Module: 1
Sponsoring Program: BP219
Administrator: Rebecca Brown

STUDY LIST INFORMATION
Course Number: BP219
Course Name: Big Data Analysis of the Microbiome
Units: 3
Grading Option: S/U
Course Director: Joseph DeRisi (for studylist only

MORE COURSE INFORMATION
Additional Course Director(s): Zachary Apte (Course Instructor)
Room Number: Teaching Lab, GH 227
Campus: Mission Bay
Schedule: Mon, Thurs, Fri, 12:00-4:00 PM
Prerequisites: No prior programming experience is required. Experience with Python and the Linux command line (bash) is strongly recommended. (Note: Big Data Analysis of the Microbiome is a fast paced course. Students without prior experience should expect to spend a significant amount of time on the exercises outside of class.)
Maximum Class Size: 10

Course Description:This mini-course focuses on the use of Python programming tools to analyze and interpret large-scale data sets obtained from next-generation sequencing (NGS) of the microbiome. Through a series of lectures and exercises, students will learn core skills for analyzing the human microbiome, documenting their analysis, and converting analysis protocols into reusable computer programs. Students will use the example of their own microbiome and integrate publically available microbiome datasets into their analysis.  Hands on exercises will involve annotation of raw microbiome data with open-source tools, highperformance computing in the cloud, and the use of publically available annotation tools.  Exercises will focus on using the Python programming language, with emphasis on concepts applicable to many programming languages and disparate areas of research.  Class time will consist of core lectures, small group project sessions, simple quizzes, and relevant research papers to be read outside of class. A Windows or Mac OS laptop will be required.  Necessary software and network access will be provided on the first day of class.  No prior programming experience is required. Experience with Python and the Linux command line (bash) is strongly recommended. (Note: Big Data Analysis of the Microbiome is a fast paced course. Students without prior experience should expect to spend a significant amount of time on the exercises outside of class.)  By the end of the course, students will have familiarity with the core concepts related to computational analysis of the human microbiome and be comfortable using the infrastructure and tools required for large-scale scientific computation.