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![Hierarchical clustering of two-dimensional scores from the principal component analysis. The two-dimensional score identifies the positions of genes on the right and the left side of the plot. Each colored circle represents one transcript. Red and blue colors represent up and down regulated transcripts. Only those transcripts that were differentially expressed between healthy and diseased samples (fold change ≥2.0, p-value ≤ 0.05) are shown. Each sample in the dataset is depicted in a single square, in which the red color represents the healthy sample and the blue color represents the diseased one. The axes represent the first two principal components derived from the dataset.](1471-2164-12-539-3){#F3}

Discussion
==========

The current study aimed to develop a method to identify differentially expressed genes between healthy and diseased frog intestines using next generation sequencing and cDNA microarrays. The main aim of this methodology was to compare the two techniques and validate the microarray results. Our main findings showed that similar sets of differentially expressed genes were obtained using both techniques. The more striking comparison is that of the patterns of the two microarrays, which are in general agreement. The hierarchical clustering of the differentially expressed genes across all comparisons using the two techniques showed that frogs of different ages have more similar transcriptomes than frogs of the same age-group. In addition, the gene expression profiles of pooled samples were more similar to one another than to those of the individual samples. This could be attributed to the decrease in the bacterial community of the gut with age \[[@B26]\]. The differentially expressed genes between the pooled samples and the individual frog intestines suggest that the loss of diversity in the gut of frogs with increasing age is not due to a decrease in the number of bacteria in the intestine, but to a change in the bacterial community itself.

The gene expression levels of all the genes screened by microarrays are not equal, and this is probably due to the fact that many of the probes are not specific for particular genes and, therefore, some genes might have been missed using microarrays. On the other hand, the sequencing results are not perfect. The

https://colab.research.google.com/drive/1YeSek0OGenPtZ6h-QAqjekdWqJU6bqIJ
https://colab.research.google.com/drive/1jAqedoek6SKSBMpHdFdBpidBTJtEHWDF
https://colab.research.google.com/drive/1UYPRqjLBPcgzqRILye0behsplqGGlc_N
https://colab.research.google.com/drive/1krEiychKxtHjPUjwFa469nl_JnX0mg5C
https://colab.research.google.com/drive/1TSrivXJIWrd9v2VAjhr_WqsN9cJFgbm7

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