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However, transcriptome data from the quantitative PCR (Q-PCR) and DNA microarray analyses currently deposited in the database are related to a fixed amount of RNA collected per sample.
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Currently, one major drawback when performing microarray analyses on data derived from dietary intervention studies is the lack of proper statistical tools.
Based on microarray analyses in hepatocellular carcinoma (HCC), SLC22A1/ OCT1 mRNA seems to be downregulated, but systematic protein expression data are currently missing.
While microarray analyses have been successfully used to classify and/or predict disease state or outcome in oncology [5] [10], no examples currently exist in which gene expression has been used to predict changes in weight after nutritional or clinical intervention in obese individuals.
Work flow for microarray analyses.
Microarray analyses using the OsNAC6 overexpressors clarified the downstream genes, which included many stress-inducible genes.
Open image in new window Fig. 3 Microarray analyses of seawater samples.
Candidate sequences were extracted from the 50 most highly upregulated genes from the microarray analyses.
Microarray analyses of rice tissues challenged with the Asian rice gall midge biotype 1 (GMB1).
We compared OsDREB1A, AP59, OsbZIP23, SNAC1, OsNAC5 and OsNAC6 downstream genes identified by microarray analyses.
The RNAs were isolated and reverse transcribed into cDNAs, followed by microarray analyses.
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