|Opis:||Introduction: With the development of high-end sequencing technologies, that produce large amounts of data from biological samples, the number of software tools for analyzing this data has also rapidly increased, but there is no agreement on the most appropriate approach for identifying differentially expressed genes. The purpose of this master's thesis was to analyze two approaches for the RNA-seq data analysis and validated their results with the gold standard.
Methods: Here, we compare two approaches, edgeR (Robinson, et al., 2010) and limma (Ritchie, et al., 2015) -voom (Law, et al., 2014), and we verified their results using the RT-qPCR method. Using RT-qPCR, we verified four genes that had differently computed log2FC and p-values. Finally, the results of all three methods were analyzed with the SPSS software tool.
Results: The results of the Spearman's rank-order correlation showed a strong correlation between calculated log2FC and p-values of both approaches, but the Wilcoxon’s test showed that the values were significantly differ. Among the four selected genes, only three were analyzed with RT-qPCR, since the primers for one gene were not specific enough. Obtained results were more matched with the voom approach than with the edgeR, which was also confirmed by Spearman's correlation and the Wilcoxon signed-rank test.
Discussion: From the results we concluded that the voom approach is better, since it gives more reliable results, even though we had a very small sample size (3 individuals for each group).|