The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
LinkedOmics: analyzing multi-omics data within and across 32 cancer types
Open accessAbstract
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.
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Self-citationSystematic pan-cancer analysis of tumour purity
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Self-citationIntegrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer
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Self-citationThe CPTAC Data Portal: A Resource for Cancer Proteomics Research
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Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma
Gillette, M. A., Satpathy, S., Cao, S., Dhanasekaran, S. M., Vasaikar, S. V., Krug, K., Petralia, F., Li, Y., Liang, W.-W., Reva, B., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.013 · Cited by 820
Self-citationProteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities
Vasaikar, S., Huang, C., Wang, X., Petyuk, V. A., Savage, S. R., Wen, B., Dou, Y., Zhang, Y., Shi, Z., Arshad, O. A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.03.030 · Cited by 799
Self-citationProteogenomic and metabolomic characterization of human glioblastoma
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Self-citationProteogenomic Characterization of Endometrial Carcinoma
Dou, Y., Kawaler, E. A., Cui Zhou, D., Gritsenko, M. A., Huang, C., Blumenberg, L., Karpova, A., Petyuk, V. A., Savage, S. R., Satpathy, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.026 · Cited by 485
“8.2) Niu et al., 2016 https://github.com/ding-lab/hotspot3d INTEGRATE v(0.2.6) Zhang et al., 2016b https://sourceforge.net/p/integrate-fusion/ wiki/Home/ LinkedOmics Vasaikar et al., 2018 http://www.linkedomics.org MASIC Monroe et al., 2008 https://github.com/PNNL-Comp-Mass-Spec/ MASIC/releases (Continued on next page…”
MethodInfluentialSelf-citation
Metadata
- Authors
- Vasaikar, S. V., Straub, P., Wang, J., & Zhang, B.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-11-09
- Volume
- 46
- Issue
- D1
- Pages
- D956-D963
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2767377392
- Cited by
- 2,312
- References
- 18
- FWCI
- 35.81
- Type
- journal article
- Language
- en
- Open access
- gold
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