The presented strategy can be easily extended by implementing expression information about developing embryos obtained through public microarray and SAGE databases.
While gene expression markers are being developed to monitor sub-bleaching levels of stress in situ (e.g., [ 261]), Kenkel et al. [ 322] opined that the current challenge for implementing expression-based methods lies in identifying coral genes demonstrating the most pronounced and consistent stress response, preferably with a large dynamic range to enable reliable quantification.
Quantitative expression levels of present transcripts were estimated using the PLIER algorithm (Probe Logarithmic Intensity Error) for normalization that was implemented in Expression Console (Affymetrix).
We implemented this expression as a generic function for two software applications: S-PLUS and MATLAB.
In our EABM we implemented the expression of the rpoS gene by modeling the agents to spend less energy and stop cellular division under starvation conditions.
The data were implemented with expression probe set summarizing data.
Data passed quality controls implemented in Expression Console software, build 1.2.0.20, and QC Tool (Affymetrix, Santa Clara, CA, USA).
Principal Component Analysis implemented using expression data obtained for the five contigs allowed clear separation of most, but not all, resistant F2 plants from the sensitive F2 plants (Fig. 6).
Clustering of genes was carried out using CLICK, a clustering algorithm implemented in EXpression Analyzer and DisplayER (EXPANDER) 4.0.2 [ 95], which does not require prior assumptions on the structure or the number of clusters to be found.
We implemented these expressions in a 3.2 GHz Quad-Core Intel Xeon computer with 16GB of RAM.
