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Related Experiment Video

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Identifying differential exon splicing using linear models and correlation coefficients.

Sonia H Shah¹, Jacqueline A Pallas

¹Wolfson Institute of Biomedical Research, Department of Computer Science, University College London, Gower Street, London, UK. sonia.shah@cs.ucl.ac.uk

BMC Bioinformatics

|January 22, 2009

Summary

This summary is machine-generated.

This study presents a new workflow for analyzing differential splicing in Affymetrix exon array data using freely available R and Bioconductor packages. The approach combines the LIMMA framework with a novel gene correlation coefficient to identify differentially spliced genes.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Affymetrix exon arrays enable detailed transcript analysis.
Existing analysis tools can be costly or complex to install.
There is a need for accessible workflows for differential splicing analysis.

Purpose of the Study:

To develop a cost-effective and accessible analysis workflow for differential splicing using R and Bioconductor.
To evaluate the effectiveness of the splice index method with the LIMMA framework.
To introduce and assess a novel gene correlation coefficient for identifying differentially spliced genes.

Main Methods:

Utilized freely available R and Bioconductor packages for data analysis.
Applied the splice index method within the LIMMA framework.
Developed and implemented a gene correlation coefficient based on probeset expression patterns.

Main Results:

The LIMMA approach successfully identified tissue-specific transcripts and splicing events.
Data filtering is crucial to reduce false positives.
The gene correlation coefficient approach effectively identified genes with numerous differentially spliced exons, complementing LIMMA's strength in detecting single/few spliced exons.

Conclusions:

LIMMA is a viable method for differential exon splicing analysis from Affymetrix exon array data.
The gene correlation coefficient shows promise for identifying differentially spliced genes.
The two methods offer complementary insights into different types of splicing variations.