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
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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: (