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

Marijke Van Moerbeke,Hinrick W H Göhlmann,Adetayo Kasim,Ziv Shkedy,Joke Reumers,Willem Talloen

doi:10.1186/s12859-017-1687-8

Marijke Van Moerbeke, Hinrick W H Göhlmann + Show 4 more

Open Access

https://doi.org/10.1186/s12859-017-1687-8

Copy DOI

Abstract

BackgroundAlternative 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.ResultsWe 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.ConclusionThe 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.

Highlights

Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms
The probe level Splicing Index (SI) estimation procedure for detecting differential splicing (PECA-SI method) detects alternative splicing based on a probe level splicing index instead of the exon level used by Microarray Detection of Alternative Splicing (MiDAS) [25]
The raw .CEL files are background corrected with the Robust Multichip Analysis (RMA) background correction, normalized with quantilenormalization and log2-transformed [27] resulting in probe level intensities on which first the Informative /non-informative (I/NI) calls and REIDS model are performed

Summary

Introduction

Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. We propose a new modelling approach for the detection of AS namely the Random Effects for the Identification of Differential Splicing (REIDS). This model identifies splicing events based on a set of two scores; an array score which is used to identify samples containing an alternatively spliced exon and an exon score to prioritize spliced exons. The REIDS method was compared with FIRMA as the existing preferred method for alternative splicing detection using simulated data and two real-life exon array studies. A third case study based on HTA illustrates how the REIDS method enables the disentanglement of differentially expressed genes and differential spliced exons. REIDS is currently bundled in a package publicly available on R-forge

Objectives

Methods

Results

Discussion

Conclusion