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

Yang Shi, Arul M. Chinnaiyan, Hui Jiang

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Summary: 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/.

Original languageEnglish (US)
Pages (from-to)2222-2224
Number of pages3
JournalBioinformatics
Volume31
Issue number13
DOIs
StatePublished - Jul 1 2015
Externally publishedYes

Fingerprint

RNA Splicing
High-Throughput Nucleotide Sequencing
Recombinant DNA
Differential Expression
RNA
Transcriptome
Documentation
Protein Isoforms
Research Design
Gene Expression
Permutation Test
Statistical Significance
Experimental design
Sequencing
High Throughput
Availability
Gene
Gene expression
Design of experiments
Throughput

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

RSeqNP : A non-parametric approach for detecting differential expression and splicing from RNA-Seq data. / Shi, Yang; Chinnaiyan, Arul M.; Jiang, Hui.

In: Bioinformatics, Vol. 31, No. 13, 01.07.2015, p. 2222-2224.

Research output: Contribution to journalArticle

Shi, Yang ; Chinnaiyan, Arul M. ; Jiang, Hui. / RSeqNP : A non-parametric approach for detecting differential expression and splicing from RNA-Seq data. In: Bioinformatics. 2015 ; Vol. 31, No. 13. pp. 2222-2224.
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