Visualization & Analysis
EstroGene DataBase
A Comprehensive NGS Database Focusing on Estrogen Biology in Breast Cancer
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The EstroGene Project was launched by the Drs. Steffi Oesterreich and Adrian Lee’s Laboratory at University of Pittsburgh, UPMC Hillman Cancer Center. It aims to document and integrate the majority of publicly available estrogen-related next generation sequencing data sets (including RNA-seq, microarray, ChIP-seq, ATAC-seq, DNase-seq, ChIA-PET, Hi-C, GRO-seq, etc), and establish a comprehensive data base to allow users’ customized data search and visualization visualization.

Estrogen receptors (ER) belongs to the nuclear receptor family which is indispensable for sensing estrogen signaling and mediating development, metabolism, homeostasis and other essential functions of the organism. ER-α has been widely reported as the key estrogen signaling receptor in ER+ breast cancer, which accounts for above 70% of all breast cancer cases. Upon activation by ligands, ER-α tends to form dimers and binds to genomic DNA to trigger the confound downstream effects. The rapid development of next generation sequencing technologies leads to hundreds of unbiased genomic profiling and benefits a comprehensive understanding of estrogen receptor actions in breast cancer. However, lack of a central database which curates and summarizes these publicly available estrogen data sets largely limits the power for these NGS profiles.

It is a comprehensive database:

  • Focusing on estrogen receptor biology in breast cancer
  • Integrating above 120 multi-omic NGS data sets from previous publications
  • Built for users’ customized searching and analysis


Transcriptomic Analysis (microarray and RNA-seq):

  • Curated 146 comparisons from 19 breast cancer cell lines (80 from microarray and 66 from RNA-seq). All the curated experiments covers 28 treatment durations.
  • Visualize the gene expressional changes after E2 treatment of each experiment
  • Compute the percentages of data sets showing significant up- or down-regulations to the gene of interest

ER Cistromic Analysis (ChIP-seq):

  • Currently curated 41 profiles from 17 data sets and 5 breast cancer cell lines (more to be added)
  • Visualize the ER binding sites and intensities within -/+ 200 kb TSS of each gene
  • Summarize the ER binding site clusters of each bin of 10 kb

Gene query with users-defined conditions:

  • Users can export and visualize gene lists under their selected experimental conditions and statistic thresholds using the analysis platform.

Continuous crowd-sourcing from the scientific research community:

  • We provided a metadata inventory of all the 136 multiomic data we have documented for download.
  • We are inviting the users to add additional data sets has not been included via google sheet to improve this database (See metadata page for details)

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