Quantitative proteomics generates large datasets with increasing depth and quantitative information. Even after data processing and statistical analysis, interpreting the results and relating their significance back to the system of study remains challenging. Often, this process is performed by scientists with expertise in their field, but limited experience in proteomic or phosphoproteomic analysis. We developed a set of tools for simple, interactive exploration of phosphoproteomics data that can be easily interpreted into biological knowledge. These tools are designed to expedite the processes of reviewing raw data from statistical output, identifying and verifying enriched sequence motifs, and viewing the data from the perspective of functional pathways. Here, we present the workflow and demonstrate its functionality by analyzing a phosphoproteomic data set from two lymphoma cell lines treated with kinase inhibitors.