Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches
| Autor: | L A Bugnon, A A Edera, S Prochetto, M Gerard, J Raad, E Fenoy, M Rubiolo, U Chorostecki, T Gabaldón, F Ariel, L E Di Persia, D H Milone, G Stegmayer |
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| Přispěvatelé: | Barcelona Supercomputing Center |
| Rok vydání: | 2022 |
| Předmět: |
Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC]
Biomolecules Deep learning (Machine learning) Computational Biology RNA secondary structure Protein Structure Secondary lncRNA Simulació per ordinador Machine learning RNA RNA Long Noncoding Computational prediction RNA Messenger Molecular Biology Software Information Systems |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| ISSN: | 1477-4054 |
| Popis: | MOTIVATION: In contrast to messenger RNAs, the function of the wide range of existing long noncoding RNAs (lncRNAs) largely depends on their structure, which determines interactions with partner molecules. Thus, the determination or prediction of the secondary structure of lncRNAs is critical to uncover their function. Classical approaches for predicting RNA secondary structure have been based on dynamic programming and thermodynamic calculations. In the last 4 years, a growing number of machine learning (ML)-based models, including deep learning (DL), have achieved breakthrough performance in structure prediction of biomolecules such as proteins and have outperformed classical methods in short transcripts folding. Nevertheless, the accurate prediction for lncRNA still remains far from being effectively solved. Notably, the myriad of new proposals has not been systematically and experimentally evaluated. RESULTS: In this work, we compare the performance of the classical methods as well as the most recently proposed approaches for secondary structure prediction of RNA sequences using a unified and consistent experimental setup. We use the publicly available structural profiles for 3023 yeast RNA sequences, and a novel benchmark of well-characterized lncRNA structures from different species. Moreover, we propose a novel metric to assess the predictive performance of methods, exclusively based on the chemical probing data commonly used for profiling RNA structures, avoiding any potential bias incorporated by computational predictions when using dot-bracket references. Our results provide a comprehensive comparative assessment of existing methodologies, and a novel and public benchmark resource to aid in the development and comparison of future approaches. This work was supported by ANPCyT (PICT 2018 3384, PICT 2018 2905, PICT 2019 3420) and UNL (CAI+D 2020 115). Researchers from sinc(i) and IAL are collaborating in the framework of the Program Science and Technology against Hunger, supported by the Argentinian Ministry of Science, to study and develop ncRNAs as exogenous bioactive molecules in agriculture. UC was funded by MICINN (IJC2019-039402-I). The work used computational resources from the Pirayu cluster, acquired with funds from the Santa Fe Science, Technology and Innovation Agency (ASACTEI), Project AC-00010-18, Res. No. 117/14. This equipment is part of the National High Performance Computing System of the Ministry of Science and Technology of Argentina. We also acknowledged the support of NVIDIA Corporation for the donation of GPUs used for this research Peer Reviewed "Article signat per 12 autors/es: L A Bugnon, A A Edera, S Prochetto, M Gerard, J Raad, E Fenoy, M Rubiolo, U Chorostecki, T Gabaldón, F Ariel, L E Di Persia, D H Milone, G Stegmayer" |
| Databáze: | OpenAIRE |
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