Target identification and selection is a critical rate-limiting step in drug discovery, especially the context of cancer research, following the identification of many driver mutations and tumor-associated transcriptional signatures. To this end, the development of refined bioinformatic tools is essential to gain insights from data deriving from high-throughput technologies. We have developed the MIEP (make-it-easy-pipeline), a user-friendly R package implementing an integrated pipeline for RNA-seq data analysis. MIEP represents an easy-to-use tool that performs statistical testing, annotates sequences, reduces biases, and summarizes results in HTML tables, volcano plots, and heat maps. MIEP performs dimensionality reduction of data, tests the enrichment of Gene Ontology terms and includes machine learning algorithms for sample classification. In our hands, the use of MIEP in the cancer research field facilitated the identification of phenotype-linked signal transduction pathways whose biological relevance was experimentally verified. Calling up a single function, a series of integrated analyses is launched in sequence. Settings can be changed while running the analysis thanks to user-friendly, interactive apps. Automatic procedures optimize the selection of the hyperparameters affecting the different algorithms. Together with careful handling of exceptions, these characteristics make MIEP a handy tool for researchers with only basic knowledge of programming. MIEP also allows editing new gene sets based on results of multiple analyses and provides gene set-focused data representations. This can accelerate the understanding of pathogenic mechanisms as well as the development of new drugs because the gene set-focused approach greatly facilitates the identification of functional hubs and new potential pharmacological targets.