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Bioinformatics. 2015 Jul 1;31(13):2222-4. doi: 10.1093/bioinformatics/btv119. Epub 2015 Feb 24.

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

Author information

1
Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
2
Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.

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.

AVAILABILITY AND IMPLEMENTATION:

The R package with its source code and documentation are freely available at http://www-personal.umich.edu/∼jianghui/rseqnp/.

CONTACT:

jianghui@umich.edu

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
25717189
PMCID:
PMC4481847
DOI:
10.1093/bioinformatics/btv119
[Indexed for MEDLINE]
Free PMC Article

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