1. 5 mTorr. Samples were analyzed using selected reaction monitoring mode with a scan width selleckchem of 1 m z and a scan time of 0. 05 s. The SRM parameters for most metabolites have been published previously. This method was used to scan for almost 300 meta bolites. Xcalibur software was used to manually assess the elution time of the correct LC spectral peak for each metabolite specific SRM. The Quan Browser utility in Xcalibur was then used to integrate the LC spectral peak area for each detected compound, and these data were exported to a Microsoft Excel spreadsheet for fur ther processing. Statistical analysis Statistical analysis of the microarray data was performed using R 2. 9. 0 and routines contained in Bioconductor. GC robust multi array average was used to normalize and scale the raw data from CEL files.

The normalized data were filtered for low expression by removing any probes with normalized expression less than 3 in at least 5 arrays. Statistical significance of gene expression differences were analyzed by one way ANOVA and empirical bayes using the limma package. Differential expression was defined based on false discovery rate adjusted p value 0. 05. False discovery rate for differential expression and for GO and KEGG enrichment testing was controlled using the Benjamini Hochberg method. Venn diagrams of differentially expressed genes were plot ted to visualize the number of differentially expressed genes for each treatment comparison and their intersec tions. Hierarchical clustering of significant genes was per formed using the hclust function and a hierarchical clustering heatmap was created using heatmap.

2 in the gplots package. Hierarchical clustering also was used to identify correlated patterns of gene expression and meta bolites. The Database for Annotation, Visualization and Integrated Discovery and ClueGO, a Cytoscape plug in, were used for Gene Ontology at level 6 and 7 and KEGG analysis of differentially expressed genes. Statistical analysis of metabolomic data was performed using an analysis tool that we developed specif ically for metabolomic data analyses. The script, written in the language R, uses linear mixed effect modeling to normalize metabolomics data AV-951 containing both fixed and random effect confounding variables. The script averages any replicate measurements made on ex perimental units and performs ANOVA to test for statis tical differences between experimental groups.

Aquaculture is the fastest growing animal production activity worldwide, supplying an increasing proportion of BAY 734506 fish for human consumption, estimated at around 50% of total supply in 2008. However, the growth of marine aquaculture is threatened by its excessive reli ance on fishmeal and fish oil from wild stocks for the production of fish feeds, which is also an eco logically unsound practice. Almost 89% of the total glo bal production of FO is currently used by aquaculture and the future of this activity strongly depends on the reduction of dependen