Abstract
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.
Highlights
MiRNAs are non-coding RNAs, approximately 22 nucleotides long, which have been shown to be crucial post-transcriptional regulators of gene expression in metazoans and plants, targeting up to 50% of the proteincoding genes [1,2]
Identification of candidate miRNA promoter regions and statistical modeling In this study we propose a semi-supervised mixture model for miRNA promoter recognition, which uses the deepCAGE data generated within the FANTOM4 Consortium as well as sequence features in order to separate putative promoters from background noise
Genomic regions enriched in cap analysis of gene expression (CAGE) tags presumably correspond to transcript transcription start sites (TSSs), our algorithm starts with scanning the genome in the regions up to 50 kb upstream of an annotated miRNA precursor, searching for clusters of mapped CAGE tags (Figure 1)
Summary
MiRNAs are non-coding RNAs, approximately 22 nucleotides long, which have been shown to be crucial post-transcriptional regulators of gene expression in metazoans and plants, targeting up to 50% of the proteincoding genes [1,2]. Most of the research over the past decade has concentrated on elucidating the mechanisms of miRNA-mediated post-transcriptional regulation in cancer and other diseases, and on the potential clinical applications of this knowledge [1,3]. It is still poorly understood how miRNAs themselves are regulated. Recent studies indicate that several alternative miRNA biogenesis pathways exist, for instance the one giving rise to splicing-derived miRNAs, called mirtrons [11] Despite this complexity, miRNA TSS identification is a crucial step in understanding miRNA regulation, locating the core promoters, and searching for putative transcription factor binding sites (TFBSs)
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