Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 bases. They have important functions in cell development and metabolism, including genetic markers, genome rearrangements, chromatin modifications, cell cycle regulation, transcription and Translation, etc. Generally, their functions are also closely related to their location in a cell. The aberrant expression of lncRNAs is associated with several types of cancer, Alzheimer's disease, etc. The study on their localization could provide preliminary insight into their cellular functions. In this work, we designed a sequence-based predictor called “iLoc-LncRNA” to predict the subcellular locations of LncRNAs. In the predictor, a high-quality benchmark dataset including four locations was constructed based on the RNALocate database. The key octonucleotide (8-tuple) features were incorporated into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach to formulate lncRNA samples. The support vector machine (SVM) was adopted to perform discrimination. In rigorous 5-fold cross-validations, we achieved the maximum overall accuracy of 86.72%, suggesting that the proposed predictor is promising and will provide important guidance in this area.
