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.

38 Analytical Tools
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rDriver (University of Texas MD Anderson Cancer Center)

Identification of cancer driver mutations is critical for advancing cancer research and precision oncology. Due to inter-tumor genetic heterogeneity, many driver mutations occur at low frequencies, which make it challenging to distinguish them from passenger mutations. rDriver predicts driver mutations by integrating genome-wide mRNA/protein expression levels, evolutionary and structural properties of mutations characterized by functional impact scores.

For questions, please contact Ken Chen: (


Screening Bayesian Evaluation and Analysis Method (ScreenBEAM) (Columbia University)

ScreenBEAM is an algorithm that measures gene-level activity to assess the effect of high-throughput RNAi or CRISPR screens through Bayesian hierarchical modeling. For both RNAi and CRISPR, multiple shRNAs or sgRNAs (respectively) are used to target a single gene. ScreenBEAM analyzes gene-level activity for the whole set of shRNAs or sgRNAs targeting the same gene (multi-probe analysis) instead of analyzing the effect of each individual shRNA or sgRNA on a given gene. This reduces false positive and negative rates of high-throughput RNAi or CRISPR screens. This algorithm can handle both microarray and next generation sequencing data as input.

For questions, please contact Jiyang Yu: (
Similarity Weighted Nonnegative Embedding (SWNE) (University of California San Diego)

SWNE is a bioinformatic method for visualizing and analyzing high-throughput single-cell gene expression datasets. This method uses nonnegative matrix factorization to decompose datasets into latent biological factors and embeds these factors, cells, and genes in a two-dimensional visualization. This method creates an accurate, context-rich map of the datasets and enables biological interpretation of the data.

For questions, please contact Yan Wu: (


Texomer (Oregon Health and Science University (2)), (University of Texas MD Anderson Cancer Center)

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.

For questions, please contact Fang Wang: (
The Cancer Genome Atlas Clinical Explorer (Stanford University)

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.

For questions, please contact this email: (
The Cancer Proteome Atlas (TCPA) (University of Texas MD Anderson Cancer Center)

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.

For questions, please contact Han Liang: (


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: (
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: (