The Ovarian Cancer
Regulatory Atlas
OVERVIEW
The Ovarian Cancer Regulatory Atlas (OCRA) was created to address the current scarcity of data in ENCODE of ovarian cancer cell lines. This paucity has hindered our ability to understand the complexities of disease risk and progression.
At its heart, OCRA is comprehensive in nature, combining both the transcriptomic gene expression and the nuances of the epigenomic landscape, both of which are key to the annotation of this histotype specific disease. OCRA profiles 18 ovarian cancer cell lines, both normal and precursor, with the resulting dataset giving definition to this crucial question: how do cis-regulatory landscapes and their corresponding transcriptomes differ among normal precursor cells of origin and ovarian cancer histotypes to help reveal the distinct developmental, differentiation, or disease state that promote pathogenesis.
The Encyclopedia of DNA Elements (ENCODE) Consortium is an ongoing international collaboration of research groups funded by the National Human Genome Research Institute (NHGRI). The goal of ENCODE is to build a comprehensive parts list of functional elements in the human genome, including elements that act at the protein and RNA levels, and regulatory elements that control cells and circumstances in which a gene is active.
ENCODE investigators employ a variety of assays and methods to identify functional elements. The discovery and annotation of gene elements is accomplished primarily by sequencing a diverse range of RNA sources, comparative genomics, integrative bioinformatic methods, and human curation. Regulatory elements are typically investigated through DNA hypersensitivity assays, assays of DNA methylation, and immunoprecipitation (IP) of proteins that interact with DNA and RNA, i.e., modified histones, transcription factors, chromatin regulators, and RNA-binding proteins, followed by sequencing.
RESEARCH
We have developed end-to-end workflows for genomic analysis of ovarian cancer patient samples. This research integrates leading-edge developments in computational sciences, next-generation genomics (including transcriptomics, epigenomics and interactome analyses) paired with functional biology to improve our understanding of disease etiology applied to improving ovarian cancer outcomes.
