UMMS Affiliation

Program in Bioinformatics and Integrative Biology

Date

4-13-2016

Document Type

Article

Disciplines

Bioinformatics | Computational Biology

Abstract

BACKGROUND: Piwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete.

METHODS: In this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods. We explore six sequence-derived features, i.e. spectrum profile, mismatch profile, subsequence profile, position-specific scoring matrix, pseudo dinucleotide composition and local structure-sequence triplet elements, and systematically evaluate their performances for transposon-derived piRNA prediction. Finally, we consider two approaches: direct combination and ensemble learning to integrate useful features and achieve high-accuracy prediction models.

RESULTS: We construct three datasets, covering three species: Human, Mouse and Drosophila, and evaluate the performances of prediction models by 10-fold cross validation. In the computational experiments, direct combination models achieve AUC of 0.917, 0.922 and 0.992 on Human, Mouse and Drosophila, respectively; ensemble learning models achieve AUC of 0.922, 0.926 and 0.994 on the three datasets.

CONCLUSIONS: Compared with other state-of-the-art methods, our methods can lead to better performances. In conclusion, the proposed methods are promising for the transposon-derived piRNA prediction. The source codes and datasets are available in S1 File.

Rights and Permissions

Citation: PLoS One. 2016 Apr 13;11(4):e0153268. doi: 10.1371/journal.pone.0153268. eCollection 2016. Link to article on publisher's site

DOI of Published Version

10.1371/journal.pone.0153268

Related Resources

Link to Article in PubMed

Journal Title

PloS one

PubMed ID

27074043

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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