Chapter title |
Identifying Differential Alternative Splicing Events from RNA Sequencing Data Using RNASeq-MATS.
|
---|---|
Chapter number | 10 |
Book title |
Deep Sequencing Data Analysis
|
Published in |
Methods in molecular biology, July 2013
|
DOI | 10.1007/978-1-62703-514-9_10 |
Pubmed ID | |
Book ISBNs |
978-1-62703-513-2, 978-1-62703-514-9
|
Authors |
Park JW, Tokheim C, Shen S, Xing Y, Juw Won Park, Collin Tokheim, Shihao Shen, Yi Xing, Park, Juw Won, Tokheim, Collin, Shen, Shihao, Xing, Yi |
Abstract |
RNA sequencing (RNA-Seq) has emerged as a powerful and increasingly cost-effective technology for analysis of transcriptomes. RNA-Seq has several significant advantages over gene expression microarrays, including its high sensitivity and accuracy, broad dynamic range, nucleotide-level resolution, ability to detect novel mRNA transcripts, and ability to analyze pre-mRNA alternative splicing. A major application of RNA-Seq is to detect differential alternative splicing, i.e., differences in exon splicing patterns among different biological conditions. We recently developed a statistical method multivariate analysis of transcript splicing (MATS) for detecting differential alternative splicing events from RNA-Seq data. Here, we describe a computational pipeline RNASeq-MATS based on the MATS algorithm. This pipeline automatically detects and analyzes differential alternative splicing events corresponding to all major types of alternative splicing patterns from RNA-Seq data. |
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