In the future, additional microarray data from rodents, primates, and perhaps other mammals will contribute to a further understanding of the common in vivo response and, ultimately, identification of disease mechanisms that are unique to specific agents or pathogens.
Using meta-analysis of large collections of microarray data to prioritize QTL candidates in rodents can present several advantages over similar approaches in humans.
We think that these resources and the development of better methods for data analysis will greatly improve the success of using microarray data to dissect complex traits in rodents.
Based on published microarray data of the liver stages of the rodent parasite P. yoelii many of the genes for the DOXP pathway are assumed to be expressed at least as strongly in liver stage as in blood stage parasites ([24]; Figure S10).
One approach that has been used for large collections of microarray data has been the performance of a meta-analysis across data on rodent models of divergent ethanol drinking collected from multiple centers and strains [ 3].
Networks, functions, and pathways analyses were generated using IPA (Ingenuity Systems, http://www.ingenuity.com, Redwood City, CA) which assists with microarray data interpretation via grouping DEG into known functions, pathways, and networks based primarily on human and rodent studies.
For a broad-level comparison, we used a meta-analysis of human brain, kidney, and muscle microarray data (Zahn et al., 2006) and a meta-analysis of different tissues from human, primate, and rodent tissues (de Magalhaes et al., 2009).
In addition, canonical pathways and functions analyses were created by IPA (Ingenuity Systems, Mountain View, CA, USA), which assists with microarray data interpretation via grouping DEG into known functions, pathways and gene interaction networks, based primarily on human and rodent studies.
Analysis of microarray data from untreated or vehicle-treated animals within the control arm of toxicogenomics studies has yielded useful information on baseline fluctuations in liver gene expression in the rodent.
TK and RNI analyzed the microarray data.
