Hybrid Approach Using SVM and MM2 in Splice Site Junction Identification

Maji, Srabanti; Garg, Deepak

doi:10.2174/1574893608999140109121721

Cited by 19 publications

(7 citation statements)

References 55 publications

(52 reference statements)

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“…Most of them exploit machine learning (ML) algorithms and use several features to describe SS, covering the consensus motifs or other nucleotides in proximity to the SS [29]. The most widely used ML algorithms include Support Vector Machines [30][31][32], Markov models [33,34], Random Forest [35,36] and Bayesian networks [37]. However, these methods are limited by the lack of knowledge about the input sequence (patterns, secondary structures, etc.…”

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confidence: 99%

Spliceator: multi-species splice site prediction using convolutional neural networks

et al. 2021

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Background Ab initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, Deep Learning methods for non-model organisms are lacking. Results We developed Spliceator to predict splice sites in a wide range of species, including model and non-model organisms. Spliceator uses a convolutional neural network and is trained on carefully validated data from over 100 organisms. We show that Spliceator achieves consistently high accuracy (89–92%) compared to existing methods on independent benchmarks from human, fish, fly, worm, plant and protist organisms. Conclusions Spliceator is a new Deep Learning method trained on high-quality data, which can be used to predict splice sites in diverse organisms, ranging from human to protists, with consistently high accuracy.

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confidence: 99%

Spliceator: multi-species splice site prediction using convolutional neural networks

et al. 2021

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“…29 global and intrinsic folding features were extracted from secondary structures of real/pseudo pre-miRNAs defined in miPred. These features include the following: (i) %G + C content and 16 dinucleotide frequencies defined as %XY, where X, Y in {A, C, G, U}; (ii) adjusted base pairing propensity denoted as dP [ 20 ]; (iii) the MFE of folding denoted as dG [ 21 ]; (iv) the adjusted base pair distance denoted as dD [ 22 ]; (v) the adjusted Shannon entropy denoted as dQ [ 23 ]; (vi) the MFE index denoted as MFEI1 and MFEI2 [ 24 ], a topological descriptor of the degree of compactness denoted as dF; and (vii) 5 normalized variants of dP, dG, dQ, dD and dF denoted as zP, zG, zQ, zD and zF, respectively [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine

et al. 2014

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BackgroundMicroRNAs (miRNAs) are a family of non-coding RNAs approximately 21 nucleotides in length that play pivotal roles at the post-transcriptional level in animals, plants and viruses. These molecules silence their target genes by degrading transcription or suppressing translation. Studies have shown that miRNAs are involved in biological responses to a variety of biotic and abiotic stresses. Identification of these molecules and their targets can aid the understanding of regulatory processes. Recently, prediction methods based on machine learning have been widely used for miRNA prediction. However, most of these methods were designed for mammalian miRNA prediction, and few are available for predicting miRNAs in the pre-miRNAs of specific plant species. Although the complete Solanum lycopersicum genome has been published, only 77 Solanum lycopersicum miRNAs have been identified, far less than the estimated number. Therefore, it is essential to develop a prediction method based on machine learning to identify new plant miRNAs.ResultsA novel classification model based on a support vector machine (SVM) was trained to identify real and pseudo plant pre-miRNAs together with their miRNAs. An initial set of 152 novel features related to sequential structures was used to train the model. By applying feature selection, we obtained the best subset of 47 features for use with the Back Support Vector Machine-Recursive Feature Elimination (B-SVM-RFE) method for the classification of plant pre-miRNAs. Using this method, 63 features were obtained for plant miRNA classification. We then developed an integrated classification model, miPlantPreMat, which comprises MiPlantPre and MiPlantMat, to identify plant pre-miRNAs and their miRNAs. This model achieved approximately 90% accuracy using plant datasets from nine plant species, including Arabidopsis thaliana, Glycine max, Oryza sativa, Physcomitrella patens, Medicago truncatula, Sorghum bicolor, Arabidopsis lyrata, Zea mays and Solanum lycopersicum. Using miPlantPreMat, 522 Solanum lycopersicum miRNAs were identified in the Solanum lycopersicum genome sequence.ConclusionsWe developed an integrated classification model, miPlantPreMat, based on structure-sequence features and SVM. MiPlantPreMat was used to identify both plant pre-miRNAs and the corresponding mature miRNAs. An improved feature selection method was proposed, resulting in high classification accuracy, sensitivity and specificity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0423-x) contains supplementary material, which is available to authorized users.

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“…Although relatively high accuracy has been achieved with the methods currently available (e.g., the accuracy for most donor splice site prediction based on the HS 3 D dataset has exceeded 90% [6, 10, 12, 13, 19, 24, 31]), further study is still necessary due to the following factors: 1) Determining a suitable window size prior to the application of any prediction method is essential [32]. Overly long window size may introduce some irrelevant features that would reduce predictive accuracy, and may take more computational time and memory space.…”

Section: Introductionmentioning

confidence: 99%

A high-performance approach for predicting donor splice sites based on short window size and imbalanced large samples

Zeng

Yuan

et al. 2019

Biol Direct

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Background Splice sites prediction has been a long-standing problem in bioinformatics. Although many computational approaches developed for splice site prediction have achieved satisfactory accuracy, further improvement in predictive accuracy is significant, for it is contributing to predict gene structure more accurately. Determining a proper window size before prediction is necessary. Overly long window size may introduce some irrelevant features, which would reduce predictive accuracy, while the use of short window size with maximum information may performs better in terms of predictive accuracy and time cost. Furthermore, the number of false splice sites following the GT–AG rule far exceeds that of true splice sites, accurate and rapid prediction of splice sites using imbalanced large samples has always been a challenge. Therefore, based on the short window size and imbalanced large samples, we developed a new computational method named chi-square decision table (χ 2 -DT) for donor splice site prediction. Results Using a short window size of 11 bp, χ 2 -DT extracts the improved positional features and compositional features based on chi-square test, then introduces features one by one based on information gain, and constructs a balanced decision table aimed at implementing imbalanced pattern classification. With a 2000:271,132 (true sites:false sites) training set, χ 2 -DT achieves the highest independent test accuracy (93.34%) when compared with three classifiers (random forest, artificial neural network, and relaxed variable kernel density estimator) and takes a short computation time (89 s). χ 2 -DT also exhibits good independent test accuracy (92.40%), when validated with BG-570 mutated sequences with frameshift errors (nucleotide insertions and deletions). Moreover, χ 2 -DT is compared with the long-window size-based methods and the short-window size-based methods, and is found to perform better than all of them in terms of predictive accuracy. Conclusions Based on short window size and imbalanced large samples, the proposed method not only achieves higher predictive accuracy than some existing methods, but also has high computational speed and good robustness against nucleotide insertions and deletions. Reviewers This article was reviewed by Ryan McGinty, Ph.D. and Dirk Walther. Electronic supplementary material The online version of this article (10.1186/s13062-019-0236-y) contains supplementary material, which is available to authorized users.

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Hybrid Approach Using SVM and MM2 in Splice Site Junction Identification

Cited by 19 publications

References 55 publications

Spliceator: multi-species splice site prediction using convolutional neural networks

Spliceator: multi-species splice site prediction using convolutional neural networks

Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine

A high-performance approach for predicting donor splice sites based on short window size and imbalanced large samples

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