids).
• dogma.java: The program that uses them all.
• seq.dat: A data file of DNA sequences, for use with dogma.java.

Lectures

Past lecture notes and recordings are here. (Password required.)

Class-Only Information

Click here if you're in the class, for more useful information.

Other Resources

Any other links that might be useful to the class will be here.