title: 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.
  
  
  METHODS
  The R package with its source code and documentation are freely available at http://www-personal.umich.edu/∼jianghui/rseqnp/.
  
  
  BACKGROUND
  jianghui@umich.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: 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.
authors.forename: Yang
authors.initials: Y
authors.lastname: Shi
authors.affiliation: 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.
authors.forename: Arul M
authors.initials: AM
authors.lastname: Chinnaiyan
authors.affiliation: 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.
authors.forename: Hui
authors.initials: H
authors.lastname: Jiang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D020033:Protein Isoforms; D012313:RNA / Q000235:genetics*; D012326:RNA Splicing / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software; D018709:Statistics, Nonparametric
chemical_list: D020033:Protein Isoforms; D012313:RNA