Course Syllabus

Course Description: The new biotechnologies developed in the past decade are transforming molecular biology research into a quantitative science based on informatics. New platforms are now capable of collecting large amount of DNA/RNA sequences and measuring their activities in cells  in single-cell precision and spatial resolution at a population scale. Without doubt, the contents of the collected data offer unprecedented opportunities for a deep understanding of the molecular mechanisms in cell and cell organizations in tissues, which improve mapping of the linkage between individual’s phenotypes and genetic/genomic patterns for disease treatment. Facing the challenges of making sense out of the sheer volume of data, computer algorithms and data analysis models are playing the central role. This course covers an introduction to various types of functional genomic data available and current computational/statistical methods used for analyzing the data to answer questions in functional genomics, systems biology and their applications in disease research. We will cover the analysis of gene expression data, proteomic data and protein-protein interaction data, with a special focus on how they can be used to understand and infer networks. In particular, the course will also cover the recent advances in cancer genomics by applications of the sequencing, array-based and proteomic technologies. The topics are organized as: 1) Introduction to Genomics and Genomic Technologies, 2) Statistical Analysis of Genomic Data, 3) Data Mining and Machine Learning Methods for Genomics, 4) Cancer Genomics and 5) Biological Network Analysis.

Goals: There will be 4 homework assignments, each requiring the implementation of a computational method and its application to a real functional genomic dataset. Each student/group will also present a research paper on cancer genomics in the class. Students will also choose a topic for the final project, and present the results. All the homework assignments are required to be programmed in matlab/python/R and submitted through canvas.

Grading: Homework Assignments: 60%, Course Project: 30%, and Participation/Presentation: 10%.

Text Books:

Prerequisites: Some programming skills are required for this course.  Biology or other non-CS students are required to take Csci 3003 or an equivalent programming course as a prerequisite or get instructor approval.  Prior knowledge of basic molecular biology is highly recommended.

Intended Audience: This course is primarily for graduate and senior undergraduate students in computer science, math, statistics, biological sciences and biomedical sciences with interest in computational biology.

Course Summary:

Date Details Due