BME 4800 and CSE 3800/5800:
Please read carefully,
although I will not cover much of these in
This course covers important techniques in bioinformatics.
The goal is to present an overall picture of bioinformatics and
inspire students to pursue research in this fast-developing field.
Outline. This course is
lecture-based. Homeworks will be assigned on major
subjects covered in the class. There
will be a (possibly take-home) final exam.
In addition, each student will work on
In particular, the planned subjects are:
1) Exact string matching. New developments in Suffix Trees and Arrays.
We will review the basics of suffix trees and arrays, and
then look at some recent work that uses string matching in
2) Sequence analysis. This includes space-efficient pairwise alignment
multiple sequence alignment.
3) Hidden Markov models (HMM). Concepts, algorithms and applications of
HMM in bioinformatics.
4) Phylongey. Various classical phylogenetic methods. We will also
the currently widely used phylogenetic methods, including parsimony,
Neighbor Joining Algorithm and maximum
likelihood. We will also cover perfect phylogeny problem.
5) Other topics, which may include genome rearrangement, biological
networks and related
algorithmic problems, gene regulation, and structural bioinformatics.
Prerequisites. As for background,
essentially no biology is assumed.
The most relevant background is some knowledge of probability and know
how to write programs.
Also, an undergraduate course on algorithms will help, but not
The required textbook is:
Biological Sequence Analysis: Probabilistic Models of
Proteins and Nucleic Acids
by Richard Durbin, Sean R. Eddy,
Anders Krogh, and Graeme Mitchison, 1999.
This book is widely used as the textbook for
teaching statistical aspects of bioinformatics.
We will use it from time to time. I also recommend the following book:
Algorithms on Strings, Trees and Sequences: Computer
Science and Computational Biology
by Dan Gusfield, 1997. This
excellent introduction for people from computer science background.
We will cover topics related to both books.
Homeworks. I will appreciate if you
can typeset your homeworks. Late homeworks will not
Each student will work on several programming projects individually,
be accepted. Please acknowledge the source of any ideas. You may share
ideas with someone else as long as you acknowledge them.
If you work with one or more person on a writeup then
you should turn in a single writeup together.
Yes, there will be no partner in your project. The projects will be
related to what
is presented in class and helps you to get a feeling about what is like
a bioinformatics software tool. There
is no restriction on the programming language to use.
But keep in mind that the performance of the programs matters.
Students. Graduate students are required to do extra
work for this course. Each graduate student will need to read a
research paper in bioinformatics,
understand it and then write a
(perhaps 2-4 pages) document on it.
Remember that your goal is not to repeat what the author(s) said.
I would like to see some interesting or semi-interesting ideas
etc.). If you prefer, you can also do a small research project on your
own about anything
you think interesting in bioinformatics. In either case, you
have the freedom
to choose subject, but make sure
to email me to get permission on
There is no midterm exams for this course. There will be a final
exam, which may be a take-home exam. For
will be a 25 minutes discussion with you in my office. The subject is
likely to be
the project you did.
Do not worry, this is not an exam, just a chance for me to see what
in the course and what interests students have.
Grading. This is a non-required
course. I expect you register it because
you are interested in it and want to learn something about
I am required to assign a grade. The grade will be assigned
homework, project report and discussion (for graduate student), and