Identification of cell-type and time point specific transcriptional regulators
Processing and analysis of high-dimensional, complex omics data requires a variety of heterogenous software. Given the large volume of data produced, high performance computing is necessary to perform such analyses in time. Bioinformatics tools are developed individually and differ in file format as well as CPU and memory requirements. This frequently leads to individual scripts for each step in a pipeline and manual intervention for data management. Long scripts automating all steps often reserve vastly more computing resources than necessary. Workflow management system such as Snakemake have become invaluable for fast and resource-efficient computational analyses. Here, we present a pipeline that combines the processing and the integration of transcriptomic and epigenomic data for the identification of transcriptional regulators in cell differentiation processes as described in Gerard et al. The implementation of the EPIC-DREM pipeline with Snakemake allows for a fluent execution of the code without manipulation of intermediate data and enables experienced users to customize it by changing or adding rules and tools. The analysis with our pipeline is completely reproducible and suitable for users with limited bioinformatics skills.