Ovarian cancer (OC) is a relatively frequent malignant disease with a lifetime risk approaching to approximately 1 in 70. As many as 15–25 % OC arise due to known heterozygous germ-line mutations in DNA repair genes, such as BRCA1, BRCA2, RAD51C, NBN (NBS1), BRIP, and PALB2. Sporadic ovarian cancers often phenocopy the features of BRCA1-related hereditary disease (so-called BRCAness), i.e., show biallelic somatic inactivation of the BRCA1 gene. Tumor-specific BRCA1 deficiency renders selective sensitivity of transformed cells to platinating compounds and several other anticancer drugs, which explains high response rates of OC to systemic therapies. High-throughput molecular profiling of OC is instrumental for further progress in identification of novel OC diagnostic markers as well as for the development of new OC-specific treatments. However, interpretation of the huge bulk of incoming data may present a challenge. There is a critical need in the development of bioinformatic tools capable to integrate the multiplicity of available data sets into biologically and medically meaningful pieces of knowledge.