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LimiTT: link miRNAs to targets

Overview of attention for article published in BMC Bioinformatics, May 2016
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Mentioned by

3 tweeters

Readers on

32 Mendeley
4 CiteULike
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LimiTT: link miRNAs to targets
Published in
BMC Bioinformatics, May 2016
DOI 10.1186/s12859-016-1070-1
Pubmed ID

Julia Bayer, Carsten Kuenne, Jens Preussner, Mario Looso


MicroRNAs (miRNAs) impact various biological processes within animals and plants. They complementarily bind target mRNAs, effecting a post-transcriptional negative regulation on mRNA level. The investigation of miRNA target interactions (MTIs) by high throughput screenings is challenging, as frequently used in silico target prediction tools are prone to emit false positives. This issue is aggravated for niche model organisms, where validated miRNAs and MTIs both have to be transferred from well described model organisms. Even though DBs exist that contain experimentally validated MTIs, they are limited in their search options and they utilize different miRNA and target identifiers. The implemented pipeline LimiTT integrates four existing DBs containing experimentally validated MTIs. In contrast to other cumulative databases (DBs), LimiTT includes MTI data of 26 species. Additionally, the pipeline enables the identification and enrichment analysis of MTIs with and without species specificity based on dynamic quality criteria. Multiple tabular and graphical outputs are generated to permit the detailed assessment of results. Our freely available web-based pipeline LimiTT ( https://bioinformatics.mpi-bn.mpg.de/ ) is optimized to determine MTIs with and without species specification. It links miRNAs and/or putative targets with high granularity. The integrated mapping to homologous target identifiers enables the identification of MTIs not only for standard models, but for niche model organisms as well.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 6%
Unknown 30 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 25%
Student > Bachelor 6 19%
Student > Ph. D. Student 6 19%
Student > Master 4 13%
Professor > Associate Professor 3 9%
Other 4 13%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 38%
Computer Science 6 19%
Nursing and Health Professions 3 9%
Engineering 3 9%
Biochemistry, Genetics and Molecular Biology 3 9%
Other 5 16%

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 18 May 2016.
All research outputs
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Outputs from BMC Bioinformatics
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Outputs of similar age
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Outputs of similar age from BMC Bioinformatics
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Altmetric has tracked 17,800,904 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,267 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.