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

BioData Mining
Prabina Kumar MeherAtmakuri Ramakrishna Rao

Abstract

Detection 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. The 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. Based 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 comput...Continue Reading

References

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Citations

Jul 23, 2020·Journal of Bioinformatics and Computational Biology·Santhosh Amilpur, Raju Bhukya
Jul 27, 2020·PeerJ·Thanyathorn ThanapattheerakulJonathan H Chan
Jan 12, 2021·Journal of Biomolecular Structure & Dynamics·Pedro F N SouzaCleverson D T Freitas
Feb 17, 2021·BioData Mining·Shabir MoosaDr Sabri Boughorbel
Aug 31, 2021·Nucleosides, Nucleotides & Nucleic Acids·Lopamudra DasSarita Nanda

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Methods Mentioned

BETA
glycosylation
protein folding

Software Mentioned

RSNNS
klaR
MDD
stats
class
packages
e1071
Genesplicer
MaLDoSS
R

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