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Bias in microRNA functional enrichment analysis

Motivation: Many studies have investigated the differential expression of microRNAs (miRNAs) in disease states and between different treatments, tissues and developmental stages. Given a list of perturbed miRNAs, it is common to predict the shared pathways on which they act. The standard test for fu...

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Autores principales: Bleazard, Thomas, Lamb, Janine A, Griffiths-Jones, Sam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4426843/
https://www.ncbi.nlm.nih.gov/pubmed/25609791
http://dx.doi.org/10.1093/bioinformatics/btv023
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author Bleazard, Thomas
Lamb, Janine A
Griffiths-Jones, Sam
author_facet Bleazard, Thomas
Lamb, Janine A
Griffiths-Jones, Sam
author_sort Bleazard, Thomas
collection PubMed
description Motivation: Many studies have investigated the differential expression of microRNAs (miRNAs) in disease states and between different treatments, tissues and developmental stages. Given a list of perturbed miRNAs, it is common to predict the shared pathways on which they act. The standard test for functional enrichment typically yields dozens of significantly enriched functional categories, many of which appear frequently in the analysis of apparently unrelated diseases and conditions. Results: We show that the most commonly used functional enrichment test is inappropriate for the analysis of sets of genes targeted by miRNAs. The hypergeometric distribution used by the standard method consistently results in significant P-values for functional enrichment for targets of randomly selected miRNAs, reflecting an underlying bias in the predicted gene targets of miRNAs as a whole. We developed an algorithm to measure enrichment using an empirical sampling approach, and applied this in a reanalysis of the gene ontology classes of targets of miRNA lists from 44 published studies. The vast majority of the miRNA target sets were not significantly enriched in any functional category after correction for bias. We therefore argue against continued use of the standard functional enrichment method for miRNA targets. Availability and implementation: A Python script implementing the empirical algorithm is freely available at http://sgjlab.org/empirical-go/. Contact: sam.griffiths-jones@manchester.ac.uk or janine.lamb@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-44268432015-05-15 Bias in microRNA functional enrichment analysis Bleazard, Thomas Lamb, Janine A Griffiths-Jones, Sam Bioinformatics Original Papers Motivation: Many studies have investigated the differential expression of microRNAs (miRNAs) in disease states and between different treatments, tissues and developmental stages. Given a list of perturbed miRNAs, it is common to predict the shared pathways on which they act. The standard test for functional enrichment typically yields dozens of significantly enriched functional categories, many of which appear frequently in the analysis of apparently unrelated diseases and conditions. Results: We show that the most commonly used functional enrichment test is inappropriate for the analysis of sets of genes targeted by miRNAs. The hypergeometric distribution used by the standard method consistently results in significant P-values for functional enrichment for targets of randomly selected miRNAs, reflecting an underlying bias in the predicted gene targets of miRNAs as a whole. We developed an algorithm to measure enrichment using an empirical sampling approach, and applied this in a reanalysis of the gene ontology classes of targets of miRNA lists from 44 published studies. The vast majority of the miRNA target sets were not significantly enriched in any functional category after correction for bias. We therefore argue against continued use of the standard functional enrichment method for miRNA targets. Availability and implementation: A Python script implementing the empirical algorithm is freely available at http://sgjlab.org/empirical-go/. Contact: sam.griffiths-jones@manchester.ac.uk or janine.lamb@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-05-15 2015-01-20 /pmc/articles/PMC4426843/ /pubmed/25609791 http://dx.doi.org/10.1093/bioinformatics/btv023 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Bleazard, Thomas
Lamb, Janine A
Griffiths-Jones, Sam
Bias in microRNA functional enrichment analysis
title Bias in microRNA functional enrichment analysis
title_full Bias in microRNA functional enrichment analysis
title_fullStr Bias in microRNA functional enrichment analysis
title_full_unstemmed Bias in microRNA functional enrichment analysis
title_short Bias in microRNA functional enrichment analysis
title_sort bias in microrna functional enrichment analysis
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4426843/
https://www.ncbi.nlm.nih.gov/pubmed/25609791
http://dx.doi.org/10.1093/bioinformatics/btv023
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