ARACNe uses expression profiles to reconstruct tissue-specific gene regulatory transcriptional interactions in cellular networks. This tool eliminates the vast majority of false positive transcriptional interactions typically inferred by pairwise gene expression correlation analysis.
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.
ATARiS is a computational method designed to reduce the background (off-target effects) in data from phenotypic screens performed using multiple RNAi reagents.
CTRP hosts data that are generated by measuring cellular responses to an 'Informer Set' of small-molecule probes and drugs. Users can mine for lineages or mutations enriched among cell lines sensitive to small molecules. By connecting cellular features to small molecule sensitivities, CTRP identifies new potential therapeutic vulnerabilities for different cancer types.
The cBioPortal is a web resource for exploring, visualizing, and analyzing complex multidimensional cancer genomics datasets. Researchers can interactively explore genetic alterations across samples, genes, and pathways and link these to clinical outcomes, when available. The portal facilitates discoveries by making large and complex cancer genomics profiles accessible to researchers and clinicians without bioinformatics expertise.
Studies have shown that genome-wide CRISPR-Cas9 inactivation of genes that are amplified need different analytical approaches for interpretation of the results. The Cas9 induces double strand breaks which lead to false-positive results. A computational method, CERES was developed for inferring gene essentiality from genome-wide CRISPR-Cas9 screens in cancer cell lines to correct the copy number effect. This approach decreases the false-positive results while taking into account the anti-proliferative copy-number effect.
CARMEN is a point-and-click application which permits discovery of significant relationships using gene expression data, generating gene pathway maps, and finding differential expression between two conditions. CARMEN allows researchers to perform expression analysis using their own data and publically available data.
Cytoscape allows users to visualize networks and related information derived from complex datasets. Data in the CTD2 Data Portal can be downloaded and viewed with Cytoscape, which eliminates the need to install additional visualization software. The program is flexible about the format of input files.
DecoRNAi is a computational approach for automated quantitation and annotation of the off-target effects in primary RNAi screening datasets.
The DeMAND algorithm elucidates the mechanisms of action of cellular perturbations (for example, by small molecules or shRNA) using gene expression data generated from control and perturbed cells. The data are then used to identify network dysregulation to determine both the interactions and the genes that are involved in the mechanism of action.
The DIGGIT package integrates patient-matched genomic mutation and gene expression data with corresponding gene regulatory networks to identify candidate driver mutations that are upstream of and directly perturb master regulators. Master regulators are transcription factors that control the majority of genes differentially expressed between two molecular phenotypes.