A computational approach for prediction of donor splice sites with improved accuracy.
Identification of splice sites is important due to their key role in predicting the exon-intron structure of protein coding genes. Though several approaches have been developed for the prediction of splice sites, further improvement in the prediction accuracy will help predict gene structure more accurately. This paper presents a computational approach for prediction of donor splice sites with higher accuracy. In this approach, true and false splice sites were first encoded into numeric vectors and then used as input in artificial neural network (ANN), support vector machine (SVM) and random forest (RF) for prediction. ANN and SVM were found to perform equally and better than RF, while tested on HS3D and NN269 datasets. Further, the performance of ANN, SVM and RF were analyzed by using an independent test set of 50 genes and found that the prediction accuracy of ANN was higher than that of SVM and RF. All the predictors achieved higher accuracy while compared with the existing methods like NNsplice, MEM, MDD, WMM, MM1, FSPLICE, GeneID and ASSP, using the independent test set. We have also developed an online prediction server (PreDOSS) available at http://cabgrid.res.in:8080/predoss, for prediction of donor splice sites using the proposed approach.
ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India. Electronic address: meherprabin@yahoo.com.
Prabina Kumar
PK
Meher
ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India. Electronic address: tanmayabioinfo@gmail.com.
Tanmaya Kumar
TK
Sahu
ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India. Electronic address: rao.cshl.work@gmail.com.
A R
AR
Rao
ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India. Electronic address: sdwahi@iasri.res.in.
S D
SD
Wahi
D019540:Area Under Curve; D001483:Base Sequence; D016009:Chi-Square Distribution; D019295:Computational Biology / Q000379:methods*; D030561:Databases, Nucleic Acid; D006801:Humans; D020407:Internet; D016571:Neural Networks, Computer; D059372:Nucleotide Motifs / Q000235:genetics; D022821:RNA Splice Sites / Q000235:genetics*; D012372:ROC Curve; D012689:Sequence Homology, Nucleic Acid; D060388:Support Vector Machine
D022821:RNA Splice Sites
Conditional error; Di-nucleotide dependency; Machine learning; PreDOSS; Sequence encoding