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The datasets were analysed using SAS software, version 9.2 [ 24].
The datasets were analysed using SAS®/STAT® software, version 9.2 of the SAS System for Windows (SAS Institute Inc., Cary, NC, USA).
In an effort to minimize bias, the datasets were analysed using three different statistical approaches to identify the most stably expressed genes during grape berry development.
The datasets were analysed using GOToolBox; specifically GO-Stats (http://genome.crg.es/GOToolBox/) [ 30] and GO Finder Version 0.83 software (http://www.yeastgenome.org/cgi-bin/GO/goTermFinder.pl) which allowed the hierarchical clustering and over-representation (enrichment) or under-representation (depletion) of genes based on shared Gene Ontology (GO) terms.
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The dataset was analysed using Stata version 11.0 (StataCorp Inc., College Station, TX, USA).
The dataset was analysed using the SPSS V 15.0 statistical package (NASDAQ, Bloomingdale, IL 60108, USA) and the Microsoft calculation software Excel V 12 (2007; Microsoft corporation, USA).
The resulting datasets were analysed using the Bioconductor software environment [ 65].
The same datasets were analysed using a Maximum Likelihood approach, as implemented in the web available PHYML application [ 95, 96].
Both datasets were analysed using the program MrBayes (see below).
All three datasets were analysed using the same MP settings.
All five transcriptomics datasets were analysed using the seven ML methods and BioHEL+RGIFE (Table 3).
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