Journal
BIOINFORMATICS
Volume 34, Issue 24, Pages 4196-4204Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty508
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Funding
- National Nature Scientific Foundation of China [61772119, 31771471]
- Fundamental Research Funds for the Central Universities of China [ZYGX2015Z006, ZYGX2016J125, ZYGX2016J118]
- Natural Science Foundation for Distinguished Young Scholar of Hebei Province [C2017209244]
- Program for the Top Young Innovative Talents of Higher Learning Institutions of Hebei Province [BJ2014028]
- Scientific Platform Improvement Project of UESTC
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Motivation: Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. They have important functions in cell development and metabolism, such as genetic markers, genome rearrangements, chromatin modifications, cell cycle regulation, transcription and translation. Their functions are generally closely related to their localization in the cell. Therefore, knowledge about their subcellular locations can provide very useful clues or preliminary insight into their biological functions. Although biochemical experiments could determine the localization of lncRNAs in a cell, they are both time-consuming and expensive. Therefore, it is highly desirable to develop bioinformatics tools for fast and effective identification of their subcellular locations. Results: We developed a sequence-based bioinformatics tool called 'iLoc-lncRNA' to predict the subcellular locations of LncRNAs by incorporating the 8-tuple nucleotide features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. Rigorous jackknife tests have shown that the overall accuracy achieved by the new predictor on a stringent benchmark dataset is 86.72%, which is over 20% higher than that by the existing state-of-the-art predictor evaluated on the same tests.
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