MicroRNAs (miRNAs) are small non-coding RNAs that can regulate gene expression playing vital role in nearly all biological pathways. Even though miRNAs have been intensely studied for more than two decades, information regarding miRNA transcription regulation remains limited. The rapid cleavage of primary miRNA transcripts (pri-miRNAs) by Drosha in the nucleus hinders their identification with conventional RNA-seq approaches. Identifying the transcription start site (TSS) of miRNAs will enable genome-wide identification of their expression regulators, including transcription factors (TFs), other non-coding RNAs (ncRNAs) and epigenetic modifiers, providing significant breakthroughs in understanding the mechanisms underlying miRNA expression in development and disease. Here we present a protocol that utilizes microTSS, a versatile computational framework for accurate and single-nucleotide resolution miRNA TSS predictions as well as miRGen, a database of miRNA gene TSSs coupled with genome-wide maps of TF binding sites.
Keywords: ChIP-seq; DNase; Histone marks; Machine learning; RNA-seq; TF; TSS; Transcription factor; Transcription start site; miRNA; microRNA; pri-miRNA.