It will greatly facilitate microarray data re-analysis and cross-platform comparison.
Following elucidation of biologically enriched themes, the microarray data was re-mined and analyzed with focus on identifying and interpreting all statistically valid changes gene expression associated with the highlighted pathways.
Microarray data was re-mapped to the current MR1 gene models, which have been extensively manually curated (see 'knowledgebase description' for details), to update the assignment of the IGs/genes and then compiled in a table (Supplementary Data 3) comprised of the IG log(2) ratio along with current annotations and log(2) ratios of its up- and downstream genes.
To obtain sufficient genes to get meaningful IPA results, microarray data were re-filtered using a threshold of P < 0.01 for differential expression of genes at each time point and for the change in gene differential expression (4× minus 2×) on day 21 vs. that on day 23.
However, in the case of MammaPrint it is possible to undertake a transparent re-analysis of the data using an alternative approach, since the raw microarray data are available.
We re-analyzed microarray data obtained from time-series experiments (0, 1, and 4 hours) performed on peritoneal macrophages from wildtype, MyD88 KO, TRIF KO, and MyD88/TRIF DKO mice treated with 100 ng/ml of LPS (Salmonella Minnesota Re595, Sigma) [10].
In the two microarray data sets that we have re-analysed both HAI-1 and midkine mRNA expression is elevated in Ta UCB but does not increase further with increasing stage (Supplementary Figure 3).
One of the interesting themes to emerge from the microarray data was the apparent re-alignment of lipid metabolism.
In addition to just collecting microarray data, MADMuscle involves an automatic re-normalization and re-analysis of all these data sets to identify clusters of co-expressed genes (see Additional File 1).
We attempted to re-analyze the microarray data published by Cantuti-Castelvetri et al. [11] according to our criteria.
We used our revised FindTar algorithm (FindTar version 2.0) to calculate loop scores for the miRNA MREs for all seed-containing mRNAs which were up- or down-regulated by the miRNAs used, and re-analyzed their microarray data (Table 2).
