Web-TCGA: an online platform for integrated analysis of molecular cancer data sets

BMC Bioinformatics

Software

Johannes Brägelmann¹  Joachim L. Schultze²  Mario Deng³  Sven Perner³ 

[1] Department of Internal Medicine III Section of Hematology/Oncology, University Hospital of Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany;Genomics and Immunoregulation, LIMES-Institute, University Bonn, Carl-Troll-Straße 31, 53115, Bonn, Germany;Pathology of the University Hospital of Luebeck and Leibniz Research Center Borstel, Luebeck and Borstel, Germany;Leibniz Research Center Borstel, Borstel, Germany;

关键词: TCGA;    Cancer genomics;    Statistics;    Web application;    Genomic data;   

DOI  :  10.1186/s12859-016-0917-9

 received in 2015-09-29, accepted in 2016-01-29,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundThe Cancer Genome Atlas (TCGA) is a pool of molecular data sets publicly accessible and freely available to cancer researchers anywhere around the world. However, wide spread use is limited since an advanced knowledge of statistics and statistical software is required.ResultsIn order to improve accessibility we created Web-TCGA, a web based, freely accessible online tool, which can also be run in a private instance, for integrated analysis of molecular cancer data sets provided by TCGA. In contrast to already available tools, Web-TCGA utilizes different methods for analysis and visualization of TCGA data, allowing users to generate global molecular profiles across different cancer entities simultaneously. In addition to global molecular profiles, Web-TCGA offers highly detailed gene and tumor entity centric analysis by providing interactive tables and views.ConclusionsAs a supplement to other already available tools, such as cBioPortal (Sci Signal 6:pl1, 2013, Cancer Discov 2:401–4, 2012), Web-TCGA is offering an analysis service, which does not require any installation or configuration, for molecular data sets available at the TCGA. Individual processing requests (queries) are generated by the user for mutation, methylation, expression and copy number variation (CNV) analyses. The user can focus analyses on results from single genes and cancer entities or perform a global analysis (multiple cancer entities and genes simultaneously).

【 授权许可】

CC BY   
© Deng et al. 2016