MicroRNAs are very important in post-transcriptional regulation in animals and plants.Most recent investigations in this area have focused on the targets of microRNAs; fewer have focused on their transcriptional regulation. Various databases of microRNAs target genes in mammals have been developed, but such databases in plants area limited.

No public resource integrates the upstream and downstream regulating elements of microRNAs.

These facts motivate our establishment of a database, AtmiRNet, for the retrieval of microRNA regulatory networks in Arabidopsis.

Since few data about microRNA promoters are available, next-generation sequencing (NGS) data were recently used to construct a predictive model for transcription start sites (TSSs) of microRNAs in Arabidopsis based on the support vector machine (SVM) algorithm. Accordingly, in this study, 187 Arabidopsis microRNA promoter sequences were collected using 63 experimentally verified and 124 SVM-predicted TSSs.

Co-occurrence transcription factor binding sites (TFBSs) analysis was conducted to identify transcription factors (TFs) with high confidence and reconstruct upstream regulatory networks of Arabidopsis microRNAs based on co-expressed microRNAs-coding genes from microarray experimental data. Finally, several microRNAs target databases and predictive tools were integrated into AtmiRNet to develop the microRNAs regulatory networks in Arabidopsis.