Coral: a web-based visual analysis tool for creating and characterizing cohorts

Author:

Adelberger Patrick1ORCID,Eckelt Klaus1ORCID,Bauer Markus J2,Streit Marc1ORCID,Haslinger Christian2,Zichner Thomas2ORCID

Affiliation:

1. Institute of Computer Graphics, Johannes Kepler University Linz, A-4040 Linz, Austria

2. Global Computational Biology and Digital Sciences, Boehringer Ingelheim RCV GmbH & Co KG, A-1121 Vienna, Austria

Abstract

Abstract Summary A main task in computational cancer analysis is the identification of patient subgroups (i.e. cohorts) based on metadata attributes (patient stratification) or genomic markers of response (biomarkers). Coral is a web-based cohort analysis tool that is designed to support this task: Users can interactively create and refine cohorts, which can then be compared, characterized and inspected down to the level of single items. Coral visualizes the evolution of cohorts and also provides intuitive access to prevalence information. Furthermore, findings can be stored, shared and reproduced via the integrated session management. Coral is pre-loaded with data from over 128 000 samples from the AACR Project GENIE, the Cancer Genome Atlas and the Cell Line Encyclopedia. Availability and implementation Coral is publicly available at https://coral.caleydoapp.org. The source code is released at https://github.com/Caleydo/coral. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

RCV GmbH & Co KG

Austrian Federal Ministry of Education, Science and Research

State of Upper Austria

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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