MiRTar2GO: A novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA-target interaction data

Ahadi, A; Sablok, G; Hutvagner, G

MiRTar2GO: A novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA-target interaction data

Ahadi, A

Sablok, G

Hutvagner, G

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Publication Type:: Journal Article
Citation:: Nucleic Acids Research, 2017, 45 (6)
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Ahadi, A https://orcid.org/0000-0002-2018-3065	en_US
dc.contributor.author	Sablok, G https://orcid.org/0000-0002-4157-9405	en_US
dc.contributor.author	Hutvagner, G https://orcid.org/0000-0002-7231-9446	en_US
dc.date.available	2016-11-16	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Nucleic Acids Research, 2017, 45 (6)	en_US
dc.identifier.issn	0305-1048	en_US
dc.identifier.uri	http://hdl.handle.net/10453/68669
dc.description.abstract	© 2016 The Author(s). MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of celltype specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org.	en_US
dc.relation	http://purl.org/au-research/grants/arc/
dc.relation	http://purl.org/au-research/grants/arc/FT110100455
dc.relation.ispartof	Nucleic Acids Research	en_US
dc.relation.isbasedon	10.1093/nar/gkw1185	en_US
dc.subject.classification	Developmental Biology	en_US
dc.subject.mesh	Cell Line	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	MicroRNAs	en_US
dc.subject.mesh	RNA, Messenger	en_US
dc.subject.mesh	Sequence Analysis, RNA	en_US
dc.subject.mesh	Immunoprecipitation	en_US
dc.subject.mesh	Gene Expression Regulation	en_US
dc.subject.mesh	Binding Sites	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Models, Genetic	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Software	en_US
dc.subject.mesh	Argonaute Proteins	en_US
dc.subject.mesh	Machine Learning	en_US
dc.title	MiRTar2GO: A novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA-target interaction data	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	45	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	05 Environmental Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - C3 - Climate Change Cluster
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	45	en_US

Abstract:

© 2016 The Author(s). MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of celltype specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org.

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http://hdl.handle.net/10453/68669