GEGVIC: A workflow to analyze Gene Expression, Genetic Variations and Immune cell Composition of tumor samples using Next Generation Sequencing data

Background. The application of next-generation sequencing techniques for genome and transcriptome profiling is to build the main source of data for cancer research. Hundreds of bioinformatic pipelines have been developed to handle the data generated by these technologies, but their use often requires specialized expertise in data wrangling and analysis that limit many biomedical researchers. Providing easy-to-use, yet comprehensive and integrative open-source tools is essential to help wet-lab and clinical scientists feel more autonomous in performing common omics data analysis in cancer research. Results. Here, we present GEGVIC, an R tool to easily perform a set of frequently used analyses in cancer research, including differential gene expression, genomic mutations exploration and immune cell deconvolution using minimally processed human/mouse genomic and transcriptomic sequencing data. GEGVIC is designed as a modular pipeline that combines a variety of widely used available methods distributed in three principal modules ( Gene Expression , Genomic Variation and Immune Composition ), which run independently and include several visualization tools. Conclusions. In summary, GEGVIC provides a simple, powerful and highly flexible workflow for researchers to process and interpret tumor transcriptomic and genomic data while decreasing or eliminating coding burden and facilitating efficiency for inexperienced bioinformatics users. GEGVIC R package instructions and source code are published on Github (https://github.com/oriolarques/GEGVIC).