Analytical Tools

The CTD² Network develops new approaches to identify novel targets and functionally validate discoveries made from large-scale genomic initiatives, such as The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Cancer Genome Characterization Initiative (CGCI), and advance them toward precision medicine. Through robust cross-Network collaborations, CTD² (1) mines data to find alterations that potentially influence tumor biology, (2) characterizes the functional roles of candidate alterations in cancers, and (3) identifies novel approaches that target causative alterations either directly or indirectly. Methodologies include bioinformatics, genome-wide gain- and loss-of-function screening, and small molecule high-throughput screening, among others.

Part of the CTD² mission is to make data and tools available and accessible to the greater research community to accelerate the discovery process. Bioinformatics support is often required for analyses of the massive datasets used and generated through experimental pipelines employed by the Network Centers. To facilitate the processes of mining, visualizing, analyzing, and using such datasets, OCG has curated this collection of analytical tools. OCG/CTD² does not endorse any specific tool. However, this list gives researchers a gateway to access many tools that are useful for analyzing and/or visualizing large-scale genomic and/or complex datasets generated through high-throughput screens and other assays.

3 Analytical Tools
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GENE-E is a tool that allows users to visual matrix-based data, for example, cell lines in columns and cell line features in rows. The program filters and sorts data by mutation status or other criteria chosen by the user and creates ranked links.

Gene-wise Prior Bayesian Group Factor Analysis (GBGFA) (Fred Hutchinson Cancer Research Center (1))

GBGFA explicitly models gene-centric dependencies when integrating genomic alterations data of the same gene from different platforms (e.g. copy number variation, gene expression and mutation data) to prioritize genes supported by multiple inputs. The multitask approach of this algorithm provides the ability to leverage similarities in the response profiles of drug groups, that are more likely to correspond to true biological effects.

For questions, please contact Olga Nikolova: (

geWorkbench is an open source bioinformatics application that provides access to an integrated suite of tools for the analysis and visualization of data from a wide range of genomic domains (gene expression, sequence, protein structure and systems biology).