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Nucleic Acids Res. 2015 Nov 16;43(20):e138. doi: 10.1093/nar/gkv698. Epub 2015 Jul 10.

A framework for improving microRNA prediction in non-human genomes.

Author information

1
Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada.
2
Institute of Biochemistry and Department of Biology, Carleton University, Ottawa, Canada Department of Biochemistry, University of Western Ontario, London, Canada.
3
Institute of Biochemistry and Department of Biology, Carleton University, Ottawa, Canada.
4
Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada jrgreen@sce.carleton.ca.

Abstract

The prediction of novel pre-microRNA (miRNA) from genomic sequence has received considerable attention recently. However, the majority of studies have focused on the human genome. Previous studies have demonstrated that sensitivity (correctly detecting true miRNA) is sustained when human-trained methods are applied to other species, however they have failed to report the dramatic drop in specificity (the ability to correctly reject non-miRNA sequences) in non-human genomes. Considering the ratio of true miRNA sequences to pseudo-miRNA sequences is on the order of 1:1000, such low specificity prevents the application of most existing tools to non-human genomes, as the number of false positives overwhelms the true predictions. We here introduce a framework (SMIRP) for creating species-specific miRNA prediction systems, leveraging sequence conservation and phylogenetic distance information. Substantial improvements in specificity and precision are obtained for four non-human test species when our framework is applied to three different prediction systems representing two types of classifiers (support vector machine and Random Forest), based on three different feature sets, with both human-specific and taxon-wide training data. The SMIRP framework is potentially applicable to all miRNA prediction systems and we expect substantial improvement in precision and specificity, while sustaining sensitivity, independent of the machine learning technique chosen.

PMID:
26163062
PMCID:
PMC4787757
DOI:
10.1093/nar/gkv698
[Indexed for MEDLINE]
Free PMC Article

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