Dataset Information

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

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.

SUBMITTER: Van Moerbeke M

PROVIDER: S-EPMC5445373 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Publications

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

Van Moerbeke Marijke M Kasim Adetayo A Talloen Willem W Reumers Joke J Göhlmann Hinrick W H HWH Shkedy Ziv Z

BMC bioinformatics 20170525 1

<h4>Background</h4>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 ...[more]

PMID: 28545391

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Project description:BackgroundWith the availability of the Affymetrix exon arrays a number of tools have been developed to enable the analysis. These however can be expensive or have several pre-installation requirements. This led us to develop an analysis workflow for analysing differential splicing using freely available software packages that are already being widely used for gene expression analysis. The workflow uses the packages in the standard installation of R and Bioconductor (BiocLite) to identify differential splicing. We use the splice index method with the LIMMA framework. The main drawback with this approach is that it relies on accurate estimates of gene expression from the probe-level data. Methods such as RMA and PLIER may misestimate when a large proportion of exons are spliced. We therefore present the novel concept of a gene correlation coefficient calculated using only the probeset expression pattern within a gene. We show that genes with lower correlation coefficients are likely to be differentially spliced.ResultsThe LIMMA approach was used to identify several tissue-specific transcripts and splicing events that are supported by previous experimental studies. Filtering the data is necessary, particularly removing exons and genes that are not expressed in all samples and cross-hybridising probesets, in order to reduce the false positive rate. The LIMMA approach ranked genes containing single or few differentially spliced exons much higher than genes containing several differentially spliced exons. On the other hand we found the gene correlation coefficient approach better for identifying genes with a large number of differentially spliced exons.ConclusionWe show that LIMMA can be used to identify differential exon splicing from Affymetrix exon array data. Though further work would be necessary to develop the use of correlation coefficients into a complete analysis approach, the preliminary results demonstrate their usefulness for identifying differentially spliced genes. The two approaches work complementary as they can potentially identify different subsets of genes (single/few spliced exons vs. large transcript structure differences).

Project description:BACKGROUND: The vast majority of human genes (>70%) are alternatively spliced. Although alternative pre-mRNA processing is modified in multiple tumors, alternative hyper-splicing signatures specific to particular tumor types are still lacking. Here, we report the use of Affymetrix Human Exon Arrays to spot hyper-splicing events characteristic of myasthenia gravis (MG)-thymoma, thymic tumors which develop in patients with MG and discriminate them from colon cancer changes. METHODOLOGY/PRINCIPAL FINDINGS: We combined GO term to parent threshold-based and threshold-independent ad-hoc functional statistics with in-depth analysis of key modified transcripts to highlight various exon-specific changes. These denote alternative splicing in MG-thymoma tumors compared to healthy human thymus and to in-house and Affymetrix datasets from colon cancer and healthy tissues. By using both global and specific, term-to-parent Gene Ontology (GO) statistical comparisons, our functional integrative ad-hoc method allowed the detection of disease-relevant splicing events. CONCLUSIONS/SIGNIFICANCE: Hyper-spliced transcripts spanned several categories, including the tumorogenic ERBB4 tyrosine kinase receptor and the connective tissue growth factor CTGF, as well as the immune function-related histocompatibility gene HLA-DRB1 and interleukin (IL)19, two muscle-specific collagens and one myosin heavy chain gene; intriguingly, a putative new exon was discovered in the MG-involved acetylcholinesterase ACHE gene. Corresponding changes in spliceosome composition were indicated by co-decreases in the splicing factors ASF/SF(2) and SC35. Parallel tumor-associated changes occurred in colon cancer as well, but the majority of the apparent hyper-splicing events were particular to MG-thymoma and could be validated by Fluorescent In-Situ Hybridization (FISH), Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and mass spectrometry (MS) followed by peptide sequencing. Our findings demonstrate a particular alternative hyper-splicing signature for transcripts over-expressed in MG-thymoma, supporting the hypothesis that alternative hyper-splicing contributes to shaping the biological functions of these and other specialized tumors and opening new venues for the development of diagnosis and treatment approaches.

Dataset Information

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

Background

Results

Conclusion

Publications

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

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