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ImiRP: a computational approach to microRNA target site mutation

Overview of attention for article published in BMC Bioinformatics, April 2016
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Title
ImiRP: a computational approach to microRNA target site mutation
Published in
BMC Bioinformatics, April 2016
DOI 10.1186/s12859-016-1057-y
Pubmed ID
Authors

Bridget C. Ryan, Torben S. Werner, Perry L. Howard, Robert L. Chow

Abstract

MicroRNAs (miRNAs) are small ~22 nucleotide non-coding RNAs that function as post-transcriptional regulators of messenger RNA (mRNA) through base-pairing to 6-8 nucleotide long target sites, usually located within the mRNA 3' untranslated region. A common approach to validate and probe microRNA-mRNA interactions is to mutate predicted target sites within the mRNA and determine whether it affects miRNA-mediated activity. The introduction of miRNA target site mutations, however, is potentially problematic as it may generate new, "illegitimate sites" target sites for other miRNAs, which may affect the experimental outcome. While it is possible to manually generate and check single miRNA target site mutations, this process can be time consuming, and becomes particularly onerous and error prone when multiple sites are to be mutated simultaneously. We have developed a modular Java-based system called ImiRP (Illegitimate miRNA Predictor) to solve this problem and to facilitate miRNA target site mutagenesis. The ImiRP interface allows users to input a sequence of interest, specify the locations of multiple predicted target sites to mutate, and set parameters such as species, mutation strategy, and disallowed illegitimate target site types. As mutant sequences are generated, ImiRP utilizes the miRBase high confidence miRNA dataset to identify illegitimate target sites in each mutant sequence by comparing target site predictions between input and mutant sequences. ImiRP then assembles a final mutant sequence in which all specified target sites have been mutated. ImiRP is a mutation generator program that enables selective disruption of specified miRNA target sites while ensuring predicted target sites for other miRNAs are not inadvertently created. ImiRP supports mutagenesis of single and multiple miRNA target sites within a given sequence, including sites that overlap. This software will be particularly useful for studies looking at microRNA cooperativity, where mutagenesis of multiple microRNA target sites may be desired. The software is available at imirp.org and is available open source for download through GitHub ( https://github.com/imirp ).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 28%
Student > Bachelor 9 23%
Researcher 6 15%
Student > Master 5 13%
Professor > Associate Professor 3 8%
Other 3 8%
Unknown 2 5%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 28%
Agricultural and Biological Sciences 11 28%
Computer Science 3 8%
Social Sciences 3 8%
Engineering 2 5%
Other 6 15%
Unknown 3 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 29 April 2016.
All research outputs
#14,717,488
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#4,823
of 7,418 outputs
Outputs of similar age
#162,201
of 300,739 outputs
Outputs of similar age from BMC Bioinformatics
#64
of 100 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 30th percentile – i.e., 30% 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 300,739 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.