Title
The UCSC Xena platform for public and private cancer genomics data visualization and interpretation. bioRxiv preprint first posted online
May. 18, 2018; doi: http://dx.doi.org/10.1101/326470
Abstract
UCSC Xena is a visual exploration resource for both public and private omics data, supported through the web-based Xena Browser and multiple turn-key Xena Hubs. This unique archecture allows researchers to view their own data securely, using private Xena Hubs, simultaneously visualizing large public cancer genomics datasets, including TCGA and the GDC. Data integration occurs only within the Xena Browser, keeping private data private. Xena supports virtually any functional genomics data, including SNVs, INDELs, large structural variants, CNV, expression, DNA methylation, ATAC-seq signals, and phenotypic annotations. Browser features include the Visual Spreadsheet, survival analyses, powerful filtering and subgrouping, statistical analyses, genomic signatures, and bookmarks. Xena differentiates itself from other genomics tools, including its predecessor, the UCSC Cancer Genomics Browser, by its ability to easily and securely view public and private data, its high performance, its broad data type support, and many unique features.
Thinking
Integrating large-cohort and small-scale laboratory data for unified analysis is one of the most urgent needs in the field of bioinformatics research. Quality control and standardization of cumbersome data from different laboratories and different databases are one of the challenges that must be solved. With the launch and completion of more and more large-cohort sequencing projects, the original data volume of related projects is already very large, and it is more and more difficult to reanalyze and mine the original data at the PB level.
Without the support of additional resources (data and computing resources), what we can do is mainly to re-integrate and extend the content of already standardized data and some of the small and medium-sized data acquired by the development, especially data quality control. And information, such as evaluation, visualization, phenotypic association analysis, and information integration of functional test literature.
