{"_id":"55faf11da62ba1170021a9ac","__v":1,"project":"55faf11ba62ba1170021a9a7","tags":[],"initVersion":{"_id":"55faf11ba62ba1170021a9aa","version":"1.0"},"user":{"_id":"554290cd6592e60d00027d17","username":"","name":"Seven Bridges"},"createdAt":"2015-09-17T16:58:00.000Z","changelog":[{"_id":"56044c3a90ee490d00440542","update":"","type":"added"}],"body":"The Cancer Genomics Cloud (CGC) [project](https://cbiit.nci.nih.gov/ncip/nci-cancer-genomics-cloud-pilots) was born out of the recognition that as the biological research enterprise grows increasingly computationally-intensive, new approaches are required to support effective data discovery, storage, and computation. This is especially true when we consider large, publicly-funded projects such as The Cancer Genome Atlas ([TCGA](http://cancergenome.nih.gov/)). Slated to reach 2.5 petabytes of multidimensional data upon completion, simply downloading the TCGA dataset to local machines for computation would require several [weeks](http://ncip.nci.nih.gov/blog/computational-needs-for-large-scale-data-analysis-towards-a-cancer-knowledge-cloud/) even with highly optimized transfer rates. Once downloaded, researchers lucky enough to have access to the computational resources capable of analyzing the data must still store the data locally, an obstacle for anyone who doesn’t happen to have a couple of petabytes of free storage in their back pocket. These challenges will only increase in severity over time as sequencing data becomes less expensive and data volumes inflate steadily (see below).\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/HLzZ0jNqRZW3Tf2zVIvh_cgc-blog%201.jpg\",\n        \"cgc-blog 1.jpg\",\n        \"1832\",\n        \"831\",\n        \"#4c5c7d\",\n        \"\"\n      ]\n    }\n  ]\n}\n[/block]\nThe CGC project aims to directly address these issues by co-localizing computational tools and resources with data in the cloud, thus relieving the burden on local computation infrastructure. We are [delighted](http://blog.sbgenomics.com/cancer-genomics-cloud-pilot/) to be one of three organizations selected by the [National Cancer Institute](http://www.cancer.gov/) (along with the [Broad Institute](http://www.broadinstitute.org/news/6166) and the [Institute for Systems Biology](https://www.systemsbiology.org/news/isb-gets-65-million-nci-create-%E2%80%98cancer-genomics-cloud%E2%80%99-partners-google-and-sra-international)) to build pilot platforms able to meet the next generation of challenges in genomics research.\n\n## This is a hard problem and your feedback is crucial.\n\nInvolvement of the cancer research community (that’s you!) is [central](http://ncip.nci.nih.gov/blog/invitation-for-public-comment-on-analysis-and-data-priorities-for-the-nci-cancer-genomic-cloud-pilots/) to our goal of providing a resource able to accelerate biomedical discovery.  This blog, as well as our twitter feed [@genomicscloud](https://twitter.com/genomicscloud), represents our approach to sharing CGC development activities, highlighting exciting cancer genomics research, and most importantly, allowing you to give feedback about what features of the CGC will best serve your research aims.\n\nWe intend this to be a community driven resource and welcome your comments, topic suggestions, and guest posts! Your response to the poll questions on the right will be used to help shape development of the CGC and will ultimately enable us to create the resource that is best suited to needs of the community overall. We are excited for you to join us in this project and hope you will help us to expand the community by contributing and sharing this blog with your colleagues.\n\n## Let’s get started.","slug":"welcome-to-the-cgc-knowledge-center","title":"Welcome to the Cancer Genomics Cloud!"}

Welcome to the Cancer Genomics Cloud!


The Cancer Genomics Cloud (CGC) [project](https://cbiit.nci.nih.gov/ncip/nci-cancer-genomics-cloud-pilots) was born out of the recognition that as the biological research enterprise grows increasingly computationally-intensive, new approaches are required to support effective data discovery, storage, and computation. This is especially true when we consider large, publicly-funded projects such as The Cancer Genome Atlas ([TCGA](http://cancergenome.nih.gov/)). Slated to reach 2.5 petabytes of multidimensional data upon completion, simply downloading the TCGA dataset to local machines for computation would require several [weeks](http://ncip.nci.nih.gov/blog/computational-needs-for-large-scale-data-analysis-towards-a-cancer-knowledge-cloud/) even with highly optimized transfer rates. Once downloaded, researchers lucky enough to have access to the computational resources capable of analyzing the data must still store the data locally, an obstacle for anyone who doesn’t happen to have a couple of petabytes of free storage in their back pocket. These challenges will only increase in severity over time as sequencing data becomes less expensive and data volumes inflate steadily (see below). [block:image] { "images": [ { "image": [ "https://files.readme.io/HLzZ0jNqRZW3Tf2zVIvh_cgc-blog%201.jpg", "cgc-blog 1.jpg", "1832", "831", "#4c5c7d", "" ] } ] } [/block] The CGC project aims to directly address these issues by co-localizing computational tools and resources with data in the cloud, thus relieving the burden on local computation infrastructure. We are [delighted](http://blog.sbgenomics.com/cancer-genomics-cloud-pilot/) to be one of three organizations selected by the [National Cancer Institute](http://www.cancer.gov/) (along with the [Broad Institute](http://www.broadinstitute.org/news/6166) and the [Institute for Systems Biology](https://www.systemsbiology.org/news/isb-gets-65-million-nci-create-%E2%80%98cancer-genomics-cloud%E2%80%99-partners-google-and-sra-international)) to build pilot platforms able to meet the next generation of challenges in genomics research. ## This is a hard problem and your feedback is crucial. Involvement of the cancer research community (that’s you!) is [central](http://ncip.nci.nih.gov/blog/invitation-for-public-comment-on-analysis-and-data-priorities-for-the-nci-cancer-genomic-cloud-pilots/) to our goal of providing a resource able to accelerate biomedical discovery. This blog, as well as our twitter feed [@genomicscloud](https://twitter.com/genomicscloud), represents our approach to sharing CGC development activities, highlighting exciting cancer genomics research, and most importantly, allowing you to give feedback about what features of the CGC will best serve your research aims. We intend this to be a community driven resource and welcome your comments, topic suggestions, and guest posts! Your response to the poll questions on the right will be used to help shape development of the CGC and will ultimately enable us to create the resource that is best suited to needs of the community overall. We are excited for you to join us in this project and hope you will help us to expand the community by contributing and sharing this blog with your colleagues. ## Let’s get started.