In this study, a new tool for the identification of differentially expressed genes with RNA-seq data, named GExposer, was developed.  Results on several independent tests show that GExposer has better performance than other methods. 
GExposer
Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization.To improve the applicability of RNA-seq technology, a large number of RNA-seq data analysis methods and correction algorithms have been developed.  In this study, a new tool for the identification of differentially expressed genes with RNA-seq data, named GExposer, was developed. 
RNA-seq
Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization.To improve the applicability of RNA-seq technology, a large number of RNA-seq data analysis methods and correction algorithms have been developed.  The naive Bayes classifier is employed to integrate fold change, transcript length, and GC content to identify differentially expressed genes.  The combination of the local normalization algorithm and naive Bayes classifier with three attributes can achieve better results; both false positive rates and false negative rates are reduced. 
naive Bayes classifier
This tool introduces a local normalization algorithm to reduce the bias of nonrandomly positioned read depth.  The combination of the local normalization algorithm and naive Bayes classifier with three attributes can achieve better results; both false positive rates and false negative rates are reduced. 
local normalization algorithm