Affordable Access

Detecting tissue-specific regulation of alternative splicing as a qualitative change in microarray data.

Authors
Type
Published Article
Journal
Nucleic Acids Research
Publisher
Oxford University Press
Volume
32
Issue
22
Pages
180–180
Source
Nelson Lab
License
Unknown

Abstract

Alternative splicing has recently emerged as a major mechanism of regulation in the human genome, occurring in perhaps 40-60% of human genes. Thus, microarray studies of functional regulation could, in principle, be extended to detect not only the changes in the overall expression of a gene, but also changes in its splicing pattern between different tissues. However, since changes in the total expression of a gene and changes in its alternative splicing can be mixed in complex ways among a set of samples, separating these effects can be difficult, and is essential for their accurate assessment. We present a simple and general approach for distinguishing changes in alternative splicing from changes in expression, based on detecting systematic anti-correlation between the log-ratios of two different samples versus a pool containing both samples. We have tested this analysis method on microarray data for five human tissues, generated using a standard microarray platform and experimental protocols shown previously to be sensitive to alternative splicing. Our automatic analysis was able to detect a wide variety of tissue-specific alternative splicing events, such as exon skipping,mutually exclusive exons, alternative 3 and alternative 5 splicing, alternative initiation and alternative termination, all of which were validated by independent reverse-transcriptase PCR experiments, with validation rates of 70-85%. Our analysis method also enables hierarchical clustering of genes and samples by the level of similarity to their alternative splicing patterns, revealing patterns of tissue-specific regulation that are distinct from those obtained by hierarchical clustering of gene expression from the same microarray data. Our data and analysis source code are available from http://www.bioinformatics.ucla.edu/ASAP.

Report this publication

Statistics

Seen <100 times