Recent advances in high-throughput sequencing techniques have made it possible to tag ribonucleoside monophosphates (rNMPs) embedded in genomic DNA for sequencing. rNMP sequencing experiments generate large, complex datasets that require efficient, scalable software that can accurately map embedded rNMPs independently of the particular sequencing technique used. Current computational pipelines designed to map rNMPs embedded in genomic DNA are customized for data generated using only one type of rNMP sequencing technique. To standardize the processing and analysis of rNMP sequencing experiments, we developed Ribose-Map. Through a series of analytical modules, Ribose-Map transforms raw sequencing data into summary datasets and publication-ready visualizations of results, allowing biologists to identify sites of embedded rNMPs, study the nucleotide sequence context of these rNMPs and explore their genome-wide distribution. By accommodating data from any of the available rNMP sequencing techniques, Ribose-Map can increase the reproducibility of rNMP sequencing experiments and enable a head-to-head comparison of these experiments.