1 - 13 of 13 results


Provides a web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. cBioPortal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes.


Authorizes to retrieve, assemble and process public data from The Cancer Genome Atlas (TCGA). TCGA-assembler is an open source software which is composed of two modules: (i) the first module earns the public data including TCGA somatic mutation and proteomics data and gathers the individual files into local data tables, and (ii) the second module furnishes multiple features for preparing information for downstream analysis such as a way for separating different types of measurements into their own data tables.


Aids in querying, downloading, analyzing and integrating The Cancer Genome Atlas (TCGA) data. TCGAbiolinks can: i) facilitate the TCGA open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) allow user to download a specific version of the data and thus to easily reproduce earlier research results. It provides multiple methods for analysis and methods for visualization in order to easily develop complete analysis pipelines.


An open source software package to obtain the TCGA data, wrangle it, and pre-process it into a format ready for multivariate and integrated statistical analysis in the R environment. In a user-friendly format with one single function call, our package downloads and fully processes the desired TCGA data to be seamlessly integrated into a computational analysis pipeline. No further technical or biological knowledge is needed to utilize our software, thus making TCGA data easily accessible to data scientists without specific domain knowledge.


A software tool that integrates multi-resource omics data. CrossHub was designed to analyze TCGA transcriptomic and epigenomic data in the context of ENCODE, Jaspar and various miRNA target prediction algorithms. This approach is intended to reveal gene expression regulation mechanisms such as methylation, transcription factor (TF)-mediated transcription repression/activation and microRNA interference. CrossHub has a scalable design intended to analyze more various cancer types available in TCGA. This tool may be a starting point for integrating the data of several major projects such as TCGA and ENCODE.


Provides data structures and methods to represent, manipulate, and integrate multi-assay genomic experiments. MultiAssayExperiment permits to integrate any data class that supports basic subsetting and dimension names. It supports many data classes by default without additional accommodations. The tool was used to visualize the overlap in assays performed for adrenocortical carcinoma patients. It had permit to confirm correlations between somatic mutation and copy number burden in colorectal cancer and breast cancer.


Allows the integration and visualization of the expression, DNA methylation and clinical TCGA (The Cancer Genome Atlas) data on a single-gene level. MEXPRESS was designed after the principles of graphical excellence to ensure that the complex and multidimensional TCGA data would be presented in a clear, precise and efficient way to the user. MEXPRESS was therefore developed to have virtually no learning curve, allowing especially clinical researchers to get their results fast without having to invest time in learning yet another tool. MEXPRESS offers a unique set of features, including its ease of use and the integrated visualization of different data types over hundreds of samples. It may therefore help to quickly test hypotheses that concern the discovery of DNA methylation or expression-based biomarkers.


Allows users to systematically access Firehose pre-processed data, and to organize it for easy management and analysis. The library also allows users to create data matrices from TCGA data, without any pre-processing. RTCGAToolbox can also access the Firehose analysis pipeline to get GISTIC2 results for questions related to copy number data. In addition, basic analysis functions of RTCGAToolbox facilitate basic comparisons and analyses as well as visualization without having to call external tools. The RTCGAToolbox can also be integrated with other analysis pipelines for further data processing.

EDGE in TCGA / Exploring drivers of gene expression in TCGA cancer genomes

Facilitates rapid exploration of the drivers of gene expression in TCGA. EDGE in TCGA offers a way to compare the relative effects of these genetic and epigenetic drivers of gene expression for each gene and cancer type. The goal of this analysis application was to partition the variance in gene expression (for each gene) due to promoter methylation, somatic mutations, copy number alterations, microRNA abundance, transcription factor expression, and germ-line genetic variability.


A web based, freely accessible online tool, which can also be run in a private instance, for integrated analysis of molecular cancer data sets provided by TCGA. In contrast to already available tools, Web-TCGA utilizes different methods for analysis and visualization of TCGA data, allowing users to generate global molecular profiles across different cancer entities simultaneously. In addition to global molecular profiles, Web-TCGA offers highly detailed gene and tumor entity centric analysis by providing interactive tables and views. The user can focus analyses on results from single genes and cancer entities or perform a global analysis (multiple cancer entities and genes simultaneously).


Allows investigation of molecular interactions in cancer. Zodiac integrates big-data analysis on multimodal TCGA data and produces knowledge of molecular interactions in cancer. It is based on Bayesian graphical models. This tool contains a whole-genome and pair-wise interaction map, which offers intragenic and intergenic interactions of all pairs of genes in cancer. It is composed of the following features: data retrieval, computation, results assembly, and results dissemination.