title: Evaluation of read count based RNAseq analysis methods.
abstract: BACKGROUND
  RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. To date, there has not been a consensus on the best approach for conducting robust RNAseq analysis.
  
  
  RESULTS
  In this study, we designed a thorough experiment to evaluate six read count-based RNAseq analysis methods (DESeq, DEGseq, edgeR, NBPSeq, TSPM and baySeq) using both real and simulated data. We found the six methods produce similar fold changes and reasonable overlapping of differentially expressed genes based on p-values. However, all six methods suffer from over-sensitivity.
  
  
  CONCLUSIONS
  Based on the evaluation of runtime using real data and area under the receiver operating characteristic curve (AUC-ROC) using simulated data, we found that edgeR achieves a better balance between speed and accuracy than the other methods.
authors.forename: Yan
authors.initials: Y
authors.lastname: Guo
authors.forename: Chung-I
authors.initials: CI
authors.lastname: Li
authors.forename: Fei
authors.initials: F
authors.lastname: Ye
authors.forename: Yu
authors.initials: Y
authors.lastname: Shyr
mesh_terms: D019540:Area Under Curve; D003198:Computer Simulation; D020869:Gene Expression Profiling; D023281:Genomics; D006801:Humans; D012372:ROC Curve; D012680:Sensitivity and Specificity; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software