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mrSNP: Software to detect SNP effects on microRNA binding

Overview of attention for article published in BMC Bioinformatics, March 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

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11 X users

Citations

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47 Dimensions

Readers on

mendeley
82 Mendeley
citeulike
5 CiteULike
Title
mrSNP: Software to detect SNP effects on microRNA binding
Published in
BMC Bioinformatics, March 2014
DOI 10.1186/1471-2105-15-73
Pubmed ID
Authors

Mehmet Deveci, Ümit V Çatalyürek, Amanda Ewart Toland

Abstract

MicroRNAs (miRNAs) are short (19-23 nucleotides) non-coding RNAs that bind to sites in the 3'untranslated regions (3'UTR) of a targeted messenger RNA (mRNA). Binding leads to degradation of the transcript or blocked translation resulting in decreased expression of the targeted gene. Single nucleotide polymorphisms (SNPs) have been found in 3'UTRs that disrupt normal miRNA binding or introduce new binding sites and some of these have been associated with disease pathogenesis. This raises the importance of detecting miRNA targets and predicting the possible effects of SNPs on binding sites. In the last decade a number of studies have been conducted to predict the location of miRNA binding sites. However, there have been fewer algorithms published to analyze the effects of SNPs on miRNA binding. Moreover, the existing software has some shortcomings including the requirement for significant manual labor when working with huge lists of SNPs and that algorithms work only for SNPs present in databases such as dbSNP. These limitations become problematic as next-generation sequencing is leading to large numbers of novel variants in 3'UTRs.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
Netherlands 2 2%
Chile 1 1%
France 1 1%
Sweden 1 1%
United States 1 1%
Unknown 74 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 24%
Researcher 17 21%
Student > Doctoral Student 9 11%
Student > Master 6 7%
Student > Bachelor 6 7%
Other 18 22%
Unknown 6 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 33%
Biochemistry, Genetics and Molecular Biology 21 26%
Computer Science 9 11%
Medicine and Dentistry 5 6%
Engineering 4 5%
Other 6 7%
Unknown 10 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 May 2014.
All research outputs
#6,211,224
of 24,666,614 outputs
Outputs from BMC Bioinformatics
#2,151
of 7,565 outputs
Outputs of similar age
#54,071
of 226,412 outputs
Outputs of similar age from BMC Bioinformatics
#30
of 100 outputs
Altmetric has tracked 24,666,614 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,565 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 71% of its peers.
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 226,412 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
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 has gotten more attention than average, scoring higher than 71% of its contemporaries.