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.2 Analytical Tools
EDDY is a statistical test for estimating differential dependencies for a set of genes between two conditions. Dependencies can be represented and assessed graphically for the expression of a gene set within a particular cellular context. EDDY then calculates the divergence between the probability distributions of scored graphs for each condition. Finally, the statistical significance of this divergence is computed.
Analysis of subset of the Cancer Therapeutic Response Portal (CTRP) transcriptome and drug screening data from 810 cancer cell lines was performed using the Evaluation of Differential DependencY (EDDY) algorithm.This analysis identified pathways enriched for differential dependencies between sensitive and non-sensitive cell-lines to each compound as well as potential novel targets, termed “mediators”. These results can be accessed using the following URL.