Course Syllabus

The syllabus for the course is spelled out in detail on the course home page, with a week by week breakdown. Briefly, we will cover the following:

Sources of Bioinformatics Data (Week 1)
Next gen sequencing, and database filtering for big data searches (Week 2,3)
- Pigeonhole principle for database filtering
- Bloom filters
- Sketching
Genome segmentation using HMMs (Week 4)
Data preprocessing, feature extraction, and abstraction for proteomics (Week 5)
- MS2 based peptide identification
- Peptide quantification
A gentle introduction to linear algebra (Week 5)
Unsupervised clustering approaches (Week 5,6)
Supervised classification methods OR dimensionality reduction and visualization (Week 6)
Applications: (Week 7,8,9) (Dr. Zhong)
- Extracellular RNA
- Longitudinal data: linear models, mixture models, classification
- Single cell sequencing and time-course clustering

Grading policy:

30% assignments (3 assignments)

30% midterm
40% final project.

Late submission policy: 5% penalty for each day after the deadline for a maximum of 4 days. Regrade requests must be turned in within 7 days of receiving the grade.