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The Ovarian Cancer

Regulatory Atlas 


The Ovarian Cancer Regulatory Atlas (O.C.R.A.) 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.


OCRA encompasses both epigenomic and transcriptomic data across 18 ovarian cancer and precursor cell lines of multiple disease histotypes. Accessible regions of chromatin can be understood through their regulatory landscapes; OCRA seeks to define these regions through 3 histone marks (H3k27ac, H3k4me1, H3k4me3), which annotate active enhancers and promotors and poised enhancer regions, along with architectural binding sites (CTCF). This data is overlaid with transcriptomic profiling to help elucidate the complex relationship of the progression from normal to cancer, which is currently absent in ENCODE for most ovarian cancer histotypes.


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.

Test Tubes

Here you will find software and published workflows that we have produced in the course of our studies on ovarian cancer. Each software is relevant to one or more of the major themes that form the focus of our research.

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