Multi-branch Convolutional Neural Network for Identification of Small Non-coding RNA genomic loci

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F20%3A00114619" target="_blank" >RIV/00216224:14740/20:00114619 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1038/s41598-020-66454-3" target="_blank" >https://doi.org/10.1038/s41598-020-66454-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-020-66454-3" target="_blank" >10.1038/s41598-020-66454-3</a>

Result language

angličtina

Original language name

Multi-branch Convolutional Neural Network for Identification of Small Non-coding RNA genomic loci

Original language description

Genomic regions that encode small RNA genes exhibit characteristic patterns in their sequence, secondary structure, and evolutionary conservation. Convolutional Neural Networks are a family of algorithms that can classify data based on learned patterns. Here we present MuStARD an application of Convolutional Neural Networks that can learn patterns associated with user-defined sets of genomic regions, and scan large genomic areas for novel regions exhibiting similar characteristics. We demonstrate that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model. We also demonstrate the ability of MuStARD for inter-species identification of functional elements by predicting mouse small RNAs (pre-miRNAs and snoRNAs) using models trained on the human genome. MuStARD can be used to filter small RNA-Seq datasets for identification of novel small RNA loci, intra- and inter- species, as demonstrated in three use cases of human, mouse, and fly pre-miRNA prediction. MuStARD is easy to deploy and extend to a variety of genomic classification questions. Code and trained models are freely available at gitlab.com/RBP_Bioinformatics/mustard.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

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

Name of the periodical

Scientific reports

ISSN

2045-2322

e-ISSN

—

Volume of the periodical

10

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

10

Pages from-to

1-10

UT code for WoS article

000559960100004

EID of the result in the Scopus database

2-s2.0-85086354140