The UCSC Xena Platform for cancer genomics data visualization and interpretation

UCSC Xena is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. The investigator-driven platform consists of a web-based Xena Browser and turn-key Xena Hubs. Xena showcases seminal cancer genomics datasets from TCGA, Pan-Cancer Atlas, PCAWG, ICGC, GTEx, and the GDC; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data modalities, including SNPs, INDELs, large structural variants, CNV, gene and other types of expression, DNA methylation, clinical and phenotypic annotations. A researcher can host their own data securely via private hubs running on a laptop or behind a firewall, with visual and analytical integration occurring only within the Xena Browser. Browser features include the high performance Visual Spreadsheet, dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, charts, statistical analyses, genomic signatures, and bookmarks.

[1]  Mark Diekhans,et al.  MuPIT interactive: webserver for mapping variant positions to annotated, interactive 3D structures , 2013, Human Genetics.

[2]  David Haussler,et al.  TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal. , 2017, Cancer research.

[3]  L. Chin,et al.  Making sense of cancer genomic data. , 2011, Genes & development.

[4]  E. Mardis The impact of next-generation sequencing technology on genetics. , 2008, Trends in genetics : TIG.

[5]  Xin Zhou,et al.  Pan-cancer genome and transcriptome analyses of 1,699 pediatric leukemias and solid tumors , 2018, Nature.

[6]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[7]  Hui Shen,et al.  DNA methylation loss in late-replicating domains is linked to mitotic cell division , 2018, Nature Genetics.

[8]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[9]  Nicola J. Rinaldi,et al.  Genetic effects on gene expression across human tissues , 2017, Nature.

[10]  Icgc,et al.  Pan-cancer analysis of whole genomes , 2017, bioRxiv.

[11]  Robert J. Lonigro,et al.  Integrative Clinical Genomics of Metastatic Cancer , 2017, Nature.

[12]  Helga Thorvaldsdóttir,et al.  Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration , 2012, Briefings Bioinform..

[13]  Li Ding,et al.  Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics , 2018, Cell.

[14]  Joshua M. Stuart,et al.  Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. , 2018, Cell.

[15]  J. Haerting,et al.  Gene-expression signatures in breast cancer. , 2003, The New England journal of medicine.

[16]  Nuria Lopez-Bigas,et al.  Gitools: Analysis and Visualisation of Genomic Data Using Interactive Heat-Maps , 2011, PloS one.

[17]  Quin F. Wills,et al.  Application of single-cell genomics in cancer: promise and challenges , 2015, Human molecular genetics.

[18]  David Haussler,et al.  Comparative genomic analysis for pediatric cancer patients evaluated in a California Initiative to Advance Precision Medicine Demonstration Project. , 2017 .

[19]  Maria Jesus Martin,et al.  BioJS: an open source JavaScript framework for biological data visualization , 2013, Bioinform..

[20]  Jannik N. Andersen,et al.  Cancer genomics: from discovery science to personalized medicine , 2011, Nature Medicine.

[21]  B. Langmead,et al.  Cloud computing for genomic data analysis and collaboration , 2018, Nature Reviews Genetics.

[22]  Heidi Ledford Big science: The cancer genome challenge , 2010, Nature.

[23]  Gary D Bader,et al.  International network of cancer genome projects , 2010, Nature.

[24]  Allison P. Heath,et al.  Toward a Shared Vision for Cancer Genomic Data. , 2016, The New England journal of medicine.

[25]  Li Ding,et al.  Driver Fusions and Their Implications in the Development and Treatment of Human Cancers , 2018, Cell reports.

[26]  Benjamin J. Raphael,et al.  MAGI: visualization and collaborative annotation of genomic aberrations , 2015, Nature Methods.

[27]  Somasekar Seshagiri,et al.  Somatic mutations lead to an oncogenic deletion of met in lung cancer. , 2006, Cancer research.

[28]  Nuria Lopez-Bigas,et al.  Visualizing multidimensional cancer genomics data , 2013, Genome Medicine.

[29]  Arul M. Chinnaiyan,et al.  Cancer transcriptome profiling at the juncture of clinical translation , 2017, Nature Reviews Genetics.

[30]  Mauro A. A. Castro,et al.  The chromatin accessibility landscape of primary human cancers , 2018, Science.

[31]  Mary Goldman,et al.  Toil enables reproducible, open source, big biomedical data analyses , 2017, Nature Biotechnology.

[32]  Peter W. Laird,et al.  Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer , 2018, Cell.

[33]  David J. Arenillas,et al.  Cis-regulatory somatic mutations and gene-expression alteration in B-cell lymphomas , 2014, Genome Biology.

[34]  Benjamin E. Gross,et al.  The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. , 2012, Cancer discovery.

[35]  M. Schatz,et al.  Big Data: Astronomical or Genomical? , 2015, PLoS biology.

[36]  John Quackenbush,et al.  WebMeV: a Cloud Platform for Analyzing and Visualizing Cancer Genomic Data , 2017, bioRxiv.

[37]  Thomas Zichner,et al.  Ordino: a visual cancer analysis tool for ranking and exploring genes, cell lines and tissue samples , 2019, Bioinform..

[38]  Syed Haider,et al.  International Cancer Genome Consortium Data Portal—a one-stop shop for cancer genomics data , 2011, Database J. Biol. Databases Curation.

[39]  klaguia International Network of Cancer Genome Projects , 2010 .

[40]  Steven J. M. Jones,et al.  Oncogenic Signaling Pathways in The Cancer Genome Atlas. , 2018, Cell.

[41]  Steven J. M. Jones,et al.  Comprehensive molecular profiling of lung adenocarcinoma , 2014, Nature.

[42]  L. Staudt,et al.  The NCI Genomic Data Commons as an engine for precision medicine. , 2017, Blood.