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Review
. 2019 Jul 19;20(4):1524-1541.
doi: 10.1093/bib/bby023.

A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data

Zhuo Zhang  1 , Hao Li  1 , Shuai Jiang  1 , Ruijiang Li  1 , Wanying Li  1 , Hebing Chen  1 , Xiaochen Bo  1
Affiliations
Review

A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data

Zhuo Zhang et al. Brief Bioinform. .

Abstract

The Cancer Genome Atlas (TCGA) is a publicly funded project that aims to catalog and discover major cancer-causing genomic alterations with the goal of creating a comprehensive 'atlas' of cancer genomic profiles. The availability of this genome-wide information provides an unprecedented opportunity to expand our knowledge of tumourigenesis. Computational analytics and mining are frequently used as effective tools for exploring this byzantine series of biological and biomedical data. However, some of the more advanced computational tools are often difficult to understand or use, thereby limiting their application by scientists who do not have a strong computational background. Hence, it is of great importance to build user-friendly interfaces that allow both computational scientists and life scientists without a computational background to gain greater biological and medical insights. To that end, this survey was designed to systematically present available Web-based tools and facilitate the use TCGA data for cancer research.

Keywords: The Cancer Genome Atlas; bioinformatics tools; cancer; databases; survey.

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Figures

Figure 1
Figure 1
Overview of common analysis and some applications for multidimensional data available from TCGA.
Figure 2
Figure 2
Two explorations of global alteration profile patterns as provided by publicly accessible Broad GDAC Firehose and Cancer Landscape Web tools. (A) This window view displays the user interface of Broad GDAC Firehose where users can choose a specific mutation analysis method. (B) This window provides network modeling of multiple cancers and data sets as indicated by the data sets and data types that were selected at the far right in Cancer Landscapes.
Figure 3
Figure 3
An exploration of driver genes associated with lung adenocarcinoma was conducted in OncoScape (A) and IntOGen (B). The two windows display different formats for the results obtained.
Figure 4
Figure 4
Views of interface windows in OASISPRO. (A) The stepwise selection of parameters for conducting a classification of clinical phenotypes is shown. (B) This window presents the input variables and results obtained from a representative analysis.
Figure 5
Figure 5
A representative window of the results provided by Regulome Explorer for a correlation analysis. This figure displays the main user interface, including the option for using multiple data types.
Figure 6
Figure 6
A representative survival plot generated with PROGgeneV2. TP53 gene expression was applied to a lung adenocarcinoma data set from TCGA.
See this image and copyright information in PMC

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