The Next generation of the G-DOC Platform: G-DOC Hub
The older version of G-DOC has been retired, and replaced by the next generation of the G-DOC platform
The Next generation of the G-DOC Platform: G-DOC Hub
G-DOC is our Flagship platform that enables the integrative analysis of genomic data to understand disease mechanisms. It was designed and engineered to be a unique multi-omics data analysis resource for translational cancer research. It currently integrates clinical, transcriptomic, metabolomic, and systems-level analysis into a single, user-friendly cloud based platform. This integration allows users to identify trends and patterns in complex datasets. Our collaborators include Lombardi Comprehensive Cancer Center Research Programs and Shared Resources.
The G-DOC platform is undergoing a major upgrade. The new version will be powered by a suite of tools that allow users to get free cloud based access to massive genomic data sets such as The Cancer Genome Atlas (TCGA), TARGET and CPTAC as well as an array of data analytic tools for exploratory analysis of big biomedical data.
Suite of Tools
- The G-DOC Hub platform powered by the UCSC Xena genomics browser
- Downstream Analysis of Variant Data using Open Cravat using VCF files (coming soon)
- Genome Browser IGV (coming soon)
- Other tools coming soon
REMBRANDT (REpository for Molecular BRAin Neoplasia DaTa) Dataset
Description: The REMBRANDT dataset was originally created at the National Cancer Institute and funded by Glioma Molecular Diagnostic Initiative. The data was collected from 2004-2006. In 2015, the NCI transferred this dataset to Georgetown. The dataset is accessible for conducting clinical translational research using the open access Georgetown Database of Cancer (G-DOC) (new window) platform. In addition, the raw and processed genomics and transcriptomics data have also been made available via the public NCBI GEO repository as a super series GSE108476 (new window). Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes in patients alongside clinical outcomes (overall survival) using this large brain cancer study
Raw data: GSE108476
MRI images: The Cancer Imaging Archive (TCIA) (new window)
precisionFDA Brain Cancer Predictive modeling challenge: The precisionFDA (new window), the Georgetown Lombardi Comprehensive Cancer Center (new window) and The Innovation Center for Biomedical Informatics at Georgetown University Medical Center launched and executed the Brain Cancer Predictive Modeling and Biomarker Discovery Challenge; which ran from November 2019 to February 2020. The challenge asked participants to develop machine learning and/or artificial intelligence models to identify biomarkers and predict patient outcomes using gene expression, DNA copy number, and clinical data. For more details about the challenge and its results read here.
- Gusev Y, Bhuvaneshwar K, Song L, Zenklusen JC, Fine H, Madhavan S. The REMBRANDT study, a large collection of genomic data from brain cancer patients. Nature Scientific Data, Aug 2018. PMID: 30106394 (new window)
- Madhavan S, Zenklusen JC, Kotliarov Y, Sahni H, Fine HA, Buetow. Rembrandt: helping personalized medicine become a reality through integrative translational research. Molecular Cancer Research. Feb 2009. PMID19208739 (new window)
Stage II Colorectal Cancer – Multi-omics Molecular Profiling Dataset
Description: Colorectal cancer (CRC) patient biospecimens with extensive clinical and follow-up data were selected from the Indivumed GmbH biobank for 40 patients (20 relapse and 20 no-relapse). The patients consisted of 12 with late stage I, and 28 with stage II. Four patients (out of 12) with late stage I had experienced relapse (~33%), and it is important to note that 12 patients (out of 28) with stage II were relapse-free (~43%). Therefore, the relapse-free group of samples, and the group with relapse are both represented by a mixture of late stage I and stage II patients. Only nine stage II patients (out of 28) had rectal cancer; of these 6 had relapsed within 5 years. Of more than 180 clinical attributes, 64 were shortlisted based on relevance to clinical outcome and biomarker analysis. The molecular data included gene expression, DNA copy number, microRNA, serum and urine metabolites.
Data Formats: (Data access coming soon )
- Subha Madhavan,* Yuriy Gusev, Thanemozhi G. Natarajan, Lei Song, Krithika Bhuvaneshwar, Robinder Gauba, Abhishek Pandey, Bassem R. Haddad, David Goerlitz, Amrita K. Cheema, Hartmut Juhl, Bhaskar Kallakury, John L. Marshall, Stephen W. Byers, and Louis M. Weiner. Genome-wide multi-omics profiling of colorectal cancer identifies immune determinants strongly associated with relapse. Frontiers in Genetics, Nov 2013, 4: 236. PMC3834519 (new window)
If you have used G-DOC in your research and would like to cite this informatics platform, please use the following peer-reviewed articles:
- Madhavan S, Gusev Y, Harris M, Tanenbaum DM, Gauba R, Bhuvaneshwar K, Shinohara A, Rosso K, Carabet L, Song L, Riggins RB, Dakshanamurthy S, Wang Y, Byers SW, Clarke R, Weiner LM. G-DOC®: A Systems Medicine Platform for Personalized Oncology. Neoplasia 13:9. (Sep 2011). PMID: 21969811 (new window)
- Krithika Bhuvaneshwar, Anas Belouali, Varun Singh, Robert M Johnson, Lei Song, Adil Alaoui, Michael A Harris, Robert Clarke, Louis M Weiner, Yuriy Gusev, Subha Madhavan. G-DOC Plus – an integrative bioinformatics platform for precision medicine. BMC Bioinformatics (April2016). PMID: 27130330 (new window)