Texomer is a statistical approach that integrates bulk whole exome and whole transcriptome sequencing data obtained from patient tissue samples and estimates tumor purity, intra-tumor heterogeneity, etc. This tool potentially improves molecular characterization and functional variant prediction of cancer samples.
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
The Cancer Genome Atlas (TCGA) Clinical Explorer is a web and mobile interface for identifying clinical – genomic driver associations. The Clinical Explorer interface provides a platform to query TCGA data using the following methods: 1) searching for clinically relevant genes, microRNAs, and proteins by name, cancer types, or clinical parameters, 2) searching for genomic and/or proteomic profile changes by clinical parameters, or 3) testing two-hit hypotheses.
Functional proteomics comprises a large-scale study of functional activity (e.g. expression, modificatins etc) of the proteins. TCPA is an interactive webinterface that enables researchers to analyze and visualize functional proteomic data of The Cancer Genome Atlas (TCGA) tumor smaples. This resource provides a unique opportunity to validate the findings from TCGA data and identify model cell lines for functional investigation. TCPA currently provides six modules: Summary, My Protein, Visualization, and Analysis.