miRBind: A Deep Learning Method for miRNA Binding Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F22%3A00128672" target="_blank" >RIV/00216224:14740/22:00128672 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2073-4425/13/12/2323" target="_blank" >https://www.mdpi.com/2073-4425/13/12/2323</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/genes13122323" target="_blank" >10.3390/genes13122323</a>

Jazyk výsledku

angličtina

Název v původním jazyce

miRBind: A Deep Learning Method for miRNA Binding Classification

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

miRBind: A Deep Learning Method for miRNA Binding Classification

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10603 - Genetics and heredity (medical genetics to be 3)

Projekt

<a href="/cs/project/GJ19-10976Y" target="_blank" >GJ19-10976Y: Klasifikace miRNA vazebných míst nezávisle na „seed” oblasti</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

GENES

ISSN

2073-4425

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

13

Strana od-do

2323

Kód UT WoS článku

000902664500001

EID výsledku v databázi Scopus

2-s2.0-85144726634