Fluent genomics with plyranges and tximeta

We construct a simple workflow for fluent genomics data analysis using the R/Bioconductor ecosystem. This involves three core steps:  the data into an appropriate abstraction, the data with respect to the biological questions of interest, and the results with respect to their underlying genomic coordinates. Here we show how to implement these steps to integrate published RNA-seq and ATAC-seq experiments on macrophage cell lines. Using , we  RNA-seq transcript quantifications into an analysis-ready data structure, called the , that contains the ranges of the reference transcripts and metadata on their provenance. Using s to represent the ATAC-seq and RNA-seq data, we differentially accessible (DA) chromatin peaks and differentially expressed (DE) genes with existing Bioconductor packages. Using  we then  the results to see if there is an enrichment of DA peaks near DE genes by finding overlaps and aggregating over log-fold change thresholds. The combination of these packages and their integration with the Bioconductor ecosystem provide a coherent framework for analysts to iteratively and reproducibly explore their biological data.