miPIE: NGS-based Prediction of miRNA Using Integrated Evidence

Methods for the de novo identification of microRNA (miRNA) have been developed using a range of sequence-based features. With the increasing availability of next generation sequencing (NGS) transcriptome data, there is a need for miRNA identification that integrates both NGS transcript expression-ba...

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Bibliographic Details
Published in:Scientific reports 2019-02, Vol.9 (1), p.1548-1548, Article 1548
Main Authors: Peace, R. J., Sheikh Hassani, M., Green, J. R.
Format: Article
Language:English
Subjects:
45
45/91
631/114/1305
631/114/794
Biosynthesis
Gene expression
Humanities and Social Sciences
MicroRNAs
miRNA
multidisciplinary
Next-generation sequencing
Protein structure
Science
Science (multidisciplinary)
Secondary structure
Species diversity
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Summary:Methods for the de novo identification of microRNA (miRNA) have been developed using a range of sequence-based features. With the increasing availability of next generation sequencing (NGS) transcriptome data, there is a need for miRNA identification that integrates both NGS transcript expression-based patterns as well as advanced genomic sequence-based methods. While miRDeep2 does examine the predicted secondary structure of putative miRNA sequences, it does not leverage many of the sequence-based features used in state-of-the-art de novo methods. Meanwhile, other NGS-based methods, such as miRanalyzer, place an emphasis on sequence-based features without leveraging advanced expression-based features reflecting miRNA biosynthesis. This represents an opportunity to combine the strengths of NGS-based analysis with recent advances in de novo sequence-based miRNA prediction. We here develop a method, microRNA Prediction using Integrated Evidence (miPIE), which integrates both expression-based and sequence-based features to achieve significantly improved miRNA prediction performance. Feature selection identifies the 20 most discriminative features, 3 of which reflect strictly expression-based information. Evaluation using precision-recall curves, for six NGS data sets representing six diverse species, demonstrates substantial improvements in prediction performance compared to three methods: miRDeep2, miRanalyzer, and mirnovo. The individual contributions of expression-based and sequence-based features are also examined and we demonstrate that their combination is more effective than either alone.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-38107-z