Methods For Splice Site Prediction Research Articles

Evaluating the Accuracy of Splice Site Prediction based on Integrating Jensen-Shannon Divergence and a Polynomial Equation of Order 2

Advances in DNA sequencing technology have caused generation of the vast amount of new sequence data. It is essential to understand the functions, features, and structures of every newly sequenced data. Analyzing sequence data by different methods could provide important information about the sequence data. One of the essential tasks for genome annotation is gene prediction that can help to understand the features and determine functions of the genes. One of the key steps towards correct gene structure prediction is accurate splice site detection. There are vast numbers of splice site prediction methods, however, a few of them can be incorporated in gene prediction modules because of their complexity. In this paper, a novel model is presented to recognize unknown splice sites in a new genome without using any prior knowledge. Our model is defined based on integrating Jensen-Shannon divergence and a polynomial equation of order 2. Finally, the proposed model is evaluated on Yeast’s genome to predict splice sites. The experimental results suggest that the proposed method is an effective approach for splice site prediction. General Terms Bioinformatics, Gene structure

A novel method for splice sites prediction using sequence component and hidden Markov model.

With increasing growth of DNA sequence data, it has become an urgent demand to develop new methods to accurately predict the genes. The performance of gene detection methods mainly depend on the efficiency of splice site prediction methods. In this paper, a novel method for detecting splice sites is proposed by using a new effective DNA encoding method and AdaBoost.M1 classifier. Our proposed DNA encoding method is based on multi-scale component (MSC) and first order Markov model (MM1). It has been applied to the HS3D dataset with repeated 10 fold cross validation. The experimental results indicate that the new method has increased the classification accuracy and outperformed some current methods such as MM1-SVM, Reduced MM1-SVM, SVM-B, LVMM, DM-SVM, DM2-AdaBoost and MS C+Pos(+APR)-SVM.

Prediction of donor splice sites using random forest with a new sequence encoding approach.

BackgroundDetection of splice sites plays a key role for predicting the gene structure and thus development of efficient analytical methods for splice site prediction is vital. This paper presents a novel sequence encoding approach based on the adjacent di-nucleotide dependencies in which the donor splice site motifs are encoded into numeric vectors. The encoded vectors are then used as input in Random Forest (RF), Support Vector Machines (SVM) and Artificial Neural Network (ANN), Bagging, Boosting, Logistic regression, kNN and Naïve Bayes classifiers for prediction of donor splice sites.ResultsThe performance of the proposed approach is evaluated on the donor splice site sequence data of Homo sapiens, collected from Homo Sapiens Splice Sites Dataset (HS3D). The results showed that RF outperformed all the considered classifiers. Besides, RF achieved higher prediction accuracy than the existing methods viz., MEM, MDD, WMM, MM1, NNSplice and SpliceView, while compared using an independent test dataset.ConclusionBased on the proposed approach, we have developed an online prediction server (MaLDoSS) to help the biological community in predicting the donor splice sites. The server is made freely available at http://cabgrid.res.in:8080/maldoss. Due to computational feasibility and high prediction accuracy, the proposed approach is believed to help in predicting the eukaryotic gene structure.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0086-4) contains supplementary material, which is available to authorized users.

An Improved Method for Splice Site Prediction in DNA Sequences Using Support Vector Machines

A vast amount of sequence data has been generated due to advancements in DNA sequencing technology. This exponential increase requires new and efficient methods for the analysis of DNA sequence data. Predicting genes in this newly sequenced data is an important and essential step towards genome annotation. Genome annotation helps in determining function of these genes. Accurate splice site prediction in DNA sequences leads to correct gene structure prediction in eukaryotes and it requires effective modelling of regions surrounding these sites. A large number of methods for splice site prediction are available in literature but very few of them are suitable to be incorporated as gene prediction module because of their complexity. In this paper, a splice site prediction method based on second order markov model and support vector machine is developed. This method shows improvement over most of the existing splice site predictors in use today. The experimental results suggest that second order markov model is an effective pre-processing approach. This approach when combined with support vector machine provides better classification accuracy in predicting splice sites.

Prediction of splice sites in plant pre-mRNA from sequence properties

Heterologous introns are often inaccurately or inefficiently processed in higher plants. The precise features that distinguish the process of pre-mRNA splicing in plants from splicing in yeast and mammals are unclear. One contributing factor is the prominent base compositional contrast between U-rich plant introns and flanking G+C-rich exons. Inclusion of this contrast factor in recently developed statistical methods for splice site prediction from sequence inspection significantly improved prediction accuracy. We applied the prediction tools to re-analyze experimental data on splice site selection and splicing efficiency for native and more than 170 mutated plant introns. In almost all cases, the experimentally determined preferred sites correspond to the highest scoring sites predicted by the model. In native genes, about 90% of splice sites are the locally highest scoring sites within the bounds of the flanking exon and intron. We propose that, in most cases, local context (about 50 bases upstream and downstream from a potential intron end) is sufficient to account for intrinsic splice site strength, and that competition for trans-acting factors determines splice site selection in vivo. We suggest that computer-aided splice site prediction can be a powerful tool for experimental design and interpretation.