MicroRNAs are small RNAs that regulate gene expression through complementary base pairing with their target mRNAs. Given the small size of the pairing region and the large number of mRNAs that each microRNA can control, the identification of biologically relevant targets is difficult. Since current knowledge of target recognition and repression has mainly relied on in vitro studies, we sought to determine if the interrogation of gene expression data of unperturbed tissues could yield new insight into these processes. The transcriptome-wide repression at the microRNA-mRNA canonical interaction sites (seed and 3'-supplementary region, identified by sole base complementarity) was calculated as a normalized Spearman correlation (Z-score) between the abundance of the transcripts in the PRAD-TCGA tissues (RNA-seq and small RNA-seq data of 546 samples). Using the repression values obtained we confirmed established properties or microRNA targeting efficacy, such as the preference for gene regions (3'UTR>CDS>5'UTR), the proportionality between repression and seed length (6mer<7mer<8mer) and the contribution to the repression exerted by the supplementary pairing at 13-16nt of the microRNA. Our results suggest that the 7mer-m8 seed could be more repressive than the 7mer-A1, while they have similar efficacy when they interact using the 3'-supplementary pairing. Strikingly, the 6mer+suppl sites yielded normalized Z-score of repression similar to the sole 7mer-m8 or 7mer-A1 seeds, which raise awareness of its potential biological relevance. We then used the approach to further characterize the 3'-supplementary pairing, using 39 microRNAs that hold repressive 3'-supplementary interactions. The analysis of the bridge between seed and 3'-supplementary pairing site confirmed the optimum +1 offset previously evidenced, but higher offsets appear to hold similar repressive strength. In addition, they show a low GC content at position 13-16, and base preferences that allow the selection of a candidate sequence motif. Overall, our study demonstrates that transcriptome-wide analysis of microRNA-mRNA correlations in large, matched RNA-seq and small-RNA-seq data has the power to uncover hints of microRNA targeting determinants operating in the in vivo unperturbed set. Finally, we made available a bioinformatic tool to analyze microRNA-target mRNA interactions using our approach.