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• W2024190032 abstract "More than 90% of human genes are alternatively spliced through different types of splicing. The high-throughput RNA-Seq technology provides unprecedented opportunities for detection of differential pre-mRNA alternative splicing between different transcriptomes. Besides differential expression analysis, differential splicing analysis may generate new understanding into cell development and differentiation as well as various human diseases. In this paper, we present a novel computational method for detecting types of differential splicing events between transcriptomes using RNA-Seq data. Our method utilizes sequential dependency of base-wise read coverage signals and detects significant differential splicing events in the form of five types of splicing events supported by junction reads. For each candidate splicing event, by taking ratio of normalized RNA-Seq splicing indexes at each nucleotide location of two samples, our method reduces the effect of sequencing and alignment biases. We employ a parametric statistical test and a change-point type of analysis on each candidate splicing event for differential splicing event detection. We applied our method on a public RNA-Seq data set of human H1 and H1 differentiation into neural progenitor cell lines and detected many significant differential splicing events falling into the five well-known types of alternative splicing. We also compared our method with the other two existing methods, and the results demonstrate that our method is a promising approach, which can uniquely detect more differential splicing events using RNA-Seq data." @default.
• W2024190032 created "2016-06-24" @default.
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• W2024190032 date "2013-09-22" @default.
• W2024190032 modified "2023-10-16" @default.
• W2024190032 title "Detecting various types of differential splicing events using RNA-Seq data" @default.
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• W2024190032 doi "https://doi.org/10.1145/2506583.2512361" @default.
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