 
|
|
|
|
|
Module 8: Bioinformatics in Clinical Genomics and Proteomics ResearchFACULTY Dr. Ying Xu, Professor of bioinformatics and computational biology and director of the Institute of Bioinformatics, University of Georgia Cynthia Nakatani, Independent Consultant, Data Analysis and Bioinformatics Analysis of expression data cannot be a task that is executed in a vacuum. The person doing the analysis should have an intimate knowledge of the platform and how the experiment was conducted. Data analysis should be considered part of the experimental cycle, not just the end of the road. This instructional module will start here with an emphasis on understanding how the data came into being and the importance of reviewing the raw data. QC of the raw data is an important step that is often overlooked, but can have truly beneficial consequences down the line. Students will examine the importance of understanding how experimental data is generated as well as the importance of reviewing raw data prior to post-processing. Statistics should not be intimidating. We will review techniques for basic single variable analysis and work our way up to multivariate analysis. Visualization techniques and the most popular clustering and classification tools will be reviewed. Basic statistical principles inherent in proteomic and genomic clinical data analysis will be reviewed. Univariate and multivariate statistical analysis will be explained and demonstrated using real genomic and proteomic data. Our goal for this course is to educate our students on the general concepts and eliminate any "fear" associated with biostatistics. Students who participate in this module will be able to think from a data analysis perspective and apply it to their experimental designs. They will become familiarized with the most popular analysis techniques for expression data and will have a solid footing on which to either use analysis tools themselves or communicate intelligently with their fellow statisticians. Furthermore, students will gain first-hand experience of analyzing genomics and proteomics data of various analytical platforms. They will become proficient in mining such data for the purpose of moving from transcriptional to translational studies or understanding mechanisms behind polygenic regulation, dysregulation, and concordant alterations in protein phenotype and post-translational modifications.GenNext's 2007 In Person Translational Courses have been completed. GenNext would like to thank its Education Partners and excellent instructors for another highly received Translational Research Program.Information regarding GenNext's 2008 program will become available shortly. Sign-up to find out about upcoming GenNext courses. For more information about this course, please contact GenNext Technologies at inform@gennexttech.com or call 650-563-9577.
|
||||
| Copyright 2007, 2008 GenNext Technologies Inc. |