CellPress is showcasing a collection of CTD² Network’s articles which will help develop precision therapeutics in cancer. Register for the Cell-NCI Symposium: Beyond Cancer Genomics Toward Precision Oncology taking place from October 4-6, 2021.
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
ARACNe is an algorithm for inferring direct regulatory relationships between transcriptional regulator proteins and target genes. This method uses microarray expression profiles to reconstruct tissue-specific gene regulatory transcriptional interactions in cellular networks. This tool could be used by researchers to determine novel driver genes and drug mechanisms of action.
ATARiS is a computational method designed to analyze the off-target effects in the data generated fromRNAi screens. RNAi reagents designed to target the same gene often induce different degrees of on-target and off-target gene suppression, resulting in inconsistent phenotypes. To address this, ATARiS tries to identify subsets of its RNAi reagents that produce a significantly similar phenotype across the screened samples. This approach also computes a consistency score for each reagent that represents the confidence that its observed phenotypic effects are the result of on-target gene suppression.