A2 Refereed review article in a scientific journal
Deep learning tools are top performers in long non-coding RNA prediction
Authors: Ammunét Tea, Wang Ning, Khan Sofia, Elo Laura L
Publisher: OXFORD UNIV PRESS
Publication year: 2022
Journal: Briefings in Functional Genomics
Journal name in source: BRIEFINGS IN FUNCTIONAL GENOMICS
Journal acronym: BRIEF FUNCT GENOMICS
Volume: 21
Issue: 3
First page : 230
Last page: 241
Number of pages: 12
ISSN: 2041-2649
eISSN: 2041-2657
DOI: https://doi.org/10.1093/bfgp/elab045
Web address : https://academic.oup.com/bfg/article/21/3/230/6523275
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/175411816
The increasing amount of transcriptomic data has brought to light vast numbers of potential novel RNA transcripts. Accurately distinguishing novel long non-coding RNAs (lncRNAs) from protein-coding messenger RNAs (mRNAs) has challenged bioinformatic tool developers. Most recently, tools implementing deep learning architectures have been developed for this task, with the potential of discovering sequence features and their interactions still not surfaced in current knowledge. We compared the performance of deep learning tools with other predictive tools that are currently used in lncRNA coding potential prediction. A total of 15 tools representing the variety of available methods were investigated. In addition to known annotated transcripts, we also evaluated the use of the tools in actual studies with real-life data. The robustness and scalability of the tools' performance was tested with varying sized test sets and test sets with different proportions of lncRNAs and mRNAs. In addition, the ease-of-use for each tested tool was scored. Deep learning tools were top performers in most metrics and labelled transcripts similarly with each other in the real-life dataset. However, the proportion of lncRNAs and mRNAs in the test sets affected the performance of all tools. Computational resources were utilized differently between the top-ranking tools, thus the nature of the study may affect the decision of choosing one well-performing tool over another. Nonetheless, the results suggest favouring the novel deep learning tools over other tools currently in broad use.
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