STAT/BIOC 842

Computational Biology (Bioinformatics)

Instructor: Steve (Istvan) Ladunga, Ph.D.
Professor of Computational Biology
Department of Statistics and Center for Biotechnology
E145 Beadle Center
Phone: (402) 472-6074
sladunga@unl.edu

Credit hours: 3. 1 hour 15 minutes lecture, 2 hour 50 minutes computer laboratory.
Term Offered: Every Fall Semester.
Call Number: 8944.
Rooms: Lectures: Tuesdays, 2:00-3:15, Room N176, Beadle Center
Computer Lab: Thursdays, 3-5:50 pm, Rm. 14, College Business Admin. Note that the time has changed recently!!!!!
Office Hours: Wednesdays, 10 am - 12 pm E145 Beadle

Course Prerequisites:  
Any introductory course in biology or statistics:

• BIOS 101 General Biology OR
• BIOS 103 Organismic Biology OR
• BIOS 206 General Genetics OR
• BIOC 321 Elements of Biochemistry   OR

• STAT 218 Introduction to Statistics OR
• BIOC 432/832 Gene Expression and Replication OR
• BIOS 477/877 Bioinformatics and Molecular Evolution

Necessary background

• Basic knowledge of molecular biology is necessary but statisticians,
  computer scientists and mathematicians will be exempted from that.
• You should be able to navigate the Internet, use Microsoft Office including Word, Powerpoint and Excel.
• No programming or database skills are required.  

The scope of the course: computational molecular biology helps us to do biology on computers. 
In ten years, this term will make no sense since almost all biology will be BOTH computational and experimental.  

Learning objectives that are measurable during tests, presentations and computer labs:

• Present your results in probabilistic terms using statistical significance
• Perform sophisticated searches over enormous databases, interpret their results
• Perform genomic comparisons, display genes and large genomic regions in Genome Browser
• Identify differentially regulated genes in disease or stress conditions, as indicated by gene expression experiments.
• Perform (semi)automatic parsing of the literature over millions of publications.
• Be able to apply Gene Ontology, pathways, gene set enrichment analysis
• Be able to use the LINUX operating system at the novice level
• Understand the signal-to-noise ratio of your experiment, estimate the statistical significance of the observations

Other objectives:

• Understand and analyze sequence polymorphisms and linkage disequilibria.
• Understand principles of complex networks of transcriptional regulation or protein-protein interactions.
• Know how to integrate the results of your experiments into the vast knowledge available in biological databases
• Understand the trends in personalized medicine
• Understand and get practice in proteomics analyses

Who would benefit from taking this course?
This course is designed first of all for biology, agronomy and statistics students. However, computer science, mathematics, physics and chemistry majors also may find it beneficial.  This course is designed to benefit computational and experimental biologists as well as biostatisticians to understand the principles of analyzing biological data, building models and testing hypotheses using computer science paradigms. This Course does not depend on any graduate course.

Lecture Topics/Course Outline and Assessment Plan: 
I believe that one of the most critical but somewhat overlooked skill is reading, understanding and presenting scientific publications that are reasonably challenging and matching to your background (e.g., biology, statistics, or computer science). Each student will be assigned a book chapter and/or a journal article to present during the computer labs using PowerPoint.
Your final grade will be based on the following scale:

• 35 percent based on the final project
• 25 percent based on the publication presentations
• 25 percent based on test assignments
• 15 percent based on class participation

Methods: lecture (75 minutes) and computer laboratory (170 minutes).

Lectures will be delivered at the N176 Beadle Center.

Laboratory work Rm. 14 of the College of Business Administration.

Project and publication presentation will rely on the servers and node computers of the Bioinformatics Core Research Facility.

References/Textbooks
Most of the Course will be taught on the basis of recent scientific reviews.

Recommended but not mandatory textbook:
Baxevanis, A.D. and Ouellette (eds.) Bioinformatics. A Practical Guide to the Analysis of Genes and Proteins. (2004) Third Edition. Wiley Interscience. ISBN 0-471-47878-4. 540 pages.   

We will also use selected chapters from the series: Current Protocols in Bioinformatics (Wiley Interscience).  This series provides both theoretical foundations and practical instructions to the most important bioinformatics algorithms and tools. 
For literature searches I recommend:
Jensen, L.U. (2006) Biological literature mining – from information retrieval to biological discovery,
Tutorial, International Society for Computational Biology.
 

A preliminary schedule of classes
The order and subject of the classes may change.

Students with disabilities are encouraged to contact the instructor for a confidential discussion of their individual needs for academic accommodation.  It is the policy of the University of Nebraska--Lincoln to provide flexible and individualized accommodation to students with documented disabilities that may affect their ability to fully participate in course activities or to meet course requirements.  To receive accommodation services, students must be registered with the Services for Students with Disabilities (SSD) office, 132 Canfield Administration, 472-3787 voice or TTY.