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LinkedOmics: analyzing multi-omics data within and across 32 cancer types
The LinkedOmics database contains multi-omics data and clinical data for 32 cancer types and a total of 11 158 patients from The Cancer Genome Atlas (TCGA) project. It is also the first multi-omics database that integrates mass spectrometry (MS)-based global proteomics data generated by the Clinical...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5753188/ https://www.ncbi.nlm.nih.gov/pubmed/29136207 http://dx.doi.org/10.1093/nar/gkx1090 |
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author | Vasaikar, Suhas V Straub, Peter Wang, Jing Zhang, Bing |
author_facet | Vasaikar, Suhas V Straub, Peter Wang, Jing Zhang, Bing |
author_sort | Vasaikar, Suhas V |
collection | PubMed |
description | The LinkedOmics database contains multi-omics data and clinical data for 32 cancer types and a total of 11 158 patients from The Cancer Genome Atlas (TCGA) project. It is also the first multi-omics database that integrates mass spectrometry (MS)-based global proteomics data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) on selected TCGA tumor samples. In total, LinkedOmics has more than a billion data points. To allow comprehensive analysis of these data, we developed three analysis modules in the LinkedOmics web application. The LinkFinder module allows flexible exploration of associations between a molecular or clinical attribute of interest and all other attributes, providing the opportunity to analyze and visualize associations between billions of attribute pairs for each cancer cohort. The LinkCompare module enables easy comparison of the associations identified by LinkFinder, which is particularly useful in multi-omics and pan-cancer analyses. The LinkInterpreter module transforms identified associations into biological understanding through pathway and network analysis. Using five case studies, we demonstrate that LinkedOmics provides a unique platform for biologists and clinicians to access, analyze and compare cancer multi-omics data within and across tumor types. LinkedOmics is freely available at http://www.linkedomics.org. |
format | Online Article Text |
id | pubmed-5753188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-57531882018-01-05 LinkedOmics: analyzing multi-omics data within and across 32 cancer types Vasaikar, Suhas V Straub, Peter Wang, Jing Zhang, Bing Nucleic Acids Res Database Issue The LinkedOmics database contains multi-omics data and clinical data for 32 cancer types and a total of 11 158 patients from The Cancer Genome Atlas (TCGA) project. It is also the first multi-omics database that integrates mass spectrometry (MS)-based global proteomics data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) on selected TCGA tumor samples. In total, LinkedOmics has more than a billion data points. To allow comprehensive analysis of these data, we developed three analysis modules in the LinkedOmics web application. The LinkFinder module allows flexible exploration of associations between a molecular or clinical attribute of interest and all other attributes, providing the opportunity to analyze and visualize associations between billions of attribute pairs for each cancer cohort. The LinkCompare module enables easy comparison of the associations identified by LinkFinder, which is particularly useful in multi-omics and pan-cancer analyses. The LinkInterpreter module transforms identified associations into biological understanding through pathway and network analysis. Using five case studies, we demonstrate that LinkedOmics provides a unique platform for biologists and clinicians to access, analyze and compare cancer multi-omics data within and across tumor types. LinkedOmics is freely available at http://www.linkedomics.org. Oxford University Press 2018-01-04 2017-11-09 /pmc/articles/PMC5753188/ /pubmed/29136207 http://dx.doi.org/10.1093/nar/gkx1090 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Database Issue Vasaikar, Suhas V Straub, Peter Wang, Jing Zhang, Bing LinkedOmics: analyzing multi-omics data within and across 32 cancer types |
title | LinkedOmics: analyzing multi-omics data within and across 32 cancer types |
title_full | LinkedOmics: analyzing multi-omics data within and across 32 cancer types |
title_fullStr | LinkedOmics: analyzing multi-omics data within and across 32 cancer types |
title_full_unstemmed | LinkedOmics: analyzing multi-omics data within and across 32 cancer types |
title_short | LinkedOmics: analyzing multi-omics data within and across 32 cancer types |
title_sort | linkedomics: analyzing multi-omics data within and across 32 cancer types |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5753188/ https://www.ncbi.nlm.nih.gov/pubmed/29136207 http://dx.doi.org/10.1093/nar/gkx1090 |
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