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Cancer Res. 2018 Nov 1;78(21):6320-6328. doi: 10.1158/0008-5472.CAN-18-1669. Epub 2018 Sep 19.

CANCERTOOL: A Visualization and Representation Interface to Exploit Cancer Datasets.

Author information

1
CIC bioGUNE, Bizkaia Technology Park, Bizkaia, Spain.
2
CIBERONC, Madrid, Spain.
3
Biochemistry and Molecular Biology Department, University of the Basque Country (UPV/EHU), Bilbao, Spain.
4
Bioinformatics Unit, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.
5
Centro de Investigación del Cáncer, Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca, Salamanca, Spain.
6
Instituto de Biología Molecular y Celular del Cáncer, Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca, Salamanca, Spain.
7
Oncology Programme, Institute for Research in Biomedicine (IRB-Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.
8
Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
9
University of Navarra, Tecnun School of Engineering, San Sebastián, Spain.
10
Departamento de Bioquímica y Biología Molecular, Universidad de Oviedo, Oviedo, Spain.
11
CLIP-Childhood Leukaemia Investigation Prague and Second Faculty of Medicine, Charles University, Prague, Czech Republic.
12
University Hospital Motol, Prague, Czech Republic.
13
University of Navarra, Department of Histology and Pathology, Pamplona, Spain.
14
IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.
15
Center for Applied Medical Research, Program of Solid Tumors, University of Navarra, Pamplona, Spain.
16
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain.
17
CIC bioGUNE, Bizkaia Technology Park, Bizkaia, Spain. acarracedo@cicbiogune.es.
18
Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
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Contributed equally

Abstract

With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design.Significance: In order to facilitate access of research groups without bioinformatics support to public transcriptomics data, we have developed a free online tool with an easy-to-use interface that allows researchers to obtain quality information in a readily publishable format. Cancer Res; 78(21); 6320-8. ©2018 AACR.

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