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A random effects model for the identification of differential splicing (REIDS) using exon and HTA arrays.

Van Moerbeke, Marijke and Kasim, Adetayo and Talloen, Willem and Reumers, Joke and Göhlmann, Hinrick W. H. and Shkedy, Ziv (2017) 'A random effects model for the identification of differential splicing (REIDS) using exon and HTA arrays.', BMC mioinformatics., 18 (1). p. 273.

Abstract

Background: Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events do occur which have been linked to genetic disorders, such as several types of cancer and neurodegenerative diseases (Fan et al., Theor Biol Med Model 3:19, 2006). Therefore, understanding the mechanism of alternative splicing and identifying the difference in splicing events between diseased and healthy tissue is crucial in biomedical research with the potential of applications in personalized medicine as well as in drug development. Results: We propose a linear mixed model, Random Effects for the Identification of Differential Splicing (REIDS), for the identification of alternative splicing events. Based on a set of scores, an exon score and an array score, a decision regarding alternative splicing can be made. The model enables the ability to distinguish a differential expressed gene from a differential spliced exon. The proposed model was applied to three case studies concerning both exon and HTA arrays. Conclusion: The REIDS model provides a work flow for the identification of alternative splicing events relying on the established linear mixed model. The model can be applied to different types of arrays.

Item Type:Article
Full text:(VoR) Version of Record
First Live Deposit - 21 June 2017
Available under License - Creative Commons Attribution.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1186/s12859-017-1687-8
Publisher statement:© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Record Created:21 Jun 2017 11:58
Last Modified:11 Jul 2017 09:21

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