Unbiased bioinformatics analysis of microRNA transcriptomics datasets and network theoretic target prediction

Abstract All attempts for the clinical translation of preclinically proven approaches to reduce myocardial injury due to acute ischemia-reperfusion failed in the past three decades. According to accumulated evidence, these failures are most likely the result of relying on biased, hypothesis-driven target identification and ignoring those comorbidities and comedications that in most cases accompany ischemic heart disease and may modify cardioprotective signaling. Therefore, an unbiased omics-based effort supported by network theoretic target identification to uncover common pathways involved in ischemia-reperfusion injury complicated by comorbidities and comedications could provide a solution for the problem mentioned earlier. Due to limitations of other available omics technologies, a cost-effective but still comprehensive workflow can be built around microRNA transcriptome profiling and network theoretic microRNA-target prediction. In this chapter, we summarize the tools necessary for such an unbiased approach including omics techniques, bioinformatics, and network theoretic algorithms, also providing examples for the successful application of this toolset.