microTSS: accurate microRNA transcription start site identification reveals a significant number of divergent pri-miRNAs

Georgios Georgakilas (), Ioannis S. Vlachos, Maria D. Paraskevopoulou, Peter Yang, Yuhong Zhang, Aris N. Economides and Artemis G. Hatzigeorgiou ()
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Georgios Georgakilas: University of Thessaly
Ioannis S. Vlachos: University of Thessaly
Maria D. Paraskevopoulou: University of Thessaly
Peter Yang: Regeneron Pharmaceuticals Inc.
Yuhong Zhang: Regeneron Pharmaceuticals Inc.
Aris N. Economides: Regeneron Pharmaceuticals Inc.
Artemis G. Hatzigeorgiou: University of Thessaly

Nature Communications, 2014, vol. 5, issue 1, 1-11

Abstract: Abstract A large fraction of microRNAs (miRNAs) are derived from intergenic non-coding loci and the identification of their promoters remains ‘elusive’. Here, we present microTSS, a machine-learning algorithm that provides highly accurate, single-nucleotide resolution predictions for intergenic miRNA transcription start sites (TSSs). MicroTSS integrates high-resolution RNA-sequencing data with active transcription marks derived from chromatin immunoprecipitation and DNase-sequencing to enable the characterization of tissue-specific promoters. MicroTSS is validated with a specifically designed Drosha-null/conditional-null mouse model, generated using the conditional by inversion (COIN) methodology. Analyses of global run-on sequencing data revealed numerous pri-miRNAs in human and mouse either originating from divergent transcription at promoters of active genes or partially overlapping with annotated long non-coding RNAs. MicroTSS is readily applicable to any cell or tissue samples and constitutes the missing part towards integrating the regulation of miRNA transcription into the modelling of tissue-specific regulatory networks.

Date: 2014
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DOI: 10.1038/ncomms6700

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