CRUX, a platform for visualising, exploring and analysing cancer genome cohort data

To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical t...

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Published inNAR genomics and bioinformatics Vol. 6; no. 1; p. lqae003
Main Authors El-Kamand, Sam, Quinn, Julian M W, Sareen, Heena, Becker, Therese M, Wong-Erasmus, Marie, Cowley, Mark J
Format Journal Article
LanguageEnglish
Published England Oxford University Press 01.03.2024
Subjects
Biomarkers
Cancer
Genomes
Genomic analysis
Survival analysis
Online AccessGet full text
ISSN2631-9268
2631-9268
DOI10.1093/nargab/lqae003

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Abstract To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and to create visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (i) it has an easy-to-use graphical interface; (ii) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (iii) it allows analyses to be run locally to address data privacy concerns (though an online version is also available) and (iv) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUX’s capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).
AbstractList To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and to create visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (i) it has an easy-to-use graphical interface; (ii) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (iii) it allows analyses to be run locally to address data privacy concerns (though an online version is also available) and (iv) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUX's capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).
To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and to create visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (i) it has an easy-to-use graphical interface; (ii) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (iii) it allows analyses to be run locally to address data privacy concerns (though an online version is also available) and (iv) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUX’s capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).
To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and to create visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (i) it has an easy-to-use graphical interface; (ii) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (iii) it allows analyses to be run locally to address data privacy concerns (though an online version is also available) and (iv) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUX’s capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).
To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and to create visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (i) it has an easy-to-use graphical interface; (ii) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (iii) it allows analyses to be run locally to address data privacy concerns (though an online version is also available) and (iv) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUX's capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and to create visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (i) it has an easy-to-use graphical interface; (ii) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (iii) it allows analyses to be run locally to address data privacy concerns (though an online version is also available) and (iv) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUX's capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).
Author Wong-Erasmus, Marie
Cowley, Mark J
El-Kamand, Sam
Sareen, Heena
Becker, Therese M
Quinn, Julian M W
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Snippet To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome...
To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome...
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SubjectTerms Biomarkers
Cancer
Genomes
Genomic analysis
Survival analysis
Title CRUX, a platform for visualising, exploring and analysing cancer genome cohort data
URI https://www.ncbi.nlm.nih.gov/pubmed/38304083
https://www.proquest.com/docview/3170909017
https://www.proquest.com/docview/2929127871
https://pubmed.ncbi.nlm.nih.gov/PMC10833466
Volume 6
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