James M Minor | Search PubMed

PubMed2006

Microarray quality control.

Physically separated groups of specific sequences (probes) provide useful high through put (HTP) measurements for the amount of selected DNA/RNA sequences in a biological target sample. Unfortunately, these measurements are impacted by various technical sources, such as platform production factors, target preparation processes, hybridization method/conditions, and signal-extraction devices and methods. Given the typically huge population of signals, statistical methods are especially effective at estimation and removal of such technical distortions (Churchill, 2002; Kerr et al., 2000; Yue et al., 2001), as well as providing metrics for computer-based quality control (QC), for example, autoQC (Minor et al., 2002a). This chapter reviews statistical procedures that have been validated by successful applications in both large-scale commercial ventures (Ganter et al., 2005) and individual research studies (Parisi et al., 2003, 2004) involving HTP projects. This chapter focuses on methods for spatially distributed probes on a flat medium surface such as glass, collectively known as a microarray.

Algorithms↗

PubMed2005

Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action.

Successful drug discovery requires accurate decision making in order to advance the best candidates from initial lead identification to final approval. Chemogenomics, the use of genomic tools in pharmacology and toxicology, offers a promising enhancement to traditional methods of target identification/validation, lead identification, efficacy evaluation, and toxicity assessment. To realize the value of chemogenomics information, a contextual database is needed to relate the physiological outcomes induced by diverse compounds to the gene expression patterns measured in the same animals. Massively parallel gene expression characterization coupled with traditional assessments of drug candidates provides additional, important mechanistic information, and therefore a means to increase the accuracy of critical decisions. A large-scale chemogenomics database developed from in vivo treated rats provides the context and supporting data to enhance and accelerate accurate interpretation of mechanisms of toxicity and pharmacology of chemicals and drugs. To date, approximately 600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations). Accomplishing this task required evaluating and improving a number of in vivo and microarray protocols, including over 80 rigorous quality control steps. The utility of pairing clinical pathology assessments with gene expression data is illustrated using three anti-neoplastic drugs: carmustine, methotrexate, and thioguanine, which had similar effects on the blood compartment, but diverse effects on hepatotoxicity. We will demonstrate that gene expression events monitored in the liver can be used to predict pathological events occurring in that tissue as well as in hematopoietic tissues.

5-Aminolevulinate Synthetase↗