The genome-wide identification of microRNA transcription start sites (miRNA TSSs) is essential for understanding how miRNAs are regulated in development and disease. In this study, we developed mirSTP (mirna transcription Start sites Tracking Program), a probabilistic model for identifying active miRNA TSSs from nascent transcriptomes generated by global run-on sequencing (GRO-seq) and precision run-on sequencing (PRO-seq). MirSTP takes advantage of characteristic bidirectional transcription signatures at active TSSs in GRO/PRO-seq data, and provides accurate TSS prediction for human intergenicmiRNAs at a high resolution. MirSTP performed better than existing generalized and experiment specific methods, in terms of the enrichment of various promoter-associated marks. MirSTP analysis of 27 human cell lines in 183 GRO-seq and 28 PRO-seq experiments identified TSSs for 480 intergenic miRNAs, indicating a wide usage of alternative TSSs. By integrating predicted miRNA TSSs with matched ENCODE transcription factor (TF) ChIP-seq data, we connected miRNAs into the transcriptional circuitry, which provides a valuable source for understanding the complex interplay between TF and miRNA. With mirSTP, we not only predicted TSSs for 72 miRNAs, but also identified 12 primary miRNAs with significant RNA polymerase pausing alterations after JQ1 treatment; eachmiRNA was further validated through BRD4 binding to its predicted promoter. MirSTP is available at http://bioinfo.vanderbilt.edu/mirSTP/.
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