Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density.

MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their pr...

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Autores principales:	Claudia Coronnello, Ryan Hartmaier, Arshi Arora, Luai Huleihel, Kusum V Pandit, Abha S Bais, Michael Butterworth, Naftali Kaminski, Gary D Stormo, Steffi Oesterreich, Panayiotis V Benos
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/339303221d764941b8c095c5b2d6688a
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spelling	oai:doaj.org-article:339303221d764941b8c095c5b2d6688a2021-11-18T05:52:37ZNovel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density.1553-734X1553-735810.1371/journal.pcbi.1002830https://doaj.org/article/339303221d764941b8c095c5b2d6688a2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23284279/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for miRNA-related studies.Claudia CoronnelloRyan HartmaierArshi AroraLuai HuleihelKusum V PanditAbha S BaisMichael ButterworthNaftali KaminskiGary D StormoSteffi OesterreichPanayiotis V BenosPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 12, p e1002830 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Claudia Coronnello Ryan Hartmaier Arshi Arora Luai Huleihel Kusum V Pandit Abha S Bais Michael Butterworth Naftali Kaminski Gary D Stormo Steffi Oesterreich Panayiotis V Benos Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density.
description	MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for miRNA-related studies.
format	article
author	Claudia Coronnello Ryan Hartmaier Arshi Arora Luai Huleihel Kusum V Pandit Abha S Bais Michael Butterworth Naftali Kaminski Gary D Stormo Steffi Oesterreich Panayiotis V Benos
author_facet	Claudia Coronnello Ryan Hartmaier Arshi Arora Luai Huleihel Kusum V Pandit Abha S Bais Michael Butterworth Naftali Kaminski Gary D Stormo Steffi Oesterreich Panayiotis V Benos
author_sort	Claudia Coronnello
title	Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density.
title_short	Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density.
title_full	Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density.
title_fullStr	Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density.
title_full_unstemmed	Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density.
title_sort	novel modeling of combinatorial mirna targeting identifies snp with potential role in bone density.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/339303221d764941b8c095c5b2d6688a
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Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density.

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