Effective prediction of microRNAs in the face of class imbalance

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MicroRNA (miRNA) are short expressed genomic sequences which encode small RNA molecules that adopt a “hairpin” secondary structure. Computational prediction of miRNA is important, since miRNA are now believed to disrupt or otherwise control the expression of 60-90% of mammalian genes. Sequence-based de novo prediction of miRNA is made difficult due to the acute class imbalance: for each true miRNA within a genome, we expect 1000 pseudo-miRNA (i.e. genomic regions producing miRNA-like hairpin structures). Therefore, effective miRNA prediction systems must have extremely high specificity (i.e. the ability to reject pseudo-miRNA), while also retaining the ability to correctly detect true miRNA (i.e. recall). We have recently introduced the Species-specific miRNA Prediction (SMIRP) framework for training highly effective species-specific miRNA prediction systems. When applied to three popular miRNA prediction methods, we observe significant improvements in precision (i.e. the proportion of predictions expected to be true miRNA) while maintaining the same high recall rates observed by the original methods. We propose to extend our research in three key areas: 1) Existing miRNA prediction methods perform well on canonical pre-miRNA, but are not well-suited for high-throughput annotation of entire genomes. Therefore, new classification techniques will be developed which optimally differentiate between real and pseudo-miRNA sequences within predicted hairpin structures. This will include the development of novel methods to compute general-purpose information-rich DNA/RNA descriptors. In addition to miRNA prediction, these descriptors will benefit other nucleic acid classification problem domains. 2) With the increasing availability of transcriptomic data, there is a need and an opportunity to develop an integrated miRNA discovery pipeline that leverages both next-generation sequencing (NGS) read patterns and powerful sequence-based methods such as SMIRP. We will develop and apply advanced machine learning approaches to optimally combine NGS- and sequence-based approaches, improving our ability to discover novel miRNA of potential importance to human health. 3) Contributions will also be made in the broader field of machine learning in the presence of extreme class imbalance where many classic performance metrics, such as ROC curves, become inappropriate as they do not adequately reflect the impact of false positive predictions. To address this and other issues, we will develop novel performance metrics for cases of acute class imbalance. While these new metrics will find immediate application in the development of miRNA prediction tools, they will also be widely applicable to other problem domains within bioinformatics and beyond.