Examines current topics and issues associated with computing and software systems. Offered: AWSpS.
Students will learn how the algorithms have been developed to solve a variety of bioinformatics problems: Dynamic programming for DNA/protein sequence alignment; Graph algorithms for delineating dynamics of biological processes, Pattern matching technique to search databases; Combinatorial algorithms for DNA sequence processing; Hidden Markov models for sequence annotation; Statistics for haplotype frequency inference; Clustering and Trees for gene expression analysis, etc. This course also uses Python programming language to implement these algorithms for given problems. Students will conduct group projects which develop bioinformatics tools/platforms with any programming language including Python.
Student learning goals
Students will learn the basic data structure of biology data, include DNA/RNA protein sequences.
Students will learn how to convert biology data into numerical data structure.
Students will develop computational models/tools with fundamental and advanced algorithms that are applicable in bioinformatics.
General method of instruction
This class is lecture-based, however, a final project is required at the end.
Students are not required to have biology background. However, CSS 343 is a prerequisite as students should be able to build a computational model using the algorithms.
Class assignments and grading
There will be up to 5 homework assignments, 2 exams and a final group project.