CSE 891: Computational Science for Evolutionary Biologists

Instructors: C. Titus Brown, ctb@msu.edu; Art Covert, covertar@gmail.com.

Overview

CSE 891, fall 2012, section 002, 3 credits. Class time: 11am-12:30pm EST. MSU location: 1455A BPS (BEACON classroom).

U Texas contact: Art Covert, covertar@gmail.com

U Idaho contact: James Foster, foster@uidaho.edu


Course information:

Doing biology increasingly requires computational skills and quantitative reasoning abilities. This course will introduce students to computational thinking and practice through an intensive scripting and programming regimen, built around a series of models and data sets from evolutionary and molecular biology. During this course we will introduce the Python programming language, scripting and pipelining, simulations, and data analysis.

This course is intended for graduate students with little or no previous programming experience. There are no prerequisites other than a strong background in at least one of evolution, ecology, genetics, or molecular biology. Enrollment by permission of instructor only.


Objectives: At the end of the course, you will have gained basic programming skills in Python, learned basic sequence assembly approaches, and worked with the Avida modeling system.

Course structure: Class will consist of in-class lectures and discussion based around screencasts and homework.

Homework and grading: Individual homeworks will be graded on a P/F basis. Homeworks will contribute evenly to the final grade, i.e. ~15 homeworks each worth 6 2/3%. Group or collaborative work is allowed and encouraged on homework, although students must each hand in homework and are individually responsible for their solutions.

Materials: Most of the course will use materials developed by Drs. Brown and Covert, which will be placed on the Web. All such course materials will be made available under a Creative Commons license (CC-BY-SA 2.0), use/reuse OK but attribution required. There is no required textbook, but students may find "Practical Computing for Biologists" (Haddock/Dunn) useful reference material.

Attendance: Attendance is generally required unless absences are arranged in advance.

Course schedule: Class will be divided into three main modules: introductory Python programming; sequence analysis and assembly; and evolutionary modeling with Avida. Each module will be approximately 5 weeks. A running theme will be using scripting and data analysis techniques in Python to explore data sets.

The current plan is: Programming: (weeks 1-5)

  1. Introducing Python, ipython notebook, and Amazon.
  2. More Python.
  3. Even more Python.
  4. Data analysis and plotting in Python.
  5. Running models in Python.
Sequence assembly: (weeks 6-10)
  1. Sequencing and de novo assembly -- the basics
  2. Running assembly programs
  3. Evaluating assemblies
  4. Digging deeper into assemblies
  5. Digging deeper into assemblies, round 2
Avida: (weeks 11-15)
  1. Introducing Avida
  2. Configuring Avida and analyzing Avida runs
  3. Experimental design with Avida
  4. Reading Avidians

Lab of Genomics, Evolution and Development / ctb@msu.edu