The cBioPortal for Cancer Genomics is an open-access, open-source resource for interactive exploration of multidimensional cancer genomics data sets. The goal of cBioPortal is to significantly lower the barriers between complex genomic data and cancer researchers by providing rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects, and therefore to empower researchers to translate these rich data sets into biologic insights and clinical applications.
Oncoscape is a data visualization and analysis platform for clinical and molecular data. By combining your data with reference datasets and renowned statistical libraries, Oncoscape accelerates research. Discover patterns in your molecular data through 3D scatter plots and heatmaps powered by dozens of clustering, classification, regression and dimension reduction algorithms. Overlay your data on thousands of biological pathways and identify genomic regions of interest. Explore clinical timelines, predict patient survival and summarize cohorts through a dashboard. Build cohorts by explicitly supplying criteria, or through interactive exploration. Combine charts to correlate patient, sample and genomic data. All analysis in Oncoscape is fully reproducible and available to the public at oncoscape.v3.sttrcancer.org
BEERE is the abbreviation of Biomedical Entity Expansion, Ranking, and Explorations, which can help biomedical researchers investigate the relevant significance of genes, proteins, and general biomedical concepts—biomedical entities—among one another from public knowledge-base of protein/gene interactions and extracted semantic relationships. BEERE aims to assist users to quickly evaluate and prioritize a list of genes or terms, i.e., “entities”, based on our established network ranking technique (1) which can take advantage of a network of probabilistic functional relationships extracted a priori. It also provide the significance of genes in functional gene set or networks.
Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. PAGER help cancer researchers gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, there are 84,282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug–gene, miRNA–gene interactions, pathways and tissue-specific gene expressions. Its web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. The tool is freely accessible at http://discovery.informatics.uab.edu/PAGER/.
The NCI Division of Cancer Biology supports multiple research programs composed of interdisciplinary communities of scientists who aim to integrate approaches, data, and tools to address important questions in basic and translational cancer research. Discover and download datasets, publications, and other resources generated by these programs.