Evolutionary Bioinformatics

BIOS 333


BIOS 333, a new course to be offered at Rice University for the first time Fall 2009:

MWF at 10 AM, BL 123

— Bioscience Group B —

Course Description

Large digital data sets are making it essential in many areas of science for practitioners to be able to create computational analysis tools to answer their own research questions.  This course is designed to introduce natural and social science students to programming, and issues in evolutionary genomics to students with a computational background.  Students will learn conceptual tools and practical skills for creating computer programs to test scientific hypotheses by assembling, manipulating and analyzing diverse digital data sets.  Students will understand and implement some of the most commonly-used algorithmic strategies in evolutionary genomics, including dynamic programming, hidden Markov models and graphical algorithms, with selected examples from other domains.

Recommended Prerequisites: Math 101/102. 


Lectures and problem sets will train students in the practical skills for writing simple computer programs useful for analyzing biological data, and teach them the mathematical and algorithmic underpinning of some of the most commonly used methods in the field of bioinformatics.  Through examples examined in class, and through a final project, students will strengthen their skills for formulating hypotheses, and learn to design a computational strategy to test them with available data.

Recommended Prerequisites

Math 101/102, or permission of the instructor.  No programming experience is expected or required. 


Grades will be based on class participation (10%), regular homework (50%), and a final project (40%).  Programming exercises and examples will primarily use Ruby and/or Perl and/or Python; homework assignments and final project can use any language/platform, with approval of instructor.


Python Programming:  An Introduction to Computer Science, by John Zelle

Computational Genome Analysis, An Introduction, by Richard C. Deonier, Simon Tavaré and Michael S. Waterman

Reasons of the day to take bios333:

You use internet resources like Pubmed, Facebook, Twitter, or Wikipedia, but you couldn’t write a simple 5-line program to answer questions like these:

  1. “Which 10 human genes have the most papers in Pubmed about them?”

  2. “How many unique second-degree followers of your twitter feed are there?”

  3. “What US city is the birth place of the most 1980s horror movies stars?”

You want to understand the ideas behind the most commonly used analysis software in biological research.

You want to understand the possibilities and challenges of new technologies that generate large volumes of biological data. 

Questions?  Contact Dr. Putnam at

nputnam at rice dot edu.