1. Statistical Analysis of Mouse Gut Microbiota Alex Tran Computer Systems Lab 2009-2010

2. Abstract The mouse gut microbiotal community is the population of bacteria, which inhabit the digestive track of a given population of mice. The ability to understand the microbiotal community has implications, which extend beyond mice and into factors of human obesity and the dietary needs of the human body. Computational genomics is an emerging field, which blends both biology and computer science, to analyze genes. This study seeks to utilize several methods emerging within computational genomics to analyze the gut microbiome of mice to observe the effects of varying diets on the gut microbiotal community, as well as create a universal and user friendly tool for researchers to utilize when studying any gut microbiome. The applications of this can extend past studying the gut microbiome, but can also be applied to any taxonomic group being counted for analysis, however several parts of the application are exclusively beneficial to the analysis of gut microbiota specifically those found in mice.

3. Introduction Genomics Gut Microbiota Mouse Digestive Tract An example of a Taxanomic tree. http://gchelpdesk.ualberta.ca/news/02jun05/images/circos- 001.png

4. Background OB/OB mice Human Microbiome Project

5. Washington University Gordon Lab Working with Gut Microbiota in Obese Mice Specifically observing Fecal matter Larger Sample Size Mice last longer Currently pay $1,000 dollars for each batch of tests Comes back in an unreadable format  Biologists can't program  All grad students are forced to learn Perl

6. Objectives User Friendliness Efficiency Universal Compatibility Hugenholtz RDP Ability to load any Taxanomic tree

7. Development Python Tkinter I/O intensive cPickle

8. 1 st 2 nd and 3 rd Quarter Progress 1 st Quarter Very Basic Gui shell in place Ability to read 2 taxonomic trees and strictly formatted files No exception catching ability No user selected taxonomies No clear data analysis possible 2 nd Quarter Driver and application style implementation Shell for multiple file loading and comparison User friendly error catch system Reads standard format files (with error detection) Any Taxonomic Tree 3 rd Quarter Shell for detailed analysis implemented Customized Tkinter File Browser added Archiving with recently used files and trees ready New combobox ability implemented Ability to have parallel programs communicate is ready for implementation

9. Final Results Ability to specify taxonomy used Basic statistical comparisons implemented Multiple file loading implemented Use in Gordon Lab Crossfile simultaneous OTU selection Initial loadscreen for tree selection and hub loading

10. Going Forward From the ground up approach More in depth statistical analysis Ability to view more than just a handful of files at a time More user friendly archiving