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.

2 Analytical Tools
A | C | D | E | F | G | M | O | P | R | S | T | V

V

Virtual Inference of Protein-activity by Enriched Regulon analysis (VIPER) (Columbia University)

VIPER algorithm allows computational inference of protein activity on an individual sample from the gene expression data. Methods to measure protein abundance on a proteome-wide scale using arrays or mass spectrometry technologies cover only a fraction of proteins, requiring large amounts of tissue, and does not directly capture protein activity. This approach uses the transcripts most directly affected by the activity of the protein and ranks relative protein activity on a sample-by-sample basis by transforming a gene expression matrix into a protein activity matrix.

For questions, please contact Andrea Califano: (ac2248@cumc.columbia.edu)
Vizome (Oregon Health and Science University (1))

Beat AML study is a groundbreaking collaborative clinical study integrating genomics with data on acute myeloid leukemia (AML) patient sample sensitivity to a panel of novel targeted therapies. Vizome, Beat AML data viewer allows easy access to clinical, genomic, transcriptomic and functional analyses of AML samples. This tool could be used to predict novel treatment options.

For questions, please contact Jeffrey Tyner: (tynerj@ohsu.edu)