OVERVIEW OF COURSES

The GCB offers four courses designed to integrate genome sciences and computational biology training program for students of various background. The list below describes individual courses. A student pursuing advanced degree in computational biology with mostly computational background may follow the sequence 536 -> 531/535 -> 537. A student pursuing in advanced degree with mostly biology background may follow the sequence 531-> 536 -> 537. A student seeking a non-dissertation training in computational biology may follow the sequence 535->531 or 535->536.

531 Intro to Genomics
Goal:   Understand genomics (a genome-scale view of biology and modern high-throughput techniques to analyze genomes)
Pre-requisite: Modern biology,

Target audience 1^st year GCB students and those with similar background

Course Description:

This course serves as an introduction to the main laboratory and theoretical aspects of genomics and computational biology. The main topics discussed center around the analysis of sequences (annotation, alignment, homology, gene finding, variation between sequences, SNP's) and the functional analysis of genes (expression levels, proteomics, screens for mutants), together with a discussion of gene mapping, linkage disequilibrium and integrative genomics.

535: Intro to BioInformatics
Goal:   Understand and use bioinformatics methods and tools
Pre-requisite: Modern biology (no computer programming!)

Target Audience: Biology PhDs and BGS students

Course Description:

The course covers methods used in computational biology, including the statistical models and algorithms used and the biological problems which they address. Students will learn how tools such as BLAST work and will use them to address real problems. The course will focus on sequence analysis problems such as exon, motif and gene finding, on comparative methods and on analysis of gene expression data.

536: Fundamentals of Computational Biology
Goal:  Understand the computational principles of bioinformatics analysis
Pre-requisite: Basic knowledge of algorithms (no biology required)
Target Audience: CIS students and others with strong computational background.
Course Description:

An introductory computational biology course designed for computational scientists. The course will cover fundamentals of algorithms, statistics, and mathematics as applied to biological problems. In particular, emphasis will be given to biological problem modeling. Students will be expected to learn the basic algorithms underlying computational biology, basic mathematical/ statistical proofs and molecular biology. Topics to be covered are: genome annotation and string algorithms, pattern search and statistical learning, molecular evolution and phylogenetics, and small molecule folding.

537: Special Topics in Bioinformatics and Computational BIology
Goal: Be able to analyze primary literature
Pre-requisite:  531 and (535 or 536)  -- or equivalent knowledge
Target Audience- 2^nd year GCB students and PhD candidates in computational biology.
Course Description:

A discussion of research papers and topics.