Title |
PROmiRNA: a new miRNA promoter recognition method uncovers the complex regulation of intronic miRNAs
|
---|---|
Published in |
Genome Biology, August 2013
|
DOI | 10.1186/gb-2013-14-8-r84 |
Pubmed ID | |
Authors |
Annalisa Marsico, Matthew R Huska, Julia Lasserre, Haiyang Hu, Dubravka Vucicevic, Anne Musahl, Ulf Andersson Orom, Martin Vingron |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 33% |
Luxembourg | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 1% |
Germany | 2 | <1% |
Netherlands | 1 | <1% |
Norway | 1 | <1% |
Italy | 1 | <1% |
Czechia | 1 | <1% |
Brazil | 1 | <1% |
Denmark | 1 | <1% |
Japan | 1 | <1% |
Other | 3 | 1% |
Unknown | 223 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 74 | 31% |
Researcher | 41 | 17% |
Student > Master | 33 | 14% |
Student > Bachelor | 18 | 8% |
Student > Doctoral Student | 18 | 8% |
Other | 28 | 12% |
Unknown | 26 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 95 | 40% |
Biochemistry, Genetics and Molecular Biology | 61 | 26% |
Computer Science | 17 | 7% |
Medicine and Dentistry | 13 | 5% |
Neuroscience | 6 | 3% |
Other | 11 | 5% |
Unknown | 35 | 15% |