dSpliceType: A Multivariate Model for Detecting Various Types of Differential Splicing Events Using RNA-Seq

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