I529:  Bioinformatics in Molecular Biology and Genetics: Practical Applications (3CR)

Spring Semester 2008
Lecture: MW 4-4:50pm
Lab: F 4-5pm
Office Hour: TBA
Instructor: Haixu Tang (INFO 225)
AI: Arunima Ram (EIG 1009)

 

Description: We aim to introduce a broad range of, from fundamantal and advanced, applications of bioinformatics methods and tools to solving problems in genomics and molecular biology. Prior to this class, the students should have learned basic methods and theories in bioinformatics, e.g. by taking I519. In this class, we will focus on how to apply them to solving biological problems in real life. Some advanced computational techniques that are widely applied in bioinformatics, e.g. Hidden Markov model (HMM), Bayesian Network (BN), will be discussed in details in the class. The important themes that will be covered by this course include

- Sequence modeling and classification
- Genome annotation
- Motif finding
- Genome comparison
- Protein families
- Non-coding RNAs
- MicroRNAs and their targets
- Functional prediction
- Phylogenetics
- Mass spectrometry and proteomics

This class will have a separate lab section, in which the students will be taught in how to solve biological problems in a step-by-step fashion. The programs that will be covered in the lab of this class include

- Sequence modeling using Markov chains: seq++;
- Pair HMM: SLAM, TwinScan, QRNA;
- HMM: Genscan;
- Profile HMM: Hmmer, Pfam;
- Stochastic Context Free Grammer (SCFG): COVE;
- Non-coding RNA search: Rsearch;
- Phylogenetics: PHYLIP, PAML;

Students will be instructed to write scripts (Perl and PHP preferrable) and/or programs that make use of the current implementation of sophisticated algorithms, such as HMM, BN, SVM, etc., to solve biological problems.

This course is designed for the advanced level bioinformatics graduate students after they take I519. Graduate students with either biology or phisical/computer science backgrounds who is interested in bioinformatics applications in molecular biology are also welcome to take this course.

 

Textbook: : Richard Durbin, Sean R. Eddy, Anders Krogh, and Graeme Mitchison, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , Cambridge University Press, 1999, (BSA)
Some of the topics from the course can not be found in this book. We will distribute complementary lecture notes and reading materials along the course for these topics. We also recommend the students to read the book, Nello Cristianini and Matthew W. Hahn, Introduction to Computational Genomics: A Case Studies Approach ,Cambridge University Press, 2006
Assignments: We will have 5 take-home assignments and 1 class project.

Grading: Combined assignments (30%), One mid-term exam (20%), Final exam (25%), Class Project (20%), Attendence (5%).

Prerequisites:  I519 or equivalent knowledge in bioinformatics required.

Group Assignment: The class will be divided into several small groups for mini projects.
The group assignment is going to be determined in the first class.

Final projects (please email me if you have any questions regarding these projects.)



Tentative syllabus [This is subject to minor changes!]:

 

Week
 Date
 Contents
 Lecture notes
1
 1/7 Mon.
 Introduction to the class

BSA 1.1 - 1.2
The primer of Perl
Hypertext Preprocessor PHP
-- we will use it for the web site design in this class.

1/9 Wed.
 Probabilistic modeling
BSA 1.4, Chapter 11
Notes

1/11 Fri.
 Lab1: Web site design using PHP and mySQL
(Homework 1)
2
 1/14 Mon.
 Probabilistic sequence modeling I Note

 1/16 Wed.
 Probabilistic sequence modeling I: frequency and profiles


1/18 Fri.
 Lab2: Alignment algorithms: Smith-Waterman, FASTA and Blast
3
 1/21/ Mon.
 No class (Martin Luther King Jr. Day)


1/22 Wed.
 Probabilistic sequencing modeling II: Markov chain BSA Chapter 4
Notes

1/26 Fri.

Lab3: Modeling biological sequences using seq++ ; blocks and related tools; Sequence weblogo
4
 1/28 Mon.
 Probabilistic sequencing modeling II: Markov chain
(Homework 2)

1/30 Wed.
 Hidden Markov Model I: Model structure
BSA Chapter 3
Notes

2/1 Fri.

Group Discussion (HW1)

5
 2/4 Mon.
 Hidden Markov Model I: Model structure


2/6 Wed.
 Hidden Markov Model II: GHMM


2/8 Fri.

Lab4: GeneMark.HMM & Genscan
6
 2/11 Mon.

HMM III: Parameter estimation
(Homework 3)

 BSA Chapter 3
Notes

2/13 Wed.
 HMM III: parameter estimation



2/15 Fri.

Group discussion (HW2)

7
 2/18 Mon.
 EM algorithm I
Notes

2/20 Wed.
 EM algorithm II

2/22 Fri.

Lab5: MEME
8

2/25 Mon.

Pair HMM I

2/27 Wed.

Pair HMM II

2/29 Fri.

SLAM,TwinScan,QRNA
9
 3/3 Mon.
 Q & A
(Homework 4)		 BSA Chapter 4
Notes

3/5 Wed.
 Midterm

10
 Spring recess
11 3/17 Mon.
 Profile HMM I
 BSA Chapter 5
Notes

3/19 Wed.
 Profile HMM II

3/21 Fri.

Lab7: Pfam & Hmmer
12
 3/24 Mon.
 Profile HMM III

3/26 Wed.
 Advance probabilistic graphic models I
 Notes

3/28 Fri.

 Group Discussion (HW 3)
13
 3/31 Mon.
 Gibbs sampling I
Homework 5 		Notes

4/2 Wed.
 Gibbs sampling II


4/4 Fri.

 Lab8: ClustalW, Treeview/ATV
14
 4/7 Mon.
 Phylogenetics: Distance-based methods I
 BSA Chapter 7
Notes

 4/9 Wed.
 Phylogenetics: Distance-based methods II
 BSA Chapter 7

 4/11 Fri.
 Lab9: Phylip
(Group discussion HW4)
15
 4/14 Mon.
 Phylogenetics: character-based methods I BSA 7
 Notes

4/16 Wed.
 Phylogenetics: character-based method II
 BSA 9

4/18 Fri.

 Lab10: PAML

16
 4/21 Mon.
 Phylogenetics: Maximal likelihood (ML) method
 BSA 9
Notes

4/23 Wed.
 Project presentation (continue on 4/25, Friday)
17 4/28 Mon Final Exam

5/2 Fri. Final project report due

Last updated : 1/7/2008