4/11: Final homework
4/1: Midterm Project
1/30: HW1 and datafiles
1/13: Welcome! Please take some time to complete a survey form and email to me. Thank you!
This course provides a review of both old and new challenging problems in the area of bioinformatics and computational biology. The course does not assume prior background in biology. However you do need to have a strong background in algorithms and probability/statistics (see prerequisite).
We will first cover some basics in biology, and then spend about 1/3 of the semester reviewing some traditional topics in bioinformatics (which overlaps with many topics covered in CS5263 and CS6293 in previous semesters). For the remaining semester we will be mainly focusing on Translational Bioinformatics. A majority of the materials will be coming from PLoS Computational Biology: Translational Bioinformatics Collection. Additional topics may be covered depending on interest level.
The official prerequisite for the course is CS5263. If you do not meet the prerequisite but are interested in participating in this course, you are expected to have taken the graduate algorithms course and recevied a grade of B or better. You are also expected to have a solid knowledge of probability and statistics, and a strong desire to learn by yourself. If you do not meet the above mentioned criteria, please talk to me at the beginning of this course.
We meet in room MB 1.1.03 . Lecturers are Tuesday and Thursday, 4:00-5:15 PM.
Dr. Jianhua Ruan
Office location: FLN 4.01.48
Office hours: Wednesday 2:00-3:00pm, or by appointment
Phone: (210) 458-6819
There is no textbook required for this course. The instructor will provide the needed materials including chapters from textbooks, journal papers, and review articles in class or on course homepage.
Online Reading Materials and Resources
|2/25 T||Chapter 2: Data-Driven View of Disease Biology||Jianhua Ruan||Slides||Additional reading: Statistics and Probability Primer|
|2/27 R||Chapter 4: Protein Interactions and Disease||Md. Jamiul Jahid||Slides|
|3/4 T||Chapter 5: Network Biology Approach to Complex Diseases
|3/6 R||Chapter 3: Small Molecules and Disease
|3/18 T||Chapter 7: Pharmacogenomics
|3/20 R||Chapter 8: Biological Knowledge Assembly and Interpretation
|3/25 T||Chapter 6: Structural Variation and Medical Genomics
|3/27 R||Chapter 14: Cancer Genome Analysis||Hung-I Chen||Slides|
|4/1 T||Chapter 15: Disease Gene Prioritization
|4/3 R||Chapter 9: Analyses Using Disease Ontologies
|4/8 T||Chapter 12: Human Microbiome Analysis
|4/10 R||David, GSEA, and EnrichNet||Jianhua Ruan &
|4/15 T||Chapter 17: Bioimage Informatics for Systems Pharmacology
|4/17 R||Chapter 13: Mining Electronic Health Records in the Genomics Era
|4/22 T||Chapter 16: Text Mining for Translational Bioinformatics
|4/24 R||Chapter 11: Genome-Wide Association Studies
|5/5 M||Final Project Report Due|
|Topics||Number of weeks|
|Introduction to molecular biology and basic sequence analysis algorithms||2|
|NGS data processing||1.5|
|Gene expression, gene ontology and gene set enrichment analysis||1.5|