BMI 219 Protein Function Prediction from a Big Data Context Using Similarity Networks

Module: 2
Sponsoring Program: BBC
Administrator: Julia Molla/Rebecca Brown

Course Number: BMI 219
Course Name: Protein Function Prediction from a Big Data Context Using Similarity Networks
Units: 3
Grading Option: S/U
Course Director: Patsy Babbitt

Additional Course Director(s):
Room Number: BH 211/ Thur. CC 151
Campus: Mission Bay
Schedule: Tues 1-3pm, Thur 12-3
Maximum Class Size: 10

Course Description: Protein similarity networks are used to summarize structure-function relationships on a very large scale and at many levels of divergence across homologous proteins. Generated from comparison of sequence, structure, or other data, the similarity-based clusters that result can be mapped with many types of functional information to enable identification of functional trends from the context of these similarities. The course will introduce sequence similarity networks and their use for guiding experiments and prediction of functional properties of proteins of known and unknown function. The goal is to provide hands on training for exploration and interpretation of similarity networks. Bring your own biological questions for analysis or use our examples. Note: If your problem set is >10,000 sequences, please submit the set 2 weeks prior to the class so that the network can be generated before the course starts. The instructors can provide guidance regarding how to create the sequence sets you want to investigate.