miMsg: a target enrichment algorithm for predicted miR-mRNA interactions based on relative ranking of matched expression data

Martin A Rijlaarsdam; David J Rijlaarsdam; Ad J M Gillis; Lambert C J Dorssers; Leendert H J Looijenga

doi:10.1093/bioinformatics/btt246

miMsg: a target enrichment algorithm for predicted miR-mRNA interactions based on relative ranking of matched expression data

Martin A Rijlaarsdam
, David J Rijlaarsdam
, Ad J M Gillis
, Lambert C J Dorssers
, Leendert H J Looijenga

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

MOTIVATION: Algorithms predicting microRNA (miR)-mRNA interactions generate high numbers of possible interactions, many of which might be non-existent or irrelevant in a certain biological context. It is desirable to develop a transparent, user-friendly, unbiased tool to enrich miR-mRNA predictions.

RESULTS: The miMsg algorithm uses matched miR/mRNA expression data to enrich miR-mRNA predictions. It grades interactions by the number, magnitude and significance of misplacements in the combined ranking profiles of miR/mRNA expression assessed over multiple biological samples. miMsg requires minimal user input and makes no statistical assumptions. It identified 921 out of 56 262 interactions as top scoring and significant in an actual germ cell cancer dataset. Twenty-eight miR-mRNA pairs were deemed of highest interest based on ranking by miMsg and supported by current knowledge about validated interactions and biological function. To conclude, miMsg is an effective algorithm to reduce a high number of predicted interactions to a small set of high confidence interactions for further study.

AVAILABILITY AND IMPLEMENTATION: Matlab source code and datasets available at www.martinrijlaarsdam.nl/mimsg .

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original language	English
Pages (from-to)	1638-46
Number of pages	9
Journal	Bioinformatics (Oxford, England)
Volume	29
Issue number	13
DOIs	https://doi.org/10.1093/bioinformatics/btt246
Publication status	Published - 1 Jul 2013
Externally published	Yes

Keywords

Algorithms
Gene Expression Profiling
Humans
MicroRNAs/metabolism
Neoplasms, Germ Cell and Embryonal/genetics
RNA, Messenger/metabolism

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10.1093/bioinformatics/btt246

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title = "miMsg: a target enrichment algorithm for predicted miR-mRNA interactions based on relative ranking of matched expression data",

abstract = "MOTIVATION: Algorithms predicting microRNA (miR)-mRNA interactions generate high numbers of possible interactions, many of which might be non-existent or irrelevant in a certain biological context. It is desirable to develop a transparent, user-friendly, unbiased tool to enrich miR-mRNA predictions.RESULTS: The miMsg algorithm uses matched miR/mRNA expression data to enrich miR-mRNA predictions. It grades interactions by the number, magnitude and significance of misplacements in the combined ranking profiles of miR/mRNA expression assessed over multiple biological samples. miMsg requires minimal user input and makes no statistical assumptions. It identified 921 out of 56 262 interactions as top scoring and significant in an actual germ cell cancer dataset. Twenty-eight miR-mRNA pairs were deemed of highest interest based on ranking by miMsg and supported by current knowledge about validated interactions and biological function. To conclude, miMsg is an effective algorithm to reduce a high number of predicted interactions to a small set of high confidence interactions for further study.AVAILABILITY AND IMPLEMENTATION: Matlab source code and datasets available at www.martinrijlaarsdam.nl/mimsg .SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",

keywords = "Algorithms, Gene Expression Profiling, Humans, MicroRNAs/metabolism, Neoplasms, Germ Cell and Embryonal/genetics, RNA, Messenger/metabolism",

author = "Rijlaarsdam, \{Martin A\} and Rijlaarsdam, \{David J\} and Gillis, \{Ad J M\} and Dorssers, \{Lambert C J\} and Looijenga, \{Leendert H J\}",

year = "2013",

month = jul,

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doi = "10.1093/bioinformatics/btt246",

language = "English",

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T2 - a target enrichment algorithm for predicted miR-mRNA interactions based on relative ranking of matched expression data

AU - Rijlaarsdam, Martin A

AU - Rijlaarsdam, David J

AU - Gillis, Ad J M

AU - Dorssers, Lambert C J

AU - Looijenga, Leendert H J

PY - 2013/7/1

Y1 - 2013/7/1

N2 - MOTIVATION: Algorithms predicting microRNA (miR)-mRNA interactions generate high numbers of possible interactions, many of which might be non-existent or irrelevant in a certain biological context. It is desirable to develop a transparent, user-friendly, unbiased tool to enrich miR-mRNA predictions.RESULTS: The miMsg algorithm uses matched miR/mRNA expression data to enrich miR-mRNA predictions. It grades interactions by the number, magnitude and significance of misplacements in the combined ranking profiles of miR/mRNA expression assessed over multiple biological samples. miMsg requires minimal user input and makes no statistical assumptions. It identified 921 out of 56 262 interactions as top scoring and significant in an actual germ cell cancer dataset. Twenty-eight miR-mRNA pairs were deemed of highest interest based on ranking by miMsg and supported by current knowledge about validated interactions and biological function. To conclude, miMsg is an effective algorithm to reduce a high number of predicted interactions to a small set of high confidence interactions for further study.AVAILABILITY AND IMPLEMENTATION: Matlab source code and datasets available at www.martinrijlaarsdam.nl/mimsg .SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AB - MOTIVATION: Algorithms predicting microRNA (miR)-mRNA interactions generate high numbers of possible interactions, many of which might be non-existent or irrelevant in a certain biological context. It is desirable to develop a transparent, user-friendly, unbiased tool to enrich miR-mRNA predictions.RESULTS: The miMsg algorithm uses matched miR/mRNA expression data to enrich miR-mRNA predictions. It grades interactions by the number, magnitude and significance of misplacements in the combined ranking profiles of miR/mRNA expression assessed over multiple biological samples. miMsg requires minimal user input and makes no statistical assumptions. It identified 921 out of 56 262 interactions as top scoring and significant in an actual germ cell cancer dataset. Twenty-eight miR-mRNA pairs were deemed of highest interest based on ranking by miMsg and supported by current knowledge about validated interactions and biological function. To conclude, miMsg is an effective algorithm to reduce a high number of predicted interactions to a small set of high confidence interactions for further study.AVAILABILITY AND IMPLEMENTATION: Matlab source code and datasets available at www.martinrijlaarsdam.nl/mimsg .SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

KW - Algorithms

KW - Gene Expression Profiling

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KW - Neoplasms, Germ Cell and Embryonal/genetics

KW - RNA, Messenger/metabolism

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ER -

miMsg: a target enrichment algorithm for predicted miR-mRNA interactions based on relative ranking of matched expression data

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Keywords

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