Electrical and Computer Engineering, 55:122, Computational Genomics

Elective, 3 s.h., Spring Semester 2003

Description: An introduction to contemporary Computational methods used in

Genomics and Molecular Biology. Major topics include DNA and RNA

Sequence analysis, Sequence/Gene/Disease Mapping, Gene Expression

and Disease Gene Linkage.

The course will consist of in-depth coverage of principal Genome Science challenges, and their most recent "solutions".

(This course will be cross-listed as 2:171 in Biology).

Pre(co)requisite(s): 55:121 (cross-listed as 2:170) Introduction to Bioinformatics

Textbook: “Bioinformatics: Sequence and Genome Analysis”, D. Mount, Cold Spring Harbor Press, 2001.

References: “Programming Perl”, L. Wall, T. Christiansen, and J. Orwant, O'Reilly, 2000.

“Bioinformatics Computer Skills”, C, Gibas and P. Jambeck, O'Reilly, 2001.

A collection of papers from the published literature will also be used.

Course Objectives: Students will learn about the computational problems in the emerging areas of Bioinformatics, Computational Biology, and Genomics. The students will have varied backgrounds of engineering, computer science, and the life sciences. These students will be prepared to work in the interdisciplinary area marrying recent advances in high-performance computing and networking, with the exploding information resources of the human genome and related data.

Topics (weeks):

1. Introduction to Computational Genomics (1)

2. A Spectrum of Computing Issues: (2)

Programming and Languages, Operating Systems,

Computer Architecture, Computer Networks,

Algorithms, Data Structures, Databases.

3. Bio-Computing Technologies (1)

Introduction to UNIX, Introduction to Perl,

Introduction to BioPerl

4. Sequence Analysis - Concepts & Algorithms (2)

Comparison, Alignment, Low Complexity Analysis,

Assembly, Clustering

5. Genome Modeling (2)

Gene Prediction Concepts and Techniques, Coding,

non-coding, Intron/Exon Boundaries, Promoters,

TF Binding sites, UTR Indentification

6. Hidden Markov Models and Domain Finding (1)

7. Phylogenetic Tools and Techniques (1)

8. Protein Structure Prediction and Analysis (1)

9. Map Building Methods (1)

10. Linkage Analysis Algorithms, Tools and Applications (1)

11. MicroArrays and Expression Analysis Methods (1)

12. Pathway Elucidation Techniques and Tools (1)

Total 15 weeks

Computer Usage: Students taking the course solve problems in computational genomics together in teams using a number of programming languages and application environments. UNIX, Java, and the web are used as program development environments.

Laboratory Projects: None.

Contribution to Criterion 4 “Professional component”: x Mathematics and Basic Sciences

x Engineering Science

_ Engineering Design

x General Education

x Other (e.g., elective)

Program outcomes (Criterion 3 Outcomes for Core Courses):

Course Learning Goal	Program Outcome
1. Gain an understanding computational tools needed for a wide range of genomics problems.	A(●),C(●), K(●)
2. Gain an understanding of working in interdisciplinary teams of biologists, biochemists, medical researchers, geneticists, and computer engineers.	A(●), B(●), D(●), I(●)
3. Gain an understanding of basic bioinformatics problems and their solutions, including: nucleotide and protein sequence comparison, complexity analysis, sequence search, alignment, assembly, and gene clustering.	A(●), C(●), K(●)
4. Gain an understanding of computational biology modeling problems including: gene prediction and transcriptome interpretation.	A(●),K(●)
5. Gain an understanding of computer Markov Model building and searching of large databases.	A(●), I(●), J(●), K(●)
6. Gain an understanding of Phylogenetics, Genetic linkage analysis, and map construction.	A(●),K(●)
7. Gain an understanding of gene expression, and the current state of understanding of the mechanisms controlling regulation of gene expression. Understand computational methods for analysis of microarray technologies, and interpretations of gene expression from this data.	A(●),B(●),K(●)
8. Gain an understanding of common bioinformatics tools such as BLAST, HMMER, GENSCAN, and MFOLD.	A(●), B(●), K(●)
9. Gain an understanding of ethical, legal, and social issue associated with the Human Genome Project and its outcomes.	F(●), H(●),J(●)

○ denotes moderate contribution to the outcome ● denotes substantial contribution to the outcome

Prepared by: T. L. Casavant Date: April 2002