PhD Prelim Study Guide for Bioinformatics

The prelim questions will be given based on the materials covered by the following four courses:

CS5660 Bioinformatics Tools and Techniques

CS5670 Computer Science Applications in Bioinformatics II

CS6670 Computer Science Applications in Bioinformatics with a Project

CS7960 Topics in Bioinformatics

Study Guide for CS5660: Bioinformatics Tools and Techniques

Textbook: An Introduction to Bioinformatics Algorithms, by Neil C. Jones and Pavel A. Pevzner, MIT Press, ISBN-10: 0262101068, ISBN-13: 978-0262101066.

Chapters 1-7 (inclusive). Students must understand all material presented and be able to answer correctly all questions at the end each chapter.

Study Guide for CS5670: Computer Science Applications in Bioinformatics II

Textbook: An Introduction to Bioinformatics Algorithms, by Neil C. Jones and Pavel A. Pevzner, MIT Press, ISBN-10: 0262101068, ISBN-13: 978-0262101066.

Chapters 8-12 (inclusive). Students must understand all material presented and be able to answer correctly all questions at the end each chapter.

Study Guide for CS6670: Computer Science Applications in Bioinformatics with a Project

Textbook: Algorithms on strings, trees, and sequences By Dan Gusfiled, Cambridge University Press, ISBN 0-521-58519-8

Chapters:

1-3 Exact Matching

5-7, 9 Suffix Trees and Their Uses

11-12,14 Inexact Matching

15 Sequence Databases

17 Evolutionary Trees

Study Guide for CS7960: Topics in Bioinformatics

Option 1 (instructor: Dr. Changhui Yan)

Textbook: Biological sequence analysis By R. Durbin, S. Eddy, A. Krogh and G. Mitchison, Cambridge University Press, ISBN 0-521-62971-3

Chapters:

2. Pairwise alignment

3. Markov chains and hidden Markov models

4. Pairwise alignment using HMMs

5. Profile HMMs for sequence families

6. Multiple sequence alignment

Topic 1: Applications of Markov chains in protein function and structure classification

Topic 2: Applications of HMMs in transmembrane protein topology prediction

Option 2 (instructor: Dr. Minghui Jiang)

Textbook: none (we will read research papers directly).
1. Multiple sequence alignment
1a. Approximation algorithms (Gusfield, 1993).
1b. Graph theoretical techniques: directed acyclic graphs (Lee, Grasso, and Sharlow, 2002; Sze, Lu, and Yang, 2005).
1c. Progressive algorithms: Clustal-W and T-Coffee.
2. Combinatorial pattern matching and motif finding
2a. Approximate string matching.
2b. Gibbs sampling (Lawrence et al., 1993).
2c. Random projection (Buhler and Tompa, 2002).
3. Structural bioinformatics
3a. RNA secondary structure prediction.
3b. Protein folding.
3c. Structural alignment.