Analysis Tools

Featured Tools

cBioPortal

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

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

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.

UALCAN

UALCAN is a comprehensive, user-friendly, and interactive web resource for analyzing cancer OMICS data. It is built on PERL-CGI with high quality graphics using javascript and CSS. UALCAN is designed to, a) provide easy access to publicly available cancer OMICS data (TCGA and MET500), b) allow users to identify biomarkers or to perform in silico validation of potential genes of interest, c) provide graphs and plots depicting gene expression and patient survival information based on gene expression, d) evaluate gene expression in molecular subtypes of breast and prostate cancer, e) evaluate epigenetic regulation of gene expression by promoter methylation and correlate with gene expression, f) perform pan-cancer gene expression analysis, and g) Provide additional information about the selected genes/targets by linking to HPRD, GeneCards, Pubmed, TargetScan, The human protein atlas, DRUGBANK, Open Targets and the GTEx. These resources allow researchers to gather valuable information and data about the genes/targets of interest.

PAGER

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/.

Cancer Complexity Knowledge Portal

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.