Evaluation of deep learning in non-coding RNA classification

TLDR: This study reviews the progress of ncRNA type classification, specifically lncRNA, lincRNA, circular RNA and small nc RNA, and presents a comprehensive comparison of six deep learning based classification methods published in the past two years, and takes a close look at six state-of-the-art deep learning non-coding RNA classifiers.

Abstract: Non-coding (nc) RNA plays a vital role in biological processes and has been associated with diseases such as cancer. Classification of ncRNAs is necessary for understanding the underlying mechanisms of the diseases and to design effective treatments. Recently, deep learning has been employed for ncRNA identification and classification and has shown promising results. In this study, we review the progress of ncRNA type classification, specifically lncRNA, lincRNA, circular RNA and small ncRNA, and present a comprehensive comparison of six deep learning based classification methods published in the past two years. We identify research gaps and challenges of ncRNA types, such as the classification of subclasses of lncRNA, transcript length and compositional variation, dependency on database searches and the high false positive rate of existing approaches. We suggest future directions for cross-species performance deviation, deep learning model selection and sequence intrinsic features. Many functions of RNA strands that do not code for proteins are still to be deciphered. Methods to classify different groups of non-coding RNA increasingly use deep learning, but the landscape is diverse and methods need to be categorized and benchmarked to move forward. The authors take a close look at six state-of-the-art deep learning non-coding RNA classifiers and compare their performance and architecture.