miRBind: A Deep Learning Method for miRNA Binding Classification

KLIMENTOVÁ, Eva, Václav HEJRET, Ján KRČMÁŘ, Katarína GREŠOVÁ, Ilektra-Chara GIASSA and Panagiotis ALEXIOU. miRBind: A Deep Learning Method for miRNA Binding Classification. GENES. MDPI, 2022, vol. 13, No 12, p. 2323-2335. ISSN 2073-4425. Available from: https://dx.doi.org/10.3390/genes13122323.

Basic information

• Original name: miRBind: A Deep Learning Method for miRNA Binding Classification
• Authors: KLIMENTOVÁ, Eva (203 Czech Republic, belonging to the institution), Václav HEJRET (203 Czech Republic, belonging to the institution), Ján KRČMÁŘ (703 Slovakia, belonging to the institution), Katarína GREŠOVÁ (703 Slovakia, belonging to the institution), Ilektra-Chara GIASSA (300 Greece, belonging to the institution) and Panagiotis ALEXIOU (300 Greece, guarantor, belonging to the institution).
• Edition: GENES, MDPI, 2022, 2073-4425.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10603 Genetics and heredity
• Country of publisher: Switzerland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 3.500
• RIV identification code: RIV/00216224:14740/22:00128672
• Organization unit: Central European Institute of Technology
• Doi: http://dx.doi.org/10.3390/genes13122323
• UT WoS: 000902664500001
• Keywords in English: miRNA; target prediction; miRNA binding; CLASH; convolutional neural network
• Tags: CF BIOIT, rivok
• Tags: International impact, Reviewed

Abstract

The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To date, the potential for miRNA:target site binding is evaluated using either co-folding free energy measures or heuristic approaches, based on the identification of binding 'seeds', i.e., continuous stretches of binding corresponding to specific parts of the miRNA. The limitations of both these families of methods have produced generations of miRNA target prediction algorithms that are primarily focused on 'canonical' seed targets, even though unbiased experimental methods have shown that only approximately half of in vivo miRNA targets are 'canonical'. Herein, we present miRBind, a deep learning method and web server that can be used to accurately predict the potential of miRNA:target site binding. We trained our method using seed-agnostic experimental data and show that our method outperforms both seed-based approaches and co-fold free energy approaches. The full code for the development of miRBind and a freely accessible web server are freely available.

Links

• GJ19-10976Y, research and development project: Name: Klasifikace miRNA vazebných míst nezávisle na „seed” oblasti

Investor: Czech Science Foundation