Submit Manuscript  

Article Details


Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions

Author(s):

Xiao-Fei Yang, Yang Gao* and Pu-Feng Du*  

Abstract:


Background: Long non-coding RNAs (lncRNAs) are transcripts with a length more than 200 nucleotides, functioning in the regulation of gene expression. More and more evidence has shown that the biological functions of lncRNAs are intimately related to their subcellular localizations. Therefore, it is very important to confirm the lncRNA subcellular localization.

Method: In this paper, we proposed a novel method to predict the subcellular localization of lncRNAs. To more comprehensively utilize lncRNA sequence information, we exploited both k-mer nucleotide composition and sequence order correlated factors of lncRNA to formulate lncRNA sequences. Meanwhile, a feature selection technique which was based on the analysis of variance (ANOVA) was applied to obtain the optimal feature subset. Finally, we used the support vector machine (SVM) to perform the prediction.

Results: The AUC value of the proposed method can reach 0.9695, which indicated the proposed predictor is an efficient and reliable tool for determining lncRNA subcellular localization. Furthermore, the predictor can reach the maximum overall accuracy of 90.37% in leave-one-out cross validation, which clearly outperforms the existing state-of- the-art method.

Conclusion: It is demonstrated that the proposed predictor is feasible and powerful for the prediction of lncRNA subcellular. To facilitate subsequent genetic sequence research, we shared the source code at https://github.com/NicoleYXF/lncRNA.

Keywords:

Long non-coding RNA, subcellular localization, sequence order correlated factors, feature selection, analysis of variance, support vector machine

Affiliation:

College of Intelligence and Computing, Tianjin University, Tianjin 300350, School of Medicine, Nankai University, Tianjin 300071, College of Intelligence and Computing, Tianjin University, Tianjin 300350



Full Text Inquiry