DeepLNC, a long non-coding RNA prediction tool using deep neural network

Abstract

The significant role of long non-coding RNAs (lncRNAs) in various cellular functions, such as gene imprinting, immune response, embryonic pluripotency, tumorogenesis, and genetic regulations, has been widely studied and reported in recent years. Several experimental and computational methods involving genome-wide search and screenings of ncRNAs are being proposed utilizing sequence features-length, occurrence, and composition of bases with various limitations. The proposed classifier, Deep Neural Network (DNN) is fast and an accurate alternative for the identification of lncRNAs as compared to other existing classifiers. The information content stored in k-mer pattern has been used as a sole feature for the DNN classifier using manually annotated training datasets from LNCipedia and RefSeq database, obtaining accuracy of 98.07 %, sensitivity of 98.98 %, and specificity of 97.19 %, respectively, on test dataset. The k-mer information content generated on the basis of Shannon entropy function has resulted in improved classifier accuracy. This classification framework was also tested on known human genome dataset, and the framework has successfully identified known lncRNAs with 99 % accuracy rate. The said algorithm has been implemented as a web prediction tool, which is available on server interface http:// bioserver.iiita.ac.in/deeplnc.

Deep Learning in Omics Data Analysis and Precision Medicine

“…In this context, Hill et al proposed an RNN to differentiate between coding and non-coding RNAs (38), demonstrating the capability of their algorithm to identify ncRNAs without providing their model with previous knowledge. Tripathi et al developed a method to detect long ncRNAs (lncRNAs) (39). They reached a remarkable 99% accuracy rate applying a DFF to reference databases.…”

LncRNAs: genetic and epigenetic effects in plants

“…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]. ISeeRNA is a computational pipeline to identify lncRNAs on the basis of support vector machine (SVM) algorithm.…”

lncRNA_Mdeep: an alignment-free predictor for long non-coding RNAs identification by multimodal deep learning

“…Several computational methods are developed for distinguishing lncRNAs from protein-coding transcripts. Existing computation methods can mainly categorized into alignment-based methods [8][9][10][11][12][13] and alignment-free methods [14][15][16][17][18][19][20][21]. The alignment-based methods generally align the transcripts against comprehensive reference protein database to predict lncRNAs, for example, CPC [8] aligned transcripts against UniRef90 dataset [22] using BLSATX [23] tool; lncRNA-ID [11] and lncADeep [13] aligned the transcripts against Pfam dataset [24] using HMMER [25] tool.…”

“…Such as, lncRNA-MFDL [17] was developed to identify lncRNAs by fusing multiple features and a deep stacking network, and Tripathi et.al. [18] proposed the DeepLNC method to identify lncRNAs by k-mer features and a deep neural network classi er. Although deep learning algorithms achieve a better performance than conventional machine learning algorithms, these two methods still depend on manually crafted features, and fail to learn intrinsic features automatically from raw transcript sequences.…”