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High level microarray analysis uses data mining techniques in order to analyze the huge volume of all this biological information [ 2].
However, the bottleneck is the construction of an expression matrix of TDFs (rows) per time points (columns) of HiCEP electrophoretic data due to the problem of imperfect alignment, though most microarray analysis uses such matrices as given data (e.g., [ 17, 18]).
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This database contains microarray data of 40 different experimental conditions obtained through microarray analysis using the same custom microarray chip as described in this manuscript.
In order to understand gene expression changes in rice leaves undergoing a ClsA-induced HR, we performed microarray analysis using the Affymetrix GeneChip rice genome array.
In 2015, Pfeffer et al. performed a microarray analysis using RNA prepared from plasma-derived EVs and validated the candidate miRNAs.
The results of our DNA microarray analysis using three strains showed increased expression ratios under heat conditions for FMP21, YER034W and PRM5.
The results of the microarray analysis used in this study (series number: GSE31196) are available at the NCBI-GEO (Edgar et al. 2003).
Here, we describe the experimental design, methodology, and data validation for the microarray analysis used to characterize the transcriptome profile of iHCs at different stages of their differentiation.
To clarify the onset of Fe deficiency, changes in gene expression were examined by microarray analysis using rice roots at 3, 6, 9, 12, 24, and 36 h after the onset of Fe-deficiency treatment.
We carried out oligonucleotide microarray analysis using the GeneChip U133plus2 Affymetrix expression microarray (Affymetrix, Santa Clara, CA).
We performed global expression microarray analysis using a custom-made Affymetrix GeneChip.
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