I Analytical and Visualization Tools

Developing New Analytical and Visualization Tools

One of the major hurdles in advancing comprehensive genomic level analyses of cancer is overcoming the limitations of current analytical methods to deal with large complex data sets. There is also a need for meta-analysis tools that can perform integrative analysis of genomic level alteration data (from different platforms) with clinical and non-genomic data. The National Cancer Institute supports the development of novel approaches to data analysis and visualization of genomic level alterations, including copy number, epigenomic changes, expression profiling and sequence variants, along with associated pathological/clinical data for the biospecimens.

TCGA is also focusing on driving innovations in analysis methods and visualization technologies. These technologies will:

• Reduce bias introduced into analysis methods and visualization technologies;
• Integrate various types of data;
• Map possible connections between expression, sequence, and/or clinical data patterns and specific metabolic and signaling pathways;
• Enhance the analysis and visualization of numerous types of genomic and clinical data;
• Integrate statistical analytical methods with meta-data analysis;
• Identify non-serial linkage and/or networks between groups of genes, pathways or genomic regions based on partition clustering of clinical and genomic level data; and
• Find correlations between specific expression patterns (and/or other genomic level data) and distinct clinical stages or aspects of a cancer (e.g. correlation with age at cancer onset, cancer subtypes).

NCI awarded five two-year grants to support the development of additional analytical tools and techniques appropriate for large-scale, high-throughput cancer genome analysis.

The technology areas and institutions receiving awards include:

• Tools for Large-Scale Analysis of Driver Pathways.
  Johns Hopkins University: This project is developing tools for extracting biological relevance from multiple, high-dimensional datasets.



• An Integrative Genomics Data Viewer (IGV) to Support the TCGA Project.
  The Broad Institute of MIT and Harvard: This grant supports the development of the Integrative Genomics Viewer (IGV), a novel and freely available visualization tool that is helping users to simultaneously integrate and analyze different types of genomic data, giving them the flexibility to zoom in on a specific genomic region of interest or to pan out for a broad, whole genome view. The IGV can be accessed via the TCGA Data Portal.



• New Methods for Cancer Class Discovery and Prediction: Integration, Visualization. Boston University: This project is exploring the modification, use, and adaptation of advanced statistical methods for integrating TCGA data, and the use of the VISANT mining tool for integrating TCGA with other publicly available data.


• A Computational Platform for Analyzing and Visualizing Integrated TCGA Data.
  Memorial Sloan-Kettering Cancer Center: This project is mapping available data types onto cell network nodes, such as nodes for genes and proteins; and creating visualization methods to simultaneously represent the molecular alterations observed in specific tumors.



• Visualization and Analysis of the TCGA Data Using the UCSC Cancer Genomics Browser. The University of California at Santa Cruz: This project is developing a suite of web-based tools designed to integrate, visualize, and analyze genomic and clinical data generated by the TCGA project to facilitate a synergistic interaction among clinicians, experimental biologists and bioinformaticians.