The information to construct the cells of our body is encoded by our DNA, which is shared by almost all cells in the body. Despite having the same basic set of instructions, cells can vary dramatically in function, appearance, and composition, by using different parts of the genetic code. This process is guided by variable packaging and marking of the DNA, resulting in different utilization of parts of the genome in a process referred to as “epigenetics”. One of the most important epigenetic marks is a chemical change to DNA, called DNA methylation. In recent years, we have learned that cancer cells display extensive DNA methylation abnormalities, with some parts of the genome missing appropriate DNA methylation, and others gaining DNA methylation at abnormal sites in the DNA. I have contributed substantially to the characerization of DNA methylation abnormalities in gynecological cancers, participating in large consortia, such as The Cancer Genome Atlas, where I was the primary analyst in the epigenetic characterization of serous ovarian cancer, endometrial cancer, and uterine carcinosarcoma. Here I propose to use my expertise in DNA methylation characterization to identify the cell types that give rise to different kinds of ovarian cancer. Although DNA methylation undergoes marked change in cancer cells, many of the cell-type specific DNA methylation marks are still evident in the cancer cells, and can serve as a molecular memory of the originating cell type. We propose to use Whole Genome Bisulfite Sequencing to comprehensively characterize the DNA methylation profiles of purified populations of normal cells, as well as various benign and malignant ovarian tissues. Whole genome characterization of DNA methylation profiles has not yet been reported for ovarian cancers or normal cells related to ovarian cancer. Currently available DNA methylation data for these tissue or types only cover a small portion and a biased selection of the genome. For normal tissues only bulk ovaries or fallopian tubes have been used, where different cells are mixed together and target cells masked. We plan to make our data available to benefit the broader research community, after removal of uniquely identifying germline information.