↓ Skip to main content

Prediction of piRNAs using transposon interaction and a support vector machine

Overview of attention for article published in BMC Bioinformatics, December 2014
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

twitter
8 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
66 Dimensions

Readers on

mendeley
81 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Prediction of piRNAs using transposon interaction and a support vector machine
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0419-6
Pubmed ID
Authors

Kai Wang, Chun Liang, Jinding Liu, Huamei Xiao, Shuiqing Huang, Jianhua Xu, Fei Li

Abstract

BackgroundPiwi-interacting RNAs (piRNAs) are a class of small non-coding RNA primarily expressed in germ cells that can silence transposons at the post-transcriptional level. Accurate prediction of piRNAs remains a significant challenge.ResultsWe developed a program for piRNA annotation (Piano) using piRNA-transposon interaction information. We downloaded 13,848 Drosophila piRNAs and 261,500 Drosophila transposons. The piRNAs were aligned to transposons with a maximum of three mismatches. Then, piRNA-transposon interactions were predicted by RNAplex. Triplet elements combining structure and sequence information were extracted from piRNA-transposon matching/pairing duplexes. A support vector machine (SVM) was used on these triplet elements to classify real and pseudo piRNAs, achieving 95.3¿±¿0.33% accuracy and 96.0¿±¿0.5% sensitivity. The SVM classifier can be used to correctly predict human, mouse and rat piRNAs, with overall accuracy of 90.6%. We used Piano to predict piRNAs for the rice stem borer, Chilo suppressalis, an important rice insect pest that causes huge yield loss. As a result, 82,639 piRNAs were predicted in C. suppressalis.ConclusionsPiano demonstrates excellent piRNA prediction performance by using both structure and sequence features of transposon-piRNAs interactions. Piano is freely available to the academic community at http://ento.njau.edu.cn/Piano.html.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 1%
Sweden 1 1%
Unknown 79 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 22%
Researcher 14 17%
Student > Master 13 16%
Student > Bachelor 5 6%
Professor 4 5%
Other 12 15%
Unknown 15 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 32%
Biochemistry, Genetics and Molecular Biology 19 23%
Computer Science 8 10%
Medicine and Dentistry 4 5%
Neuroscience 2 2%
Other 3 4%
Unknown 19 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 15 September 2015.
All research outputs
#6,305,377
of 12,378,406 outputs
Outputs from BMC Bioinformatics
#2,160
of 4,542 outputs
Outputs of similar age
#90,196
of 267,235 outputs
Outputs of similar age from BMC Bioinformatics
#60
of 162 outputs
Altmetric has tracked 12,378,406 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,542 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 51% 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 267,235 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 162 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 61% of its contemporaries.