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RSeqNP: a non-parametric approach for detecting differential expression and splicing from RNA-Seq data.

ABSTRACT: High-throughput sequencing of transcriptomes (RNA-Seq) has become a powerful tool to study gene expression. Here we present an R package, rSeqNP, which implements a non-parametric approach to test for differential expression and splicing from RNA-Seq data. rSeqNP uses permutation tests to access statistical significance and can be applied to a variety of experimental designs. By combining information across isoforms, rSeqNP is able to detect more differentially expressed or spliced genes from RNA-Seq data.The R package with its source code and documentation are freely available at http://www-personal.umich.edu/?jianghui/rseqnp/.jianghui@umich.eduSupplementary data are available at Bioinformatics online.

SUBMITTER: Shi Y

PROVIDER: S-EPMC4481847 | biostudies-other | 2015 Jul

REPOSITORIES: biostudies-other

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rSeqNP: a non-parametric approach for detecting differential expression and splicing from RNA-Seq data.

Shi Yang Y Chinnaiyan Arul M AM Jiang Hui H

Bioinformatics (Oxford, England) 20150224 13

<h4>Unlabelled</h4>High-throughput sequencing of transcriptomes (RNA-Seq) has become a powerful tool to study gene expression. Here we present an R package, rSeqNP, which implements a non-parametric approach to test for differential expression and splicing from RNA-Seq data. rSeqNP uses permutation tests to access statistical significance and can be applied to a variety of experimental designs. By combining information across isoforms, rSeqNP is able to detect more differentially expressed or sp ...[more]

PMID: 25717189

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