Predicting Long Non-coding RNAs Based on Genomic Sequence Information  

Jie Lv1 , Hongbo Liu1 , Hui Liu1 , Qiong Wu1 , Yan Zhang2
1. School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
2. College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Author    Correspondence author
Computational Molecular Biology, 2013, Vol. 3, No. 4   doi: 10.5376/cmb.2013.03.0004
Received: 24 Nov., 2013    Accepted: 10 Dec., 2013    Published: 27 Dec., 2013
© 2013 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Zhang et al., 2013, Predicting Long Non-coding RNAs Based on Genomic Sequence Information, Computational Molecular Biology, Vol.3, No.4 24-30 (doi: 10.5376/cmb.2013.03.0004)

Abstract

The binary classification of coding and non-coding genes is simplified near to 50 years. Genome-wide transcriptome studies have revealed that there exist tens of thousands of long non-coding RNAs (lncRNAs), while the functions are being uncovered slowly. Accurate identification of lncRNAs is the initial step to the systematic characterization of lncRNAs. The diversity of transcription patterns for lncRNAs challenges the available non-coding RNA prediction algorithms. Until now, prediction of lncRNAs mostly relies on genomic sequence and cross-species alignment information. Here, we introduce the main strategies that can discriminate lncRNA from protein-coding transcripts. Especially, recently available machine learning algorithms are shown efficient to the rapid and accurate identification of lncRNAs from a large number of putative lncRNAs based on transcriptome assembled transcripts, which would provide the basis of understanding of lncRNA biology.

Keywords
Next-Generation sequencing; Prediction; Computational approaches; Machine Learning; RNA-Seq
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