miPIE: NGS-based Prediction of miRNA Using Integrated Evidence

• 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; Transcription
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-38107-z

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.