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International Journal of Genomics
Volume 2016, Article ID 9185496, 11 pages
http://dx.doi.org/10.1155/2016/9185496
Research Article

Lncident: A Tool for Rapid Identification of Long Noncoding RNAs Utilizing Sequence Intrinsic Composition and Open Reading Frame Information

1College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
2Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai 519041, China

Received 5 August 2016; Revised 24 October 2016; Accepted 28 November 2016

Academic Editor: Graziano Pesole

Copyright © 2016 Siyu Han et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

More and more studies have demonstrated that long noncoding RNAs (lncRNAs) play critical roles in diversity of biological process and are also associated with various types of disease. How to rapidly identify lncRNAs and messenger RNA is the fundamental step to uncover the function of lncRNAs identification. Here, we present a novel method for rapid identification of lncRNAs utilizing sequence intrinsic composition features and open reading frame information based on support vector machine model, named as Lncident (LncRNAs identification). The 10-fold cross-validation and ROC curve are used to evaluate the performance of Lncident. The main advantage of Lncident is high speed without the loss of accuracy. Compared with the exiting popular tools, Lncident outperforms Coding-Potential Calculator, Coding-Potential Assessment Tool, Coding-Noncoding Index, and PLEK. Lncident is also much faster than Coding-Potential Calculator and Coding-Noncoding Index. Lncident presents an outstanding performance on microorganism, which offers a great application prospect to the analysis of microorganism. In addition, Lncident can be trained by users’ own collected data. Furthermore, R package and web server are simultaneously developed in order to maximize the convenience for the users. The R package “Lncident” can be easily installed on multiple operating system platforms, as long as R is supported.