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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Park, J.W., Tokheim, C., Shen, S., Xing, Y. (2013). Identifying Differential Alternative Splicing Events from RNA Sequencing Data Using RNASeq-MATS. In: Shomron, N. (eds) Deep Sequencing Data Analysis. Methods in Molecular Biology, vol 1038. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-514-9_10
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DOI: https://doi.org/10.1007/978-1-62703-514-9_10
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Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-62703-513-2
Online ISBN: 978-1-62703-514-9
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