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Prediction of mature microRNA and piwi-interacting RNA without a genome reference or precursors.

Menor MS, Baek K, Poisson G - Int J Mol Sci (2015)

Bottom Line: In this study, we propose an approach that relies primarily on the nucleotide composition of the read and does not require reference genomes of related species for prediction.We found that the usage of an L1-based Gaussian kernel can double the true positive rate compared to the standard L2-based Gaussian kernel.Our approach can increase the true positive rate by at most 60% compared to the existing piRNA predictor based on the analysis of a hold-out test set.

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Computer Sciences, University of Hawaii at Mānoa, 1680 East-West Road, Honolulu, HI 96822, USA. mmenor@hawaii.edu.

ABSTRACT
The discovery of novel microRNA (miRNA) and piwi-interacting RNA (piRNA) is an important task for the understanding of many biological processes. Most of the available miRNA and piRNA identification methods are dependent on the availability of the organism's genome sequence and the quality of its annotation. Therefore, an efficient prediction method based solely on the short RNA reads and requiring no genomic information is highly desirable. In this study, we propose an approach that relies primarily on the nucleotide composition of the read and does not require reference genomes of related species for prediction. Using an empirical Bayesian kernel method and the error correcting output codes framework, compact models suitable for large-scale analyses are built on databases of known mature miRNAs and piRNAs. We found that the usage of an L1-based Gaussian kernel can double the true positive rate compared to the standard L2-based Gaussian kernel. Our approach can increase the true positive rate by at most 60% compared to the existing piRNA predictor based on the analysis of a hold-out test set. Using experimental data, we also show that our approach can detect about an order of magnitude or more known miRNAs than the mature miRNA predictor, miRPlex.

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Test set ROC curves for McRUMs and SVM using the L1 Gaussian kernel. McRUMs use all-pairs (AP) or one vs. rest (OVR) decompositions. The ROC curves are generated from observed FPR and TPR under varying posterior probability thresholds from 0.30 to 0.99 in increments of 0.01; (a) Results for microRNAs; (b) Results for piwi-interacting RNAs; (c) Results for other types of small RNAs.
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ijms-16-01466-f003: Test set ROC curves for McRUMs and SVM using the L1 Gaussian kernel. McRUMs use all-pairs (AP) or one vs. rest (OVR) decompositions. The ROC curves are generated from observed FPR and TPR under varying posterior probability thresholds from 0.30 to 0.99 in increments of 0.01; (a) Results for microRNAs; (b) Results for piwi-interacting RNAs; (c) Results for other types of small RNAs.

Mentions: The predictive performance results of McRUMs and SVM are given in the ROC curves in Figure 3, which shows no clear superior method. Performance is nearly identical for miRNAs (Figure 3a). The SVM has a small advantage for piRNAs (Figure 3b), and the OVR McRUM has the advantage for other ncRNAs. The primary advantage of using McRUM models over the SVM model is the smaller models generated by McRUM, as illustrated in Figure 4. The SVM and both McRUM models are composed of three binary classification models. On average, a binary classification model of the SVM uses about six-times more terms (support vectors) in its linear model than McRUMs. Therefore, McRUM models are able to produce predictions at six-times the rate of the SVM, leading to faster RNA-seq analysis run-times.


Prediction of mature microRNA and piwi-interacting RNA without a genome reference or precursors.

Menor MS, Baek K, Poisson G - Int J Mol Sci (2015)

Test set ROC curves for McRUMs and SVM using the L1 Gaussian kernel. McRUMs use all-pairs (AP) or one vs. rest (OVR) decompositions. The ROC curves are generated from observed FPR and TPR under varying posterior probability thresholds from 0.30 to 0.99 in increments of 0.01; (a) Results for microRNAs; (b) Results for piwi-interacting RNAs; (c) Results for other types of small RNAs.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4307313&req=5

ijms-16-01466-f003: Test set ROC curves for McRUMs and SVM using the L1 Gaussian kernel. McRUMs use all-pairs (AP) or one vs. rest (OVR) decompositions. The ROC curves are generated from observed FPR and TPR under varying posterior probability thresholds from 0.30 to 0.99 in increments of 0.01; (a) Results for microRNAs; (b) Results for piwi-interacting RNAs; (c) Results for other types of small RNAs.
Mentions: The predictive performance results of McRUMs and SVM are given in the ROC curves in Figure 3, which shows no clear superior method. Performance is nearly identical for miRNAs (Figure 3a). The SVM has a small advantage for piRNAs (Figure 3b), and the OVR McRUM has the advantage for other ncRNAs. The primary advantage of using McRUM models over the SVM model is the smaller models generated by McRUM, as illustrated in Figure 4. The SVM and both McRUM models are composed of three binary classification models. On average, a binary classification model of the SVM uses about six-times more terms (support vectors) in its linear model than McRUMs. Therefore, McRUM models are able to produce predictions at six-times the rate of the SVM, leading to faster RNA-seq analysis run-times.

Bottom Line: In this study, we propose an approach that relies primarily on the nucleotide composition of the read and does not require reference genomes of related species for prediction.We found that the usage of an L1-based Gaussian kernel can double the true positive rate compared to the standard L2-based Gaussian kernel.Our approach can increase the true positive rate by at most 60% compared to the existing piRNA predictor based on the analysis of a hold-out test set.

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Computer Sciences, University of Hawaii at Mānoa, 1680 East-West Road, Honolulu, HI 96822, USA. mmenor@hawaii.edu.

ABSTRACT
The discovery of novel microRNA (miRNA) and piwi-interacting RNA (piRNA) is an important task for the understanding of many biological processes. Most of the available miRNA and piRNA identification methods are dependent on the availability of the organism's genome sequence and the quality of its annotation. Therefore, an efficient prediction method based solely on the short RNA reads and requiring no genomic information is highly desirable. In this study, we propose an approach that relies primarily on the nucleotide composition of the read and does not require reference genomes of related species for prediction. Using an empirical Bayesian kernel method and the error correcting output codes framework, compact models suitable for large-scale analyses are built on databases of known mature miRNAs and piRNAs. We found that the usage of an L1-based Gaussian kernel can double the true positive rate compared to the standard L2-based Gaussian kernel. Our approach can increase the true positive rate by at most 60% compared to the existing piRNA predictor based on the analysis of a hold-out test set. Using experimental data, we also show that our approach can detect about an order of magnitude or more known miRNAs than the mature miRNA predictor, miRPlex.

Show MeSH