title: NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq data.
abstract: BACKGROUND
  RNA-seq, a massive parallel-sequencing-based transcriptome profiling method, provides digital data in the form of aligned sequence read counts. The comparative analyses of the data require appropriate statistical methods to estimate the differential expression of transcript variants across different cell/tissue types and disease conditions.
  
  
  RESULTS
  We developed a novel nonparametric empirical Bayesian-based approach (NPEBseq) to model the RNA-seq data. The prior distribution of the Bayesian model is empirically estimated from the data without any parametric assumption, and hence the method is "nonparametric" in nature. Based on this model, we proposed a method for detecting differentially expressed genes across different conditions. We also extended this method to detect differential usage of exons from RNA-seq data. The evaluation of NPEBseq on both simulated and publicly available RNA-seq datasets and comparison with three popular methods showed improved results for experiments with or without biological replicates.
  
  
  CONCLUSIONS
  NPEBseq can successfully detect differential expression between different conditions not only at gene level but also at exon level from RNA-seq datasets. In addition, NPEBSeq performs significantly better than current methods and can be applied to genome-wide RNA-seq datasets. Sample datasets and R package are available at http://bioinformatics.wistar.upenn.edu/NPEBseq.
authors: Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, 19104 Philadelphia, PA, USA. rdavuluri@wistar.org.
authors: Yingtao
authors: Y
authors: Bi
authors: Ramana V
authors: RV
authors: Davuluri
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D012313:RNA / Q000032:analysis / Q000235:genetics; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software; D018709:Statistics, Nonparametric
chemical_list: D012313:RNA
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