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Design of a sequence based miRNA clustering method; analysis of fungal miRNAs and host organism target genes

Narcı, Kübra
MicroRNAs are small non-coding RNA molecules which contain 21-25 nucleotides, and function in post transcriptional regulation by inhibiting the translation of mRNA targets. miRNAs typically affect gene regulation by forming composite feed forward circuits (cFFCs) which also comprise a transcription factor (TF) and a target gene. By analyzing these cFFCs, the contribution of miRNAs in altering TF networks can be revealed. These contributions could either be the de-escalation of the target gene repertoire or to increase the redundancy through cFFC formation. To conduct the analysis, the connections between genes, miRNAs, and TFs are obtained using two datasets one of which is obtained from human myeloid leukemia cell line. These two datasets are also different from each other in terms of the numbers of TFs and miRNAs that are included in the networks and the significance of the predicted connections. The first dataset which contains connectivity information of a normal cell involves 83 TFs, 564 miRNAs and 5169 genes which construct 124,740 and 34,298 human-mouse conserved TF and miRNA regulatory connections, respectively. The second dataset which contains 137 miRNAs, 274 TFs and 6749 genes which are compiled from the FANTOM 4 database from which the total number of human-mouse conserved regulatory connections is identified as 6631 for miRNAs and 60969 for TFs. Then, in order to reveal the significance on a statistical level, the randomization tests are applied to the connectivity matrix. Obtaining the significance of miRNA-based cFFCs lead us to conclusions about the effect of miRNAs in fine-tuning gene regulatory networks and the evolutionary role of miRNAs in the cell regulation.