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.

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

D

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: (ac2248@cumc.columbia.edu)
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: (kchen3@mdanderson.org)
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: (ac2248@cumc.columbia.edu)

E

Evaluation of Differential DependencY (EDDY) (Translational Genomics Research Institute)

EDDY is a statistical test for estimating differential dependencies for a set of genes between two conditions. Dependencies can be represented and assessed graphically for the expression of a gene set within a particular cellular context. EDDY then calculates the divergence between the probability distributions of scored graphs for each condition. Finally, the statistical significance of this divergence is computed.

For questions, please contact Gil Speyer: (gspeyer@tgen.org)
Evaluation of Differential DependencY-Cancer Therapeutic Response Portal (EDDY-CTRP) (Translational Genomics Research Institute)

Analysis of subset of the Cancer Therapeutic Response Portal (CTRP) transcriptome and drug screening data from 810 cancer cell lines was performed using the Evaluation of Differential DependencY (EDDY) algorithm.This analysis identified pathways enriched for differential dependencies between sensitive and non-sensitive cell-lines to each compound as well as potential novel targets, termed “mediators”. These results can be accessed using the following URL.

For questions, please contact Gil Speyer: (gspeyer@tgen.org)

F

Functional Annotation of Somatic Mutations in Cancer (FASMIC) (University of Texas MD Anderson Cancer Center)

FASMIC is an interactive and open-access web portal for comprehensively querying and visualizing mutation-associated data. The queried gene is displayed in a tabular view with basic information for each mutation and details like summary (gene name, mutation, etc.), 3D structure, literature, mutation frequency etc. under the table.

For questions, please contact Han Liang: (hliang1@mdanderson.org)
Functional Signature Ontology (FuSiOn) (University of Texas Southwestern Medical Center)

FuSiOn is an ontology map built from gene expression data resulting from human kinome perturbation screens using miRNA mimics, shRNAs, and natural products. These maps link bioactive molecules to the proteins and biological processes that they engage in cells. This tool can be used to search for chemical or genetical perturbagens that behave functionally similarly to target a gene of interest.

For questions, please contact John MacMillan: (john.macmillan@utsouthwestern.edu)

G

GENE-E

GENE-E is a tool that allows users to visual matrix-based data, for example, cell lines in columns and cell line features in rows. The program filters and sorts data by mutation status or other criteria chosen by the user and creates ranked links.

Gene-wise Prior Bayesian Group Factor Analysis (GBGFA) (Fred Hutchinson Cancer Research Center (1))

GBGFA explicitly models gene-centric dependencies when integrating genomic alterations data of the same gene from different platforms (e.g. copy number variation, gene expression and mutation data) to prioritize genes supported by multiple inputs. The multitask approach of this algorithm provides the ability to leverage similarities in the response profiles of drug groups, that are more likely to correspond to true biological effects.

For questions, please contact Olga Nikolova: (nikolova@ohsu.edu)
geWorkbench

geWorkbench is an open source bioinformatics application that provides access to an integrated suite of tools for the analysis and visualization of data from a wide range of genomic domains (gene expression, sequence, protein structure and systems biology).

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