dSpliceType: A Multivariate Model for Detecting Various Types of Differential Splicing Events Using RNA-Seq
Alternative splicing plays a key role in regulating gene expression. Dysregulated alternative splicing events have been linked to a number of human diseases. Recently, the high-throughput RNA-Seq technology provides unprecedented opportunities and holds a strong promise for better characterizing and dissecting alternative splicing events on a whole transcriptome scale. Therefore, efficient and effective computational methods and tools for detecting differentially spliced genes and events in human disease are urgently needed. We present a novel and efficient computational method, dSpliceType, to detect five most common types of differential splicing events between two conditions using RNA-Seq. dSpliceType is among the first to utilize sequential dependency of normalized base-wise read coverage signals and capture biological variability among replicates using a multivariate statistical model. dSpliceType substantially reduces sequencing biases by taking ratio of normalized RNA-Seq splicing indexes at each nucleotide between disease and control conditions. Our method employs a change-point analysis followed by a parametric statistical test using Schwarz Information Criterion (SIC) on each candidate splicing event for differential splicing event detection. We evaluated and compared the performance of dSpliceType with the other two existing methods, MATS and Cuffdiff. The result demonstrates that dSpliceType is a fast, effective and accurate approach, which can detect various types of differential splicing events from a wide range of expressed genes, including genes with lower abundances. dSpliceType is freely available at http://orleans.cs.wayne.edu/dSpliceType/.
KeywordsDifferential Splicing Detection Next-Generation Sequencing RNA-Seq Multivariate Conditional Gaussian Schwarz Information Criterion Change Point Analysis Hypothesis Testing
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