Lecture:	Tuesday/Thursday 1:15-2:30pm, Science Center 183
Lab A:	Monday 1:15 - 2:45pm, Science Center 240
Lab B:	Monday 3:00 - 4:30pm, Science Center 240

Instructor:	Ameet Soni
Email:
Office:	Science Center 253
Office hours:	10am to noon, Friday or by appointment
Prep Time (limited availability):	Monday 10am to noon, Tuesday/Thursday 11:00 to 1:00pm

Course Discussion:

Piazza (mandatory enrollment through Moodle)

Welcome to CS68. This course is an introduction to the fields of bioinformatics and computational biology, with a central focus on algorithms and their application to a diverse set of computational problems in molecular biology. While the course will view algorithms applied through the lens of biology, the themes of the course drive at the heart of the larger "Big Data" phenomena. Computational themes will include dynamic programming, greedy algorithms, supervised learning and classification, data clustering, tree inference, graphical models, data management, and structured data representation. Applications will include genetic sequence analysis, pairwise-sequence alignment, phylogenetic trees, motif finding, gene-expression analysis, and protein-structure prediction.

While significant time will be spent exploring the biological significance of problems, the central focus in this course will be on understanding how to develop algorithms for complex problems. In particular, the general question we will answer is "How does one reason about large amounts of complex data?" That is, how do we uncover underlying phenomena and draw conclusions in the face of large data sets with noisy, intricate relationships? While this question is presented in context of problems in molecular biology, it applies to open problems across all of the sciences. We will see that many of the algorithms we cover have applications and foundations in far-reaching domains including natural language, social network analysis, security, and search.

To enroll in this course you must have completed CPSC 35. There is no requirement for prior biology experience. The course will also cover a good deal of probability theory, but much of this can be picked up with provided reading. This course is designated as a natural sciences and engineering practicum (NSEP) and qualifies as a Group 3: Applications course for the CS major/minor requirements. It also cross-listed as BIOL 68 and counts as a non-lab course towards a biology major.

Required Course Textbook

Biological Sequence Analysis by Durbin, Eddy, Krogh, and Mitchison.

Additional references

TBA