R-loops are transcription intermediates containing an RNA:DNA hybrid and a displaced single-strand DNA. R-loops are important regulators of various cellular processes such as transcription and DNA repair. However, when R-loop levels and/or distributions in the genome are dysregulated, they act as endogenous sources of genomic instability, a hallmark of cancers. Mutations in genes encoding for R-loop regulators are observed in various cancers and diseases, linking R-loop dysregulation to disease pathogenesis. As the number of known R-loop regulators increases, this raises an intriguing question on how different factors contribute to R-loop homeostasis. Furthermore, whether various R-loop regulators function at the same R-loop regions in different diseases is largely unknown. In order to answer these questions, a proper mapping method to analyze R-loop landscapes in different diseases will be necessary. My thesis project focuses on establishing an R-loop mapping strategy called MapR. MapR was reported to produce a high signal-to-noise ratio in genome-wide next-generation sequencing (NGS). However, it lacks a quality control step prior to NGS. In this study, I successfully established and optimized the MapR method to quantify R-loop levels by quantitative PCR (MapR-qPCR) as the quality control step. A significantly higher level of R-loop enrichment was observed at the R-loop positive RPL13A locus compared to that at the R-loop negative SNRPN locus by the MapR-qPCR analysis. Furthermore, treating MapR samples with RNaseH, an enzyme that hydrolyzes the RNA moiety within RNA:DNA hybrids, partially suppressed R-loop enrichment at RPL13A locus, suggests that MapR enriches for R-loops. My project will allow us to integrate the MapR-qPCR analysis as quality control checkpoints prior to genome-wide sequencing.