Classes: W 10-12 in Bahen 3000
Instructor: Michael Brudno
Office: Pratt (PT) 286C & CCBR 604
Office Hours: By appointment
The development of HTS is forcing a reconsideration of the computational methods used for genome analysis, with the problems of read mapping and genome assembly becoming much more complex. Simultaneously, HTS is enabling the development of methods to address problems which were previously not addressed with genome sequencing, such as the prediction of structural or copy number polymorphisms. The HTS data has a very different error model, requiring modifications to classical algorithms, and the sheer size of the data requires the use of effective algorithms, appropriate hardware, and effective implementations. In this class we will explore the features of HTS data that make it different from classical sequencing data, and try to determine what are the possible methods to address some of these differences. Because of the novelty of the data and of the problems, the emphasis will be on discovering the right solutions, rather than just learning about them.
The prerequisite is an undergrad-level bioinformatics course, or permission of the instructor. The permission will be given if you have a basic knowledge of molecular biology (transcription, etc), a strong background in algorithms (at least CSC 373 level), and basic probability theory.
The basic requirements for the class will be a course project (60% of the grade), paper presentations and participation (20% of the grade) and written paper summaries (20% of the grade).
The whole point of the paper summaries is to make sure that you've read the papers before coming to class. However I will allow you to hand in no more than two summaries up to 2 days late (by Friday of the same week).