The regulation of intragenic miRNAs by their own intronic promoters is one of the open problems of miRNA biogenesis. Here, we describe PROmiRNA, a new approach for miRNA promoter annotation based on a semi-supervised statistical model trained on deepCAGE data and sequence features. We validate our results with existing annotation, PolII occupancy data and read coverage from RNA-seq data. Compared to previous methods PROmiRNA increases the detection rate of intronic promoters by 30%, allowing us to perform a large-scale analysis of their genomic features, as well as elucidate their contribution to tissue-specific regulation. PROmiRNA can be downloaded from http://promirna.molgen.mpg.de.

The iCLIP and eCLIP techniques facilitate the detection of protein–RNA interaction sites at high resolution, based on diagnostic events at crosslink sites. However, previous methods do not explicitly model the specifics of iCLIP and eCLIP truncation patterns and possible biases. We developed PureCLIP (https://github.com/skrakau/PureCLIP), a hidden Markov model based approach, which simultaneously performs peak-calling and individual crosslink site detection. It explicitly incorporates a non-specific background signal and, for the first time, non-specific sequence biases. On both simulated and real data, PureCLIP is more accurate in calling crosslink sites than other state-of-the-art methods and has a higher agreement across replicates.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1364-2) contains supplementary material, which is available to authorized users.

In miRNA biogenesis, pri-miRNA transcripts are converted into pre-miRNA hairpins. The in vivo properties of this process remain enigmatic. Here, we determine in vivo transcriptome-wide pri-miRNA processing using next-generation sequencing of chromatin-associated pri-miRNAs. We identify a distinctive Microprocessor signature in the transcriptome profile from which efficiency of the endogenous processing event can be accurately quantified. This analysis reveals differential susceptibility to Microprocessor cleavage as a key regulatory step in miRNA biogenesis. Processing is highly variable among pri-miRNAs and a better predictor of miRNA abundance than primary transcription itself. Processing is also largely stable across three cell lines, suggesting a major contribution of sequence determinants. On the basis of differential processing efficiencies, we define functionality for short sequence features adjacent to the pre-miRNA hairpin. In conclusion, we identify Microprocessor as the main hub for diversified miRNA output and suggest a role for uncoupling miRNA biogenesis from host gene expression.