The Cancer Genomics Cloud (CGC), powered by Seven Bridges and funded by the National Cancer Institute, is a flexible cloud resource platform that enables storage, analysis, and computation of large cancer datasets in the cloud. Since its launch in 2016, the platform has been continuously iterated with new applications and features to address the exponential growth and diversity of complex datasets. With the CGC, any user with an account can easily access petabytes of data, share it, analyze and use the computational power of the cloud without having to learn how to program and get familiar with several different data portals. 

The CGC provides a unified platform for cancer data analysis by co-localizing three components within the same cloud platform: 1) large cancer datasets including ‘omics, imaging, and several other datasets; 2) >600 bioinformatics tools and best-practice optimized workflows for analyzing multi-omics data; and 3) the computational capabilities to do large-scale analyses. The user-friendly portal of the CGC allows researchers to browse, query and filter datasets of interest and also bring their own data for collaborative analysis in the context of other publicly available data. In addition to the simplicity of data access and management, the CGC provides the flexibility to bring privately developed tools, and the ability to complete reproducible and interactive analyses, all with the speed of cloud computing resources without needing any cloud provider accounts or managed billing. Without downloading data to local computers, users can then continue their work in the cloud using Jupyter notebooks or RStudio packages using the CGC data cruncher interactive analysis functionality, and share results easily. 

Start working now

Join the more than 6,000 other researchers from across the globe who are currently using the CGC to explore and analyze TCGA and other large cancer datasets. Get started by logging in below.

 

Learn About the CGC

 

Need Help?

Begin with our convenient Tutorials and detailed Knowledge Center for more information about how the CGC can help you learn from large omics datasets faster.  

 

Credits to support your research 

An important goal of the CGC is to understand how researchers can use cloud computing resources to analyze their data. Unlike traditional models, where the cost of compute and data storage is paid up front, on the CGC you only incur costs as you run an analysis.

However, we've found that it can be daunting to try to learn a system while worrying about analysis costs. For this reason, the NCI has generously provided substantial funds to support your compute and storage on the CGC as you are getting familiar with the platform. When you create an account on the CGC, you'll be automatically granted $300 in credits. Have a big project in mind? Submit a collaborative project request to access up to $10,000 in credits.

Of course, once you've used the credits, you can contact us to create a billing group that can be supported via a credit card or purchase order.

 

Publications

Explore a growing list of publications describing how the CGC is being used to support new and emerging research.

How to Cite Us

When publishing papers that utilized the CGC, please reference our funding below and cite our paper:

Lau et al (2017) The Cancer Genomics Cloud: Collaborative, Reproducible, and Democratized—A New Paradigm in Large-Scale Computational Research. Cancer Res. 77(21):e3-e6. doi: 10.1158/0008-5472.CAN-17-0387.

 

LATEST UPDATES

Featured
Release notes

PyImageJ: Python wrapper for ImageJ2 project is public on CGC

The PyImageJ: Python wrapper for ImageJ2 project that serves as a comprehensive tutorial for users interested in leveraging PyImageJ for image analysis and processing is now publicly available on CGC. It features one Data Studio interactive analysis, written in Python, with step-by-step demonstrations and examples showcasing the integrative capabilities of PyImageJ.

UmetaFlow: Untargeted Metabolomics Workflow for Data Processing and Analysis published

We've just published the UmetaFlow: Untargeted Metabolomics Workflow for Data Processing and Analysis Public Project. This project was developed in collaboration with the OpenMS team and serves as a comprehensive tutorial for users interested in metabolomics data analysis. It includes a Data Studio interactive analysis, written in Python, with step-by-step demonstrations and examples highlighting UmetaFlow's capabilities.
UmetaFlow utilizes the pyOpenMS package, a Python wrapper for the OpenMS algorithms, allowing integration with other commonly used Python data science modules. This allows for interactive computing, easy data exploration and visualization, and rapid prototyping of new analytical steps.

Variant Annotation Apps Public Project published

Exciting news about an update to one of our public projects. We recently published the Variant Annotation Apps public project which is meant to replace the Variant Browser. This project is a starting point for users to annotate their VCF files and contains public files and apps to get started.  

FusionCatcher 1.33 now available

FusionCatcher tool has been published on the CGC. FusionCatcher identifies somatic novel or known fusion genes, translocations, and chimeras within RNA-seq data.

exceRpt WF published

The exceRpt pipeline has been published on the CGC. The WF performs preprocessing and identification of smallRNAs, and results visualisation. 

Cell Ranger tools published

Cell Ranger Aggr, Cell Ranger Reanalyze, Cell Ranger VDJ and Cell Ranger Count tools from Cell Ranger toolkit v8.0.1 are published on the CGC. 

Cell Ranger is a set of analysis pipelines that perform sample demultiplexing, barcode processing, single cell 3' and 5' gene counting, V(D)J transcript sequence assembly and annotation, and Feature Barcode analysis from single cell data.

Severus and GeneFuse published

Severus 1.1 and GeneFuse 0.8.0 have been published on the CGC. 

Severus 1.1 calls somatic structural variants in long reads data, while GeneFuse detects and visualizes known gene fusions from FASTQ files.

JuLI and FuSeq_WES apps published

JuLI (0.1.6.2) and FuSeq_WES (1.0.0, 4c55ebb) have been published on the CGC. 

JuLI and FuSeq_WES are callers for gene fusions in targeted/WES data. JuLI controlpanel and FuSeq_WES Prepare References utility tools have also been published.

Release notes

Here are the latest tool updates in the Public Apps gallery on the CGC:

  • Regenie - Whole genome regression analysis tool.

  • Vcfanno - Variant annotator.

  • SHAPEIT5 - Phasing toolkit that includes: SHAPEIT5 phase_common, SHAPEIT5 phase_rare, SHAPEIT5 ligate.

  • PLINK2 - Population genetics toolkit.

  • UMI-tools - UMIs manipulation toolkit: UMI-tools Count-tab, UMI-tools Count, UMI-tools Group, UMI-tools Dedup, UMI-tools Extract, UMI-tools Whitelist.

  • BCFtools: BCFtools Annotate, BCFtools Call, BCFtools CNV, BCFtools Concat, BCFtools Consensus, BCFtools Convert, BCFtools CSQ, BCFtools Filter, BCFtools GTcheck, BCFtools Index, BCFtools Isec, BCFtools Mpileup, BCFtools Query, BCFtools Reheader, BCFtools Roh, BCFtools Sort, BCFtools Stats, BCFtools View, BCFtools Merge, BCFtools Norm.

  • Indexcov - Fast QC checks from BAI/CRAI files.

  • Variant Effect Predictor - Variant annotator.

  • AnnotSV - Annotation and ranking of SV variants.

  • Personal Cancer Genome Reporter - Functional annotation and classification of somatic variants.

Release notes

R API client library - sevenbridges2

We are excited to announce the release of our new R API client library - sevenbridges2, version 0.2.0. The name might not be groundbreaking, but our revamped R API client library certainly is! This initiative was driven by the need to modernise our R API offering, ensuring a more efficient and feature-rich experience for our users. Here are the key highlights and improvements: 

  • Modernisation and Optimisation: The new sevenbridges2 R API client leverages R6 classes to enhance performance and maintainability. It features optimisations that reduce RAM usage and enhance runtime performance. 

  • Expanded API Coverage: Initially focusing on essential functionalities such as Users, Projects, Files, Volumes, Billing, Apps, Tasks, and Bulk Actions, this release sets the foundation for further development. Future updates will include advanced features like async and enterprise-level actions to enrich our API capabilities. 

  • User Experience: The new client introduces intuitive methods designed to enhance usability across various user scenarios, from local scripting to R Shiny app development, making interactions with our platform more efficient and scalable. 

  • Enhanced Documentation: Unlike its predecessor, the sevenbridges2 R API client library includes comprehensive roxygen documentation for each method, providing clear and detailed explanations that enhance usability and streamline maintenance.

Now available for installation: 

  • CRAN: Install the sevenbridges2 package using install.packages("sevenbridges2") (available starting 2024-07-01). 

  • GitHub: Explore the latest updates and contribute on our GitHub repository: https://github.com/sbg/sevenbridges2 

 

UPCOMING EVENTS

 

Join us 

Join us at our virtual Office Hours and ask any questions you may have and get help directly from Seven Bridges experts. We hold Office Hours on Tuesdays and Thursdays every week. Users on all platforms are encouraged to attend with any questions they may have. Click here to join the 10:00 am ET Tuesday and 2:00 pm ET Thursday office hours hosted by Dave Roberson.

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ACknowledgements

We are delighted to be working with fellow award recipients at the The Broad Institute and The Institute for Systems Biology.

Funding

The Seven Bridges Cancer Research Data Commons Cloud Resource has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, Contract No. HHSN261201400008C and ID/IQ Agreement No. 17X146 under Contract No. HHSN261201500003I and 75N91019D00024.