? High-quality, quantitative microarrays are an absolute requirement of array experiments involving comparative analysis of related genomes, or sensitive and specific diagnosis of infectious disease. Our goal is to develop a suite of software applications that incorporate experimentally validated solution hybridization parameters into the processes of microarray design and microarray data preprocessing. The specific hypothesis we propose to test is that, using solution hybridization parameters, we can accurately predict the binding behavior of perfectly matched and several classes of mismatched oligonucleotides on an array.
Specific aims of the project proposed herein are 1) to model and predict hybridization behavior of DNA arrays based on solution hybridization parameters, 2) to design test arrays to quantify the impact of biophysical properties of the probe and target on array hybridization and assess our ability to predict hybridization, 3) apply biophysical criteria to a real-world probe design problem and use the experience to develop a best-practice approach for using these criteria in design, 4) integrate biophysical modeling with accepted array design and analysis procedures in an automated pipeline, and 5) develop a user interface for the software and tools for integration of biophysics-based analysis with established microarray data analysis procedures. The predictive models that we develop can be applied both in the design of optimized arrays and in preprocessing of raw signal for analysis. As an empirical test of our methods and array designs we will supply versions of a diagnostic array to discriminate among sequenced species of Brucella. Brucella is classified by the CDC as a class B pathogen; as an intracellular pathogen it has the alarming ability to infect macrophages and to be masked from the host's immune response. We will provide a well-designed and tested oligonucleotide microarray with the necessary probes for discrimination among three biovars of Brucelia (abortus, melitensis, and suis). ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM072619-03
Application #
7265242
Study Section
Special Emphasis Panel (ZRG1-BST-D (02))
Program Officer
Preusch, Peter C
Project Start
2005-08-01
Project End
2010-07-31
Budget Start
2007-08-01
Budget End
2008-07-31
Support Year
3
Fiscal Year
2007
Total Cost
$371,437
Indirect Cost
Name
University of North Carolina Charlotte
Department
Biostatistics & Other Math Sci
Type
Other Domestic Higher Education
DUNS #
066300096
City
Charlotte
State
NC
Country
United States
Zip Code
28223
Garhyan, Jaishree; Gharaibeh, Raad Z; McGee, Stephen et al. (2013) The illusion of specific capture: surface and solution studies of suboptimal oligonucleotide hybridization. BMC Res Notes 6:72
Gharaibeh, Raad Z; Newton, Joshua M; Weller, Jennifer W et al. (2010) Application of equilibrium models of solution hybridization to microarray design and analysis. PLoS One 5:e11048
Gharaibeh, Raad Z; Fodor, Anthony A; Gibas, Cynthia J (2010) Accurate estimates of microarray target concentration from a simple sequence-independent Langmuir model. PLoS One 5:e14464
Zahn, Laura M; Ma, Xuan; Altman, Naomi S et al. (2010) Comparative transcriptomics among floral organs of the basal eudicot Eschscholzia californica as reference for floral evolutionary developmental studies. Genome Biol 11:R101
Gharaibeh, Raad Z; Fodor, Anthony A; Gibas, Cynthia J (2008) Background correction using dinucleotide affinities improves the performance of GCRMA. BMC Bioinformatics 9:452
Gharaibeh, Raad Z; Fodor, Anthony A; Gibas, Cynthia J (2007) Software note: using probe secondary structure information to enhance Affymetrix GeneChip background estimates. Comput Biol Chem 31:92-8