Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.

Approximately half of known human miRNAs are located in the introns of protein coding genes. Some of these intronic miRNAs are only expressed when their host gene is and, as such, their steady state expression levels are highly correlated with those of the host gene's mRNA. Recently host gene e...

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Autores principales:	M Hossein Radfar, Willy Wong, Quaid Morris
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c9ebb7bcfe794bfeb499e8150f24a23b
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spelling	oai:doaj.org-article:c9ebb7bcfe794bfeb499e8150f24a23b2021-11-18T06:52:22ZComputational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.1932-620310.1371/journal.pone.0019312https://doaj.org/article/c9ebb7bcfe794bfeb499e8150f24a23b2011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21694770/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Approximately half of known human miRNAs are located in the introns of protein coding genes. Some of these intronic miRNAs are only expressed when their host gene is and, as such, their steady state expression levels are highly correlated with those of the host gene's mRNA. Recently host gene expression levels have been used to predict the targets of intronic miRNAs by identifying other mRNAs that they have consistent negative correlation with. This is a potentially powerful approach because it allows a large number of expression profiling studies to be used but needs refinement because mRNAs can be targeted by multiple miRNAs and not all intronic miRNAs are co-expressed with their host genes.Here we introduce InMiR, a new computational method that uses a linear-Gaussian model to predict the targets of intronic miRNAs based on the expression profiles of their host genes across a large number of datasets. Our method recovers nearly twice as many true positives at the same fixed false positive rate as a comparable method that only considers correlations. Through an analysis of 140 Affymetrix datasets from Gene Expression Omnibus, we build a network of 19,926 interactions among 57 intronic miRNAs and 3,864 targets. InMiR can also predict which host genes have expression profiles that are good surrogates for those of their intronic miRNAs. Host genes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3' UTRs and are significantly more likely to have predicted Pol II and Pol III promoters in their introns.We provide a dataset of 1,935 predicted mRNA targets for 22 intronic miRNAs. These prediction are supported both by sequence features and expression. By combining our results with previous reports, we distinguish three classes of intronic miRNAs: Those that are tightly regulated with their host gene; those that are likely to be expressed from the same promoter but whose host gene is highly regulated by miRNAs; and those likely to have independent promoters.M Hossein RadfarWilly WongQuaid MorrisPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 6, p e19312 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q M Hossein Radfar Willy Wong Quaid Morris Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.
description	Approximately half of known human miRNAs are located in the introns of protein coding genes. Some of these intronic miRNAs are only expressed when their host gene is and, as such, their steady state expression levels are highly correlated with those of the host gene's mRNA. Recently host gene expression levels have been used to predict the targets of intronic miRNAs by identifying other mRNAs that they have consistent negative correlation with. This is a potentially powerful approach because it allows a large number of expression profiling studies to be used but needs refinement because mRNAs can be targeted by multiple miRNAs and not all intronic miRNAs are co-expressed with their host genes.Here we introduce InMiR, a new computational method that uses a linear-Gaussian model to predict the targets of intronic miRNAs based on the expression profiles of their host genes across a large number of datasets. Our method recovers nearly twice as many true positives at the same fixed false positive rate as a comparable method that only considers correlations. Through an analysis of 140 Affymetrix datasets from Gene Expression Omnibus, we build a network of 19,926 interactions among 57 intronic miRNAs and 3,864 targets. InMiR can also predict which host genes have expression profiles that are good surrogates for those of their intronic miRNAs. Host genes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3' UTRs and are significantly more likely to have predicted Pol II and Pol III promoters in their introns.We provide a dataset of 1,935 predicted mRNA targets for 22 intronic miRNAs. These prediction are supported both by sequence features and expression. By combining our results with previous reports, we distinguish three classes of intronic miRNAs: Those that are tightly regulated with their host gene; those that are likely to be expressed from the same promoter but whose host gene is highly regulated by miRNAs; and those likely to have independent promoters.
format	article
author	M Hossein Radfar Willy Wong Quaid Morris
author_facet	M Hossein Radfar Willy Wong Quaid Morris
author_sort	M Hossein Radfar
title	Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.
title_short	Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.
title_full	Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.
title_fullStr	Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.
title_full_unstemmed	Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.
title_sort	computational prediction of intronic microrna targets using host gene expression reveals novel regulatory mechanisms.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/c9ebb7bcfe794bfeb499e8150f24a23b
work_keys_str_mv	AT mhosseinradfar computationalpredictionofintronicmicrornatargetsusinghostgeneexpressionrevealsnovelregulatorymechanisms AT willywong computationalpredictionofintronicmicrornatargetsusinghostgeneexpressionrevealsnovelregulatorymechanisms AT quaidmorris computationalpredictionofintronicmicrornatargetsusinghostgeneexpressionrevealsnovelregulatorymechanisms
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Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.

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