30 31 Transposable elements (TEs) are mobile genetic elements in eukaryotic genomes. Recent 32 research highlights the important role of TEs in the embryogenesis, neurodevelopment, and 33 immune functions. However, there is a lack of a one-stop and easy to use computational pipeline 34 for expression analysis of both genes and locus-specific TEs from RNA-Seq data. Here, we 35 present GeneTEFlow, a fully automated, reproducible and platform-independent workflow, for 36 the comprehensive analysis of gene and locus-specific TEs expression from RNA-Seq data 37 employing Nextflow and Docker technologies. This application will help researchers more easily 38 perform integrated analysis of both gene and TEs expression, leading to a better understanding of 39 roles of gene and TEs regulation in human diseases. GeneTEFlow is freely available at 40 https://github.com/zhongw2/GeneTEFlow. 41 42 54 cancers[14-16], neurodegenerative disorders[17, 18], and immune-mediated inflammation[19, 55 20]. Therefore, it has become increasingly important to explore biological roles of TEs 56 expression. However, genome-wide analysis of TEs expression from high throughput RNA 57 sequencing data has been a challenging computational problem. TEs contain highly repetitive 58 sequence elements, making it arduous to unambiguously assign reads to the correct genomic 59 location and accurately quantitate their expression level. Several bioinformatics tools have been 60 developed to address this challenge with relatively good success [16, 21-23]. Recently, SQuIRE 61 was reported to have the capability to quantify locus-specific expression of TEs from RNA-Seq 62 data[23]. In addition, RNA-Seq data has long been used to detect dysregulated genes between 63 different disease and/or drug treatment conditions to help understand disease mechanisms and/or 64 drug response mechanisms. Therefore, it is of great interest to quantify both TEs and gene 65 expression to elucidate contribution of both to disease mechanisms. Although many open source 66 software and tools exist for analysing gene [24-26] and TEs expression, there are considerable 67 challenges to efficiently apply these tools. In general, these multi-step data processing pipelines 68 use many different tools. Correct versions of each tool need to be installed separately, and 69 appropriate options, parameters, different reference genome and gene annotation files have to be 70 set at each step. This can be quite tedious and challenging especially for non-computational 71 users. Additionally, to ensure reproducibility of the analysis results, it is critical to capture 72 analysis parameters from each step of the process. Equally important, to enable general use of 73 the pipeline, the pipeline should be platform agnostic. Thus far, a one-stop computational 74 framework for the comprehensive analysis of gene and locus-specific TEs expression from 75 RNA-Seq data does not exist.