The high-throughputs sequence technologies produce a massive amount of data, that can reveal new genes, identify splice variants, and quantify gene expression genome-wide. However, the volume and the complexity of data from RNA-seq experiments necessitate a scalable, and mathematical analysis based on a robust statistical model. Therefore, it is challenging to design integrated workflow, that incorporates the various analysis procedures. Particularly, the comparative transcriptome analysis is complicated due to several sources of measurement variability and poses numerous statistical challenges. In this research, we performed an integrated transcriptional profiling pipeline, which generates novel reproducible codes to obtain biologically interpretable results. Starting with the annotation of RNA-seq data and quality assessment, we provided a set of codes to serve the quality assessment visualization needed for establishing the RNA-Seq data analysis experiment. Additionally, we performed comprehensive differential gene expression analysis, presenting descriptive methods to interpret the RNA-Seq data. For implementing alternative splicing and differential exons usage analysis, we improved the performance of the Bioconductor package DEXSeq by defining the open reading frame of the exonic regions, which are differentially used between biological conditions due to the alternative splicing of the transcripts. Furthermore, we present a new methodology to analyze the differentially expressed long non-coding RNA, by finding the functional correlation of the long non-coding RNA with neighboring differential expressed protein coding genes. Thus, we obtain a clearer view of the regulation mechanism, and give a hypothesis about the role of long non-coding RNA in gene expression regulation.