BMI Course Information
BMI 591 Topics in Translational Bioinformatics (3)
Instructor Information |
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Fall 2009 |
Course not offered |
Spring 2009 |
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Catalog Description
The course will provide an introduction to bioinformatics methods and applications used in the field of translational medicine research. Topics will include bioinformatics data acquisition and management (e.g., microarrays, sequencers, database integration), analysis methodologies (e.g., dynamic programming, statistics, data mining) and applications. Active student participation will be required through weekly paper reading assignments, presentations and projects.
Prerequisites
Admission to any SCI graduate program
Familiarity with genetics concepts
Ability to critically read published research
Textbook and Other Materials
2-3 articles will be provided weekly for discussion
Recommended (not required) textbooks:
Bioinformatics Sequence and Genome Analysis, by David Mount, 2nd edition
Bioinformatics for Geneticists: A Bioinformatics Primer for the Analysis of Genetic Data, editor Michael Barnes, 2nd edition
Molecular Biology of the Cell, Alberts et al, 4th edition
Course Learning Outcomes
To provide students with an overview of the nature of data and analysis techniques used in bioinformatics areas such as sequence alignment, gene expression, genome wide association studies, copy number variation, next generation sequencing
To prepare students for the development and application of integrative computational methods to the analysis of biological data
To enable students to locate and use various algorithms and tools for bioinformatics data management and analysis
To acquire and develop skills necessary to: Critically read and evaluate research articles in the area of bioinformatics
Perform peer evaluation and participate in class discussion of article presentation
Research topics in bioinformatics and translational research
Present various research topics
Write a research paper on a topic of choice in the field of bioinformatics and translational research
Major Topics and Time Covered
Weeks 1 and 2: Bioinformatics data – genetics overview, biological data types and lab acquisition methods; biomedical publications database search and bibliography management
Weeks 3 and 4: Sequence alignment
Weeks 5 – 7: QTLs, disease association studies, genotypes, genome wide association studies
Week 8: Next Generation Sequencing
Week 9 - 10: Gene expression analysis
Week 11: Copy number variation
Weeks 12-13: Integrative Data Analysis
Weeks 14-15: Final project presentations