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.

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


Deconvolution Analysis of RNAi Screening Data (DecoRNAi) (University of Texas Southwestern Medical Center)

A major challenge of the large-scale siRNA and shRNA loss-of function screens is off-target effects resulting from short regions (~6 nucleotides) of oligonucleotide complementary to many different mRNAs. DecoRNAi is a computational approach that could be used for identification and correction of the off-target effects in the primary RNAi screening data sets.

For questions, please contact Yang Xie: (
DEMETER2 (Dana-Farber Cancer Institute)

DEMETER2 is a computation method that estimates gene dependencies by integrating data from large-scale RNAi screens (targeting up to the whole transcriptome) with read-out of cell viabilities performed in cancer cell lines. This method infers gene dependency estimates and allows for corrections to eliminate batch effects and confounders due to gene amplifications.

For questions, please contact Aviad Tsherniak: (
DepMap (Dana-Farber Cancer Institute)

DepMap is a comprehensive preclinical reference portal that connects tumor features with genetic and small molecule dependencies. This Portal could be used to understand the vulnerabilities of cancer, identify genetic targets for therapeutic development, and patient stratification.

For questions, please contact this email: (
Detecting Mechanism of Action based Network Dysregulation (DeMAND) (Columbia University)

The DeMAND algorithm elucidates mechanisms of action of cellular perturbations (e.g. small-molecule) by analyzing network dysregulations. This approach predicts drug mechanisms of action 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.

For questions, please contact Andrea Califano: (
Differential Allelic Cis-regulatory Effects-scan (DACRE-scan) (University of Texas MD Anderson Cancer Center)

DACRE-scan is a statistical tool that deconvolutes and integrates tumor DNA and RNA profiles from matched whole-exome and whole-transcriptome tissue sequencing data. This tool is being used to discover functional variants (somatic and germline) that are subject to differential allelic cis-regulatory effects.

For questions, please contact Ken Chen: (
Driver-gene Inference by Genetical-Genomics and Information Theory (DIGGIT) (Columbia University)

Master regulators (MR) are transcription factors that control the majority of genes differentially expressed between two molecular phenotypes. Genomic alterations that contribute to aberrant MR activity must be upstream of the MR, although the specific pathways involved may not be known. 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 master regulators and drive cellular phenotypes.

For questions, please contact Andrea Califano: (