ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context

Identifying functionally significant microRNAs (miRs) and their correspondingly most important messenger RNA targets (mRNAs) in specific biological contexts is a critical task to improve our understanding of molecular mechanisms underlying...

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Main Authors:	Wu, Chao, Bardes, Eric E., Jegga, Anil G., Aronow, Bruce J.
Format:	Online
Language:	English
Published:	Oxford University Press 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086116/

id	pubmed-4086116
recordtype	oai_dc
spelling	pubmed-40861162014-12-02 ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context Wu, Chao Bardes, Eric E. Jegga, Anil G. Aronow, Bruce J. Article Identifying functionally significant microRNAs (miRs) and their correspondingly most important messenger RNA targets (mRNAs) in specific biological contexts is a critical task to improve our understanding of molecular mechanisms underlying organismal development, physiology and disease. However, current miR–mRNA target prediction platforms rank miR targets based on estimated strength of physical interactions and lack the ability to rank interactants as a function of their potential to impact a given biological system. To address this, we have developed ToppMiR (http://toppmir.cchmc.org), a web-based analytical workbench that allows miRs and mRNAs to be co-analyzed via biologically centered approaches in which gene function associated annotations are used to train a machine learning-based analysis engine. ToppMiR learns about biological contexts based on gene associated information from expression data or from a user-specified set of genes that relate to context-relevant knowledge or hypotheses. Within the biological framework established by the genes in the training set, its associated information content is then used to calculate a features association matrix composed of biological functions, protein interactions and other features. This scoring matrix is then used to jointly rank both the test/candidate miRs and mRNAs. Results of these analyses are provided as downloadable tables or network file formats usable in Cytoscape. Oxford University Press 2014-07-01 2014-05-14 /pmc/articles/PMC4086116/ /pubmed/24829448 http://dx.doi.org/10.1093/nar/gku409 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Wu, Chao Bardes, Eric E. Jegga, Anil G. Aronow, Bruce J.
spellingShingle	Wu, Chao Bardes, Eric E. Jegga, Anil G. Aronow, Bruce J. ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context
author_facet	Wu, Chao Bardes, Eric E. Jegga, Anil G. Aronow, Bruce J.
author_sort	Wu, Chao
title	ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context
title_short	ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context
title_full	ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context
title_fullStr	ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context
title_full_unstemmed	ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context
title_sort	toppmir: ranking micrornas and their mrna targets based on biological functions and context
description	Identifying functionally significant microRNAs (miRs) and their correspondingly most important messenger RNA targets (mRNAs) in specific biological contexts is a critical task to improve our understanding of molecular mechanisms underlying organismal development, physiology and disease. However, current miR–mRNA target prediction platforms rank miR targets based on estimated strength of physical interactions and lack the ability to rank interactants as a function of their potential to impact a given biological system. To address this, we have developed ToppMiR (http://toppmir.cchmc.org), a web-based analytical workbench that allows miRs and mRNAs to be co-analyzed via biologically centered approaches in which gene function associated annotations are used to train a machine learning-based analysis engine. ToppMiR learns about biological contexts based on gene associated information from expression data or from a user-specified set of genes that relate to context-relevant knowledge or hypotheses. Within the biological framework established by the genes in the training set, its associated information content is then used to calculate a features association matrix composed of biological functions, protein interactions and other features. This scoring matrix is then used to jointly rank both the test/candidate miRs and mRNAs. Results of these analyses are provided as downloadable tables or network file formats usable in Cytoscape.
publisher	Oxford University Press
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086116/
_version_	1613109758684823552

ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context

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