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dc.contributor.author Huh, Sunghyun -
dc.contributor.author Kim, Min-Sik -
dc.date.accessioned 2021-01-22T06:44:32Z -
dc.date.available 2021-01-22T06:44:32Z -
dc.date.created 2020-09-16 -
dc.date.created 2020-09-16 -
dc.date.issued 2020-11 -
dc.identifier.citation Proteomics, v.20, no.21-22, pp.2000136 -
dc.identifier.issn 1615-9853 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/12586 -
dc.description.abstract The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated large multi-omic datasets for various cancers. Each dataset consists of common and differential data types, including genomics, epigenomics, transcriptomics, proteomics, and post-translational modifications data. They together make up a rich resource for researchers and clinicians interested in understanding cancer biology to draw from. Nevertheless, the complexity of these multi-omic datasets and a lack of an easily accessible analytical and visualization tool for exploring them continue to be a hurdle for those who are not trained in bioinformatics. In this issue, Calinawan et al. describe a user-friendly, web-based visualization platform named ProTrack for exploring the CPTAC clear cell renal cell carcinoma (ccRCC) dataset. Compared to other available visualization tools, ProTrack offers an easy yet powerful customization interface, solely dedicated to the CPTAC ccRCC dataset. Their tool enables ready inspection of potential associations between different data types within a single gene or across multiple genes without any need to code. Specific mutation types or phosphosites can also be easily looked up for any gene of interest. Calinawan et al. aim to extend their work into other CPTAC datasets, which will greatly contribute to the CPTAC as well as cancer biology community in general. © 2020 Wiley-VCH GmbH -
dc.language English -
dc.publisher John Wiley & Sons Ltd. -
dc.title A User-Friendly Visualization Tool for Multi-Omics Data -
dc.type Article -
dc.identifier.doi 10.1002/pmic.202000136 -
dc.identifier.wosid 000565542100001 -
dc.identifier.scopusid 2-s2.0-85090155557 -
dc.type.local Article(Overseas) -
dc.type.rims ART -
dc.description.journalClass 1 -
dc.citation.publicationname Proteomics -
dc.contributor.localauthor Huh, Sunghyun -
dc.contributor.localauthor Kim, Min-Sik -
dc.contributor.nonIdAuthor Huh, Sunghyun -
dc.identifier.citationVolume 20 -
dc.identifier.citationNumber 21-22 -
dc.identifier.citationStartPage 2000136 -
dc.identifier.citationTitle Proteomics -
dc.type.journalArticle Article -
dc.description.isOpenAccess N -
dc.subject.keywordAuthor Clinical Proteomic Tumor Analysis Consortium -
dc.subject.keywordAuthor data visualization -
dc.subject.keywordAuthor multi-omics -


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