Abstract
Alternative splicing is an ubiquitous phenomenon in most human genes and has important functions. The switch-like exon is the type of exon that has a high level of usage in some tissues, but has a low level of usage in the other tissues. They usually undergo strong tissue-specific regulations. There is still a lack a systematic method to identify switch-like exons from multiple RNA-seq samples. We proposed a novel method called iterative Tertile Absolute Deviation around the mode (iTAD) to profile the distribution of exon relative usages among multiple samples and to identify switch-like exons and other types of exons using a robust statistic estimator. We validated the method with simulation data, and applied it on RNA-seq data of 16 human body tissues and detected 3,100 switch-like exons. We found that switch-like exons tend to be more associated with Alu elements in their flanking intron regions than other types of exons.
Highlights
Alternative splicing (AS) is a major mechanism for increasing the functional complexity and diversity of proteins made from the relatively small number of genes in high eukaryotes especially in human [1, 2]
We proposed a new method, iterative Tertile Absolute Deviation around the mode (iTAD), based on a robust statistics for identifying switch-like exons and other types of alternative splicing exons among multiple RNA-seq samples
Unlike existing methods that were designed for detecting the difference of exon relative usages between two groups of samples, iTAD analyzes frequency profiles of exon relative usages among multiple samples in an unsupervised manner to find exons with switch-like usage patterns
Summary
Alternative splicing (AS) is a major mechanism for increasing the functional complexity and diversity of proteins made from the relatively small number of genes in high eukaryotes especially in human [1, 2]. Isoforms of the same gene are not expressed in different cell types and many of them are expressed in tissue-specific manners, which causes different relative usages of alternative exons across different samples. Xing and Lee studied the special type of “tissue-switched” exons that have dramatic changes in inclusion levels across different tissues based on microarray data [3]. Those exons tend to be always included in the expression (i.e., with very high inclusion levels) in some tissues but are always excluded in some other tissues (with 0 or very small inclusion levels), which indicts strong tissue-specific regulations.
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