miPIE: NGS-based Prediction of miRNA Using Integrated Evidence View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

R. J. Peace, M. Sheikh Hassani, J. R. Green

ABSTRACT

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. More... »

PAGES

1548

Journal

TITLE

Scientific Reports

ISSUE

1

VOLUME

9

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-38107-z

DOI

http://dx.doi.org/10.1038/s41598-018-38107-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111979554

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30733467


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