lncLocator: long non-coding RNA subcellular localization predictor


Motivation: The long non-coding RNA (lncRNA) studies have been hot topics in the field of RNA biology. Recent studies have shown that their subcellular localizations carry important information for understanding their complex biological functions. Considering the costly and time-consuming experiments for identifying subcellular localization of lncRNAs, computational methods are urgent-ly desired. However, to the best of our knowledge, there are no computational tools for predicting the lncRNA subcellular locations to date. Results: In this study, we report an ensemble classifier-based predictor, lncLocator, for predicting the lncRNA subcellular localizations. To fully exploit lncRNA sequence information, we adopt both k-mer features and high-level abstraction features generated by unsupervised deep models, and construct four classifiers by feeding these two types of features to support vector machine (SVM) and random forest (RF), respectively. Then we use a stacked ensemble strategy to combine the four classifiers and get the final prediction results. The current lncLocator can predict 5 subcellular localizations of lncRNAs, including cytoplasm, nucleus, cytosol, ribosome and exosome, and yield an overall accuracy of 0.61 on the constructed benchmark dataset.
Availability: The lncLocator is available at www.csbio.sjtu.edu.cn/bioinf/lncLocator.

Figure 1. The flowchart of proposed lncLocator  

Figure 2. The oversampling method of proposed lncLocator  

© 2017 Computational Systems Biology/Shen Group.