LncRNApred: Classification of Long Non-Coding RNAs and Protein-Coding Transcripts by the Ensemble Algorithm with a New Hybrid Feature

Abstract

As a novel class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been verified to be associated with various diseases. As large scale transcripts are generated every year, it is significant to accurately and quickly identify lncRNAs from thousands of assembled transcripts. To accurately discover new lncRNAs, we develop a classification tool of random forest (RF) named LncRNApred based on a new hybrid feature. This hybrid feature set includes three new proposed features, which are MaxORF, RMaxORF and SNR. LncRNApred is effective for classifying lncRNAs and protein coding transcripts accurately and quickly. Moreover,our RF model only requests the training using data on human coding and non-coding transcripts. Other species can also be predicted by using LncRNApred. The result shows that our method is more effective compared with the Coding Potential Calculate (CPC). The web server of LncRNApred is available for free at http://mm20132014.wicp.net:57203/LncRNApred/home.jsp.

Non-coding RNAs Shaping Muscle

“…Several computational tools have been developed to estimate the coding potential of novel identified transcripts. Among the most utilized CPC (Kong et al, 2007) and its updated version CPC2 (Kang et al, 2017), CPAT , COME (Hu et al, 2017), LncRNApred (Pian et al, 2016), PORTRAIT (Arrial et al, 2009), CONC, and others can be cited. The experimental approach is mainly based on the genomewide translatome that has been studied by ribosome footprinting, a technique introduced by Ingolia in 2009 (Ingolia et al, 2009).…”

Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination

“…Then, we compared several popular machine learning based methods, including Coding Potential Calculator (CPC) [28], Coding Potential Assessment Tool (CPAT) [29], Coding-Non-Coding Index (CNCI) [30], predictor of long noncoding RNAs and messenger RNAs based on an improved k -mer scheme (PLEK) [31], Long noncoding RNA IDentification (LncRNA-ID) [32], and lncRScan-SVM [33]. In addition, lncRNA-MFDL [34] and LncRNApred [35], two artificial neural network- (ANN-) involved tools, are also introduced in this paper. However, the provided access link of lncRNA-MFDL has been forbidden; LncRNApred often throws errors while handling massive-scale data which can be processed by CPC and CPAT successfully.…”

LncRNAs: genetic and epigenetic effects in plants

“…Coding Potential Assessment, which is also another popular approach, uses an alignment-free logistic regression model to determine the coding potential of RNAs [121]. To classify lncRNAs by machine learning techniques, there are CNCI [122], PLEK [123], LncRScan-SVM [124], lncRNA-MFDL [125], lncRNA-ID [126] and lncRNApred [127]. In human and mice, ISeeRNA [128], linc-SF [129] and DeepLNC [130] use machine learning techniques to categorize lncRNAs from transcriptome sequencing data [85].…”