Location: Bioenergy Research Unit
Title: Unification of Gene Expression Data for Comparable Analyses Author
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: August 1, 2010
Publication Date: N/A
Technical Abstract: Over the past decade, a vast amount of gene expression data has been generated but most data sets are not comparable due to a lack of reliable reference standards. This not only affects unbiased data assessment and clinical applications but also damages the invaluable creation of database resources for the larger scientific community. Numerous efforts have been made in developing standard quality control references for a variety of applications; however, there is no commonly accepted standard available. Generating expression data of inherent value remains a challenge. Based on previously developed universal ribonucleic acid (RNA) controls for microarray and real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays, this study presents a sole mRNA reference for PCR cycle threshold (CT) and a master equation of standard curves for consistent data acquisition and normalization for qRT-PCR array assays. The performance of the robust reference is independent from stress and experimental conditions in yeast genome background. Such standard reference developments safeguard data reproducibility and allow unification of data from different experimental sets for comparable analysis. Valid dynamic detection ranges were defined from 10 to 7000 pg and 100 fg to 1000 pg for microarray and qRT-PCR assay, respectively. The universal RNA controls also allow comparison of expression data from different platforms of microarray and qRT-PCR within the valid overlapping detection ranges between 10 to 1000 pg. Application of the quality control reference will not only enhance gene expression technology, but also preserve the value of exponentially growing gene expression databases. Unifying gene expression data for comprehensive analyses will make it possible to gain insight into complex gene interactions and regulatory networks of biological events using more integrated approaches such as bioinformatics and systems biology.