Cytogenetic aberrations at the single-cell level represent an important characteristic of cancer cells relevant to tumor evolution and prognosis. However, with the advent of The Cancer Genome Atlas (TCGA), there has been a major shift in cancer research to the use of data from aggregate cell populations. Given that tumor cells harbor hundreds to thousands of biologically relevant genetic alterations that manifest as intra-tumor heterogeneity, these aggregate analyses may miss alterations readily observable at single-cell resolution. Using the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer (MD), we developed an algorithm to parse International System for Cytogenetic Nomenclature (ISCN) notation for quantitative abnormalities. Comparison of the MD and TCGA demonstrated that the MD is a powerful resource, and that cytogenetic aberrations captured by traditional approaches used in MD are on par with population-based genomic analyses used in TCGA. This algorithm will help non-specialists to overcome the challenges associated with the format and syntax of the MD.