Paper Info
Title
LNCRI: Long Non-Coding RNA Identifier in Multiple Species
Abstract
The pervasive nature of long non-coding RNA (lncRNA) transcription in the mammalian genomes has changed our protein-centric view of genomes. But the identification of lncRNAs is an important task to discover their functional role in species. The rapid development of next-generation sequencing technology leveraged the opportunity to discover many lncRNA transcripts. However, the cost and time-consuming nature of transcriptomics verification techniques barred the research community from focusing on lncRNA identification. To overcome these challenges we developed LNCRI (Long Non-Coding RNA Identifier), a novel machine learning (ML)-based tool for the identification of lncRNA transcripts. We leveraged weighted k-mer, pseudo nucleotide composition, hexamer usage bias, Fickett score, information of open reading frame, UTR regions, and HMMER score as a feature set to develop LNCRI. LNCRI outperformed other existing models in the task of distinguishing lncRNA transcripts from protein-coding mRNA transcripts with high accuracy in human and mouse. LNCRI also outperformed the existing tools for cross-species prediction on chimpanzee, monkey, gorilla, orangutan, cow, pig, frog and zebrafish. We applied the SHAP algorithm to demonstrate the importance of most dominating features that were leveraged in the model. We believe our tool will support the research community to identify the lncRNA transcripts in a highly accurate manner. The benchmark datasets and source code are available in GitHub: http://github.com/smusleh/LNCRI.
Year
DOI
Venue
2021
10.1109/ACCESS.2021.3131846
IEEE ACCESS
Keywords
DocType
Volume
Proteins, Mice, RNA, Encoding, Tools, Genomics, Task analysis, Long non-coding RNA, lncRNA, mRNA, machine learning, sequence analysis
Journal
9
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Saleh Musleh100.68
Mohammad Tariqul Islam202.03
Tanvir Alam301.35