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PROmiRNA: a new miRNA promoter recognition method uncovers the complex regulation of intronic miRNAs

Overview of attention for article published in Genome Biology, August 2013
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Mentioned by

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3 X users

Citations

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104 Dimensions

Readers on

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238 Mendeley
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6 CiteULike
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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

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 238 Mendeley readers of this research output. Click here to see the associated Mendeley record.

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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 November 2014.
All research outputs
#16,721,717
of 25,374,647 outputs
Outputs from Genome Biology
#4,055
of 4,467 outputs
Outputs of similar age
#118,439
of 194,415 outputs
Outputs of similar age from Genome Biology
#42
of 50 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 194,415 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.