RNA‐Seq experiments produce millions of discrete sequence reads as a measure of gene expression levels, and enable researchers to investigate complex aspects of the genomic studies. These include but not limited to identification of differentially expressed (DE) genes in two or more treatment conditions and detection of novel transcripts. One of the common assumptions of RNA-Seq data is that, all gene counts follow an overdispersed Poisson or negative binomial (NB) distributions, which may not be appropriate as some genes may have stable transcription levels with no overdispersion. Thus, a more realistic assumption in RNA-Seq data is to consider two sets of genes: overdispersed and non‐overdispersed. We consider a two‐step integrated approach to detect differentially expressed (DE) genes in RNA‐Seq data using standard Poisson model for non‐overdispersed genes and NB model for overdispersed genes. We evaluate this approach using two simulated and two real RNA‐Seq data sets. We compare the performance this method with the four popular R-software packages edgeR, DESeq, sSeq, and NBPSeq with their default settings. For both the simulated and real data sets, integrated approaches perform better or at least equally well compared to the regular methods embedded in these R-packages.