I529/B659:  Bioinformatics in Molecular Biology and Genetics: Practical Applications (3CR)

Spring Semester 2016
Lecture: MW 2:30-3:20pm(I2 122)
Lab: F 2:30-3:20pm(I109)
Office Hour: Thr 2-3:30pm (LH 301D) Haixu Tang
Fri 10-11am (LH 406) Chao Tao
Instructor: Haixu Tang
AI: Chao Tao

Description: We aim to introduce a broad range of, from fundamantal and advanced, applications of bioinformatics methods and tools to solve 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;
- Non-coding RNA search: Rsearch;

Students will be instructed to write scripts (Python and PHP preferrable) and/or programs that make use of the current implementation of sophisticated algorithms, such as HMM, BN, DBN, 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 are 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)

Optional textbook: : Kevin Murphy, Machine learning: a probabillistic perspective , MIT Press, 2012, (MLP)
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.


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

 

Week
 Date
 Contents
 Lecture notes
1
 1/11 Mon.
 Introduction to the class

BSA 1.1 - 1.2

1/13 Wed.
 Probabilistic modeling
BSA 1.4, Chapter 11
Notes

1/15 Fri.
 Lab 1: Unix servers; web site; group assignments.

2
 1/18 Mon.
 No class (Martin Luther King Jr. Day)

1/20 Wed.
 Probabilistic sequence modeling I: frequency and profiles
 Notes

 1/22 Fri.
 Lab 2: GeneMark, Glimmer
 Slides
3
 1/25 Mon.
 Probabilistic sequence modeling II: Markov Chain
BSA Chapter 3
Notes

 1/27 Wed.
 Hidden Markov Model I: Model structure BSA Chapter 3
Notes

 1/29 Fri.

Lab 3: presentation of mini-project 1
4
 2/1 Mon.
 Hidden Markov Model II: Generalized HMM
(Homework 2)

2/3 Wed.
 Hidden Markov Model III: parameter estimation
BSA Chapter 3
Notes

2/5 Fri.

Lab 4: Gene Ontology and function prediction
Slides
5
 2/8 Mon.
 HMM for multiple sequences I
Notes

2/10 Wed.
 HMM for multiple sequences II

2/12 Fri.

Lab 5: HMM for protein sequence analysis
MarCoil: prediction coiled-coil
TMHMM: prediction of transmembrane helices, 		slides
6
 2/15 Mon.

HMM for multiple sequences III
 (Homework 3)

2/17 Wed.
 Coalescent HMM
Notes
PSMC paper
MSMC paper

2/19 Fri.

Lab 6: Presentation of mini-project 2
7
 2/22 Mon.
 Pair HMM I
Notes

2/24 Wed.
 Pair HMM II

2/26 Fri.

Lab 7: Genscan and Twinscan
 slides
8

2/29 Mon.

EM algorithm I
 BSA Chapter 4
A short tutorial
Notes

3/2 Wed.

EM algorithm II

3/4 Fri.

Lab 8: weblogo, MEME, Gibbs Motif Sampler
Slides
9
 3/7 Mon.
 Q & A

3/9 Wed.
 Midterm

10
 Spring recess
11 3/21 Mon.
 Profile HMM I
 BSA Chapter 5
A paper by S. Eddy
Notes

3/23 Wed.
 Profile HMM II

3/25 Fri.

 Lab 9: Presentation of mini-project 3

12
 3/28 Mon.
 Profile HMM III

3/30 Wed.
 Gibbs Sampling
 Notes

4/1 Fri.

 Lab 10: Hmmer and Pfam
 slides
13
 4/4 Mon.
 Bayesian Network I
 Short Tutorial
Notes

4/6 Wed.
 Bayesian network II


4/8 Fri.

 Discussion about final project topics
14
 4/11 Mon.
 Junction tree algorithm
 A working example
Notes

 4/13 Wed.
 Module network
 Notes

 4/15 Fri.
 Lab 11: TBD
15
 4/18 Mon.
 Dynamic Bayesian network I
 Book Chapter by Kevin Murphy on DBN
Notes

4/20 Wed.
 Dynamic Bayesian network II

4/22 Fri.

 Project Presentation
16
 4/25 Mon.
 Project presentation

4/27 Wed.
 Project presentation
17 5/4 Wed (Note: time changed!) Final Exam: 5-6:30 pm I2 122


5/6 Fri. Final project report due

Last updated: 1/12/2016