563_763Syllabus_v2

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Advanced Bioinformatics Computing

Course Syllabus

4002 5
63
/763

Spring Quarter, 200
43


Instructors
:


Anne R. Haake, Ph.D.

Office:

70
-
2325

Office Hours:
M/W 10
-
11:30

E
-
mail:
arh@it.rit.edu

Phone: 475
-
5365


Rhys Pric
e Jones,

Ph.D.

Office
: 70
-
3559

Office Hours:

T/R 1
-
1:50

E
-
mail:
rpjavp@rit.edu

Phone: 475
-
5866



Class Meeting Times

Lecture/ Lab: Tuesday & Thursday 8:30
-
10:50 a. m. 13
-
1370


Course Objectives:
This course will p
rovide an in
-
depth exposure to advanced
techniques in computational genomics. Topics

may include
:
Gene Finding, Genetic
algorithms, Hidden Markov models, Neural networks, Gene Expression An
alysis,
Clustering algorithms,
Simple and Complex Diseases: Gene
Mapping, SNP analysis,
Machine learning, Molecular Network Analysis, Probabilistic

framework for modeling
and inference, Systems Biology


Prerequisites

Introduction to Bioinformatics Computing


Textbooks:


An

Introduction to Bioinformatics Algorithms
by N
eil Jones and Pavel Pevzner


Introduction to Bioinformatics

by Arthur M. Lesk (available online through Books 24 X 7
at Wally.rit.edu)


Also, readings of “classic” and current papers from the scientific literature.


Class Activities and Grading Criteria

Cl
ass

time will be used for



lectures



laboratory exercises similar to last quarter’s



project development



student presentations and discussion


All graduate students will be responsible for
one 30 minute prese
ntation on a topic to be
assigned
.
For each pres
entation, all students

will prepare a written
summary and
critique of the paper’s content and the presentation. These are
to
be prepared prior to
the presentation,
consulte
d during discussion
and also

handed in for grading purposes
.
The deliverables are:

a ½ page summary of the paper to be handed in before the
presentation; a 1 page summary of the presentation and critique; and a revised
summary of the paper in light of the discussion.
Active and interested participation will
contribute to the grade.


All

students
are responsible for a programming project
.

This will be a substantial
scientific experiment and will require careful and well
-
documented program code as well
as high
-
quality write
-
up and presentation.


There will be one exam

(TBA)
. Quizzes wi
ll be given on Tuesdays.



Detailed
Grading Criteria:
Grades will be based on the following components:



Exam 25
%

(Undergrads) 20% (Grads)



Quizzes 20
%



Present
ation of a topic to the class 10
%

(Grad students only)



Critique
s of papers/ presentations 15
%
-
Und
ergrads 10
% Grads



Other class participation 5
%



Lab exercises 15%



Programming project 20
%


Letter grades will be awarded based on percentage of accumulated points out of a
maximum of 100 points.

A= 90
-
100%

B=80
-
89%

C=70
-
79%

Lectures and Laboratory Exercis
es available at:

http://www.cs.rit.edu/~rpj/courses/bic/Syllabus.html

Outline of Weekly Topics: (subject to change as the quarter progresses)


Week of:

Topics


Readings & Special Activ
ities



3/7

Introduction to the Course

Wrap
-
up of Fragment Assembly


Lab 1
-
Fragment Assembly



3/14

Gene Expression Analysis I

Microarray Technologies

Experimental Design Issues

Data Standardization Issues

Approaches for Data Analysis

Supervised and Unsu
pervised Learning

Assigned Papers:

Duggan et al., 1999;Quackenbush, 2001;
Golub et al., 1999;
Brazma et al., 2001





3/21

Gene Expression Analysis II

Introduction to Project Dataset



Re
search Lecture by Dr. Robert Fri
sina

Data Pre
-
processing
:
James Tho
mpson

Lab

Data Analysis using public tools

Chapter 10: Jones & Pevzner


3/28

Gene Finding I

Gene Features: Prokaryotic

Genetic Algorithms

Gene Features: Eukaryotic



Chapter 2 (77
-
97): Lesk

Chapter 11: Jones & Pevzner

Gene Finding at

http://www.math.tau.ac.il/~rshamir/algmb/01/
scribe07/lec07.pdf

L
ab
: prokaryotic gene finding



4/4

Gene Finding II

HMM models

Gene
-
finding programs


HMM lecture:

http
://
www
.
math
.
tau
.
ac
.
il
/~
rshamir
/
algmb
/
01
/
scribe05
/
lec05
.
pdf

Lecture notes, online resources

Lab
: HMM
& gene finding

Grad Student Presentations

(Gene
Expression Analysis)


4/11

Gene Expression Analysis
III

SAGE & MPSS

More Machine Learning Approaches


SAGE: Velculescu et al., 1995;

MPSS: Brenner et al., 2000.

Lab
-
Grad Student Presentations
(Gene
Expression Analysis)



4/18

Probabilistic Framework for Modeling &
Inference

Networks


Lab
-
Grad Student Pre
sentations

(Gene
Expression Analysis)

a
nd

Student Project

Work



4/25

Simple & Complex Disease Genetics

Gene Mapping

Single Nucleotide Polymorphisms

Haplotype Mapping

Animal Models

Chapter 2

Lesk

Glazier et al., 2002

Stumpf, 2002

Lab
-
Student Project

Work


5/2

Systems Biology

Csete & Doyle, 2002

Kitano, 2002

Lab
-
Student Project

Work

5/9

EXAM

Student Project Presentations


5/16

Final exam period for wrap
-
up
discussions, project polishing, etc.

Final exam period for wrap
-
up
discussions, project polish
ing, etc.