Evidence map›Paper›PMID 25547961›Full record

ArticleBMC bioinformatics2014

Prediction of piRNAs using transposon interaction and a support vector machine.

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

Abstract read
In one paragraph

Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Review
  6. Frontiers in cellular and infection microbiology · 2023
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Characterization of microRNA Profiles inFrontiers in veterinary science · 2021
    Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kai WangDepartment of Entomology, College of Plant Protection, Nanjing Agricultural University, Nanjing, 210095, China. wangk4@miamioh.edu.
Chun LiangDepartment of Biology, Miami University, Oxford, OH, 45056, USA. liangc@miamioh.edu.
Jinding LiuDepartment of Entomology, College of Plant Protection, Nanjing Agricultural University, Nanjing, 210095, China. liujd@njau.edu.cn.
Huamei XiaoDepartment of Entomology, College of Plant Protection, Nanjing Agricultural University, Nanjing, 210095, China. xiaohuamei625@163.com.
Shuiqing HuangCollege of Information and Technology, Nanjing Agricultural University, Nanjing, 210095, China. sqhuang@njau.edu.cn.
Jianhua XuCollege of computer science and Technology, Nanjing Normal University, Nanjing, 210023, China. xujianghua@njnu.edu.cn.
Fei LiDepartment of Entomology, College of Plant Protection, Nanjing Agricultural University, Nanjing, 210095, China. lifei03@tsinghua.org.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 .

Indexed as

Support Vector MachineAnimalsDrosophila melanogasterDrosophila ProteinsHumansMiceMothsOryzaRatsRNA, Small InterferingUser-Computer InterfaceDrosophila ProteinsRNA, Small Interfering

Identifiers

PMID25547961
PMCPMC4308892

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.