CSE 5860: Computational Problems in
Evolutionary Genomics - Spring 2014
235 ITEB, firstname.lastname@example.org
Office hours: ITEB 235, Wednesday 9-12 pm or by appointment.
This course covers selected
Computational and Mathematical topics in evolutionary genomics.
I will mainly focus on problems arising in population genomics:
concepts of population genetics,
coalescent theory and population genetics, and complex
evolutionary processes in population
genetics. This course will focus on the computational aspects of
population genomics. The
goal of the course is to introduce students the current status of
the computational population
genomics and inspire students to pursue research in this
This course is lecture-based. Students are required to read and
present a research paper
(or papers). Each student should also perform some empirical study
some computational approaches for population genomics problems
some real population genomics data (e.g. the 1000 Genomes Project
In particular, the planned subjects are:
1) Introduction to Population Genetics. This includes basic
concepts of population
genetics (mutation, genetic drift, gene flow and selection).
2) Coalescent theory. Basic models in coalescent theory. Diffusion
Probabilistic computation on coalescent models. Applications of
3) Recombination. Recombination models. Lower bounds. Ancestral
and related algorithms. Other recombination models.
4) Complex evolutionary models. These may include incomplete
lineage sorting and
5) Other topics may include Monte Carlo methods, pedigree analysis
and application of
population genetics in forensics.
Prerequisites. As for
background, essentially no biology is assumed. The most relevant
background is upper division or graduate courses on algorithms
and probability and statistics.
Or a smart, mathematically mature student who has had neither,
might also be able to
follow the course.
Textbook: No textbook required. I will post
links to papers and other documents.
The following books are useful to this course.
An Introduction, by John Wakeley, 2008. This is a good
to coalescent theory. I have requested reservation of this book in
2. Ancestral Inference in Population Genetics, by Simon Tavare.
This document used to
be available online but seems no longer so. If I find it, I will
post a link.
I will occasionally assign written homework.
Presentation. Each student needs to select a
particular subject in population genomics
to present to the class. The student should contact the instructor
about the subject first.
I prefer the presentation to provide some general background and
also cover some interesting
technical aspects. The student presentation will occur during the
later part of the course.
student should do some empirical study. Often this means a student
implement some computational approaches for a selected population
and then tests its performance. Or a student can choose to analyze
real population genetics
data for some population genetics inference. I prefer that each
student works on his/her
own project but exception can be made. Alternatively, a student
can also choose to conduct
some more theoretical investigation (e.g. designing a faster
algorithm for some meaningful problem
in population genomics). Note
that each project needs to be first approved by the instructor.
Exams. I do not plan to give sit-down exams in
this course, although this may change according
to the course progress. The current plan is that I will give a
take-home final exam.
Also at the end of the class I plan to meet each student in class
to discuss what
they learned from the class.
Grading. The grade will be
assigned by: homework (15%), project (30%), paper presentation
final exam (25%) and discussion (15%).