Stat 337

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College of Arts and Sciences


Chairperson's Application for Approval of a New Course


DATE:

15

Jan
uary 2004

TO:


CAS Academic Dean


FROM:

Dr. Joseph Mayne

DEPARTMENT:

Mathematics & Statist
ics

1.
PROPOSED NEW COURSE:




Course Number
:

Stat 337



Credit Hours:
Four

(
4
)



Course Title:
Quantitative Methods in Bioinformatics



Title Abbreviation:
Quant Bioinformatics

(Titles longer than 25 character positions must be abbreviated to not more than 25 c
haracter positions, exclusive
of cross listing notations, for computer printouts. Count spaces and punctuation marks into total. Please limit
punctuation to colons, ampersands (&), and dashes, if possible.)

2.

CROSS
-
LISTINGS:


(NOTE
:

All cross
-
listings must b
e approved by the chairperson(s) of the cross
-
listed department(s).)





Course Number:
Biol 337



Credit Hours:
Four

(
4
)



Course Title:
Quantitative Methods in Bioinformatics



Title Abbreviation:
Quant Bioinformatics



Signature(s) of Concurring Chair
person (
on original forms)
:

Date


3.


PLEASE ANSWER THE FOLLOWING REGARDING THIS PROPOSED NEW COURSE:




What, if any, will be the prerequisites for this course?
Stat/Biol 335 or Stat203 or equivalent



Will it be a prerequisite
for any other course?
no



Will it be required for the major?
yes



Should any course presently offered be dropped?
no



Date or term in which this new course becomes effective:
Fall 2004



Which full
-
time faculty members will be prepared to teach or supervise thi
s course?
O’Brien, Funk,
Barron, Buntinas



Are available material resources (e.g., library, laboratory) adequate for the course?
Yes



Are adequate resources available in the library? (Yes or No)
Yes



If no, approximate cost of obtaining sufficient resources:


Signature of Bibliographer


(on original form)
:


Date






Explain briefly the writing component of this course.
Students
are

required to analyze existing data
,

draw

correct conclusions and summarize their findings in s
hort, clear reports; a written 3
-
5 page
course paper is required of students as well.



Has this course been offered as a special topics course?
No



If yes, how many times?



When?



What enrollment?

4.


REASONS FOR ADDING THIS, COURSE
:
Needed for proposed B
ioinformatics major; also, this is a
new “hot field” in biomedical and pharmaceutical research, and many US universities are just now starting
to develop similar courses.




5.


CATALOG DESCRIPTION OF NEW COURSE
:
An introduction to statistical, probabil
istic and
mathematical methods used in bioinformatics research. Probability theory, Markov Chains, Random Walks,
Analysis of DNA and Protein Sequences, BLAST search algorithms, Computationally Intensive Methods,
Microarrays, Protein Arrays, Pattern Discov
ery




6.

PLEASE INCLUDE A SYLLABUS
(and bibliography, if available).
Please see attached


7.

7.


SIGNATURES:

(on original form)




Chairperson



Date



Academic Council Represen
tative


Date



Academic Dean


Date



Registrar's Approval of Course Number



Date




After approval has been given, and the course added to the Title Database, this form will be returned to the
Academic Dean

who will forward it to the chairperson of the initiating department.








S Y L L A B U

S


BIOL
-
STAT 337



Quantitative Met
hods in Bioinformatics


4
-
credit course

Spring Semester, 2005

Prerequisites
:
A course in
Introductory Statistics or Biostatistics (Stat 203, 335 or equivalent) or

permission

of the instructor

Texts
:

Required


Ewens, W.J. &
Grant, G.R. (2001),
Statistic
al Methods in Bioinformatics
,

New York: Springer, ISBN: 0
-
387
-
95229
-
2

Supplemental


Amaratunga, D. & Cabrera, J. (2004),
Exploration and Analysis of DNA

Microarray and Protein Array Data
, New York: Wiley, ISBN: 0
-
471
-
27398
-
8



Instructor
: Dr. Timothy E. O
’Brien


Office Phone: (773) 508
-
2129

Email: tobrien@math.luc.edu



Web page:
http://www.math.luc.edu/~tobrien/

Office: Damen Hall, Room
330G


Office Hours: T
BA


Course Overview


Predicting which conditions and diseases will develop in animals and human
subjects based on its gene
and protein characteristics must involve drawing conclusions from well
-
designed studies. As such,
meaningful decisions hinge upon the correct use of statistical hypothesis testing, prediction and
estimation. The most likely con
clusions are also drawn from probabilistic and stochastic arguments, and
a wisely chosen experimental (study) design removes biases and allows researchers to generalize from
small studies to the larger population.


This course explores recently developed m
athematical, probabilistic and statistical methods currently used
in the fields of bioinformatics and DNA microarray and protein array data analysis. These include
stochastic processes, (hidden and traditional) Markov chains, tree
-

and clustering techniqu
es (including
principal components analysis and biplots), discriminant analysis, experimental design strategies and
ANOVA methods. Our focus in this course is on the application of these techniques and on meaningful
interpretation of results.


Quizzes, Te
sts and Homework


Four

25
-
minute quizzes will be given throughout the term

in addition to eight homework assignments
.

There will be no make
-
ups for the quizzes, although the lowest quiz score will be dropped. In addition,
two examinations will be given a
nd a class project
-
paper will be assigned.


Grading Scheme



Quizzes






2
0 %


Homework





30%


Exams






35

%


Project
-
Paper





15 %


Final course (letter) grades will be awarded by the following
minimum

percentages:

A 90%; B+ 87.5%; B 80%; C+ 77.
5%; C 70%; D+ 67.5%; D 60%; F <60%


Computing


Students will develop familiarity with
SAS and S
-
Plus statistical packages as well as the BLAST search
algorithm
, though no previous computer experience will be assumed.


Academic Honesty


It is presumed
that you will do your own work on the quizzes, paper, and exams. Discussing homework
problems with others is strongly encouraged; however, submitting work as your own which is copied or
paraphrased from someone else is not permitted. Cheating includes, b
ut is not limited to, illegal
collaboration, copying, using materials not permitted on tests, and assisting others on tests. Anyone
found cheating will not be permitted to withdraw and will receive an “F” grade for the course. Your
academic dean will be
informed and a statement will be placed in your permanent file.









Semester Schedule





Week of


Topics Discussed


Jan 3


Review of Statistical Inference, Probability Theory (on variable)

Jan 10


Probability Theory (several variables), Stochastic Pro
cesses

Jan 17


Analysis of One DNA Sequence

Jan 24


Q1
;

Analysis of Multiple DNA or Protein Sequences

Jan 31


Random Walks

Feb 7


BLAST

Feb 14


Q2
;

Hidden Markov Chains

Feb 21


Computationally Intensive Methods;
First exam on
Feb
24
th

Feb 28


Spring Break

(no classes)

Mar 7


Evolutionary Models

Mar 14


Q
3
;

Phylogenetic Tree Estimation

Mar 21


Microarrays and Preprocessing Microarray Data

Mar 28


Model
-
based Inference and Experimental Design Considerations

Apr 4


Q5
;

Pattern Discovery

Apr 11


Protein Arrays
, Summary
;
Projects due

on Apr 14 (last class)

Apr 22


Second exam

(10:20


12:20)