Brian Craft

The UCSC cancer genomics browser: update 2011

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) comprises a suite of web-based tools to integrate, visualize and analyze cancer genomics and clinical data. The browser displays whole-genome views of genome-wide experimental measurements for multiple samples alongside their associated clinical information. Multiple data sets can be viewed simultaneously as coordinated ‘heatmap tracks’ to compare across studies or different data modalities. Users can order, filter, aggregate, classify and display data interactively based on any given feature set including clinical features, annotated biological pathways and user-contributed collections of genes. Integrated standard statistical tools provide dynamic quantitative analysis within all available data sets. The browser hosts a growing body of publicly available cancer genomics data from a variety of cancer types, including data generated from the Cancer Genome Atlas project. Multiple consortiums use the browser on confidential prepublication data enabled by private installations. Many new features have been added, including the hgMicroscope tumor image viewer, hgSignature for real-time genomic signature evaluation on any browser track, and ‘PARADIGM’ pathway tracks to display integrative pathway activities. The browser is integrated with the UCSC Genome Browser; thus inheriting and integrating the Genome Browser’s rich set of human biology and genetics data that enhances the interpretability of the cancer genomics data.

Exploring TCGA Pan-Cancer Data at the UCSC Cancer Genomics Browser

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) offers interactive visualization and exploration of TCGA genomic, phenotypic, and clinical data, as produced by the Cancer Genome Atlas Research Network. Researchers can explore the impact of genomic alterations on phenotypes by visualizing gene and protein expression, copy number, DNA methylation, somatic mutation and pathway inference data alongside clinical features, Pan-Cancer subtype classifications and genomic biomarkers. Integrated Kaplan–Meier survival analysis helps investigators to assess survival stratification by any of the information.

The Cancer Genomics Hub (CGHub): overcoming cancer through the power of torrential data

The Cancer Genomics Hub (CGHub) is the online repository of the sequencing programs of the National Cancer Institute (NCI), including The Cancer Genomics Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects, with data from 25 different types of cancer. The CGHub currently contains >1.4 PB of data, has grown at an average rate of 50 TB a month and serves >100 TB per week. The architecture of CGHub is designed to support bulk searching and downloading through a Web-accessible application programming interface, enforce patient genome confidentiality in data storage and transmission and optimize for efficiency in access and transfer. In this article, we describe the design of these three components, present performance results for our transfer protocol, GeneTorrent, and finally report on the growth of the system in terms of data stored and transferred, including estimated limits on the current architecture. Our experienced-based estimates suggest that centralizing storage and computational resources is more efficient than wide distribution across many satellite labs. Database URL: https://cghub.ucsc.edu

Toil enables reproducible, open source, big biomedical data analyses

1. Baker, M. Nature 533, 452–454 (2016). 2. Yachie, N. et al. Nat. Biotechnol. 35, 310–312 (2017). 3. Hadimioglu, B., Stearns, R. & Ellson, R. J. Lab. Autom. 21, 4–18 (2016). 4. ANSI SLAS 1–2004: Footprint dimensions; ANSI SLAS 2–2004: Height dimensions; ANSI SLAS 3–2004: Bottom outside flange dimensions; ANSI SLAS 4–2004: Well positions; (ANSI SLAS, 2004). 5. Mckernan, K. & Gustafson, E. in DNA Sequencing II: Optimizing Preparation and Cleanup (ed. Kieleczawa, J.) 9.128 (Jones and Bartlett Publishers, 2006). 6. Storch, M. et al. BASIC: a new biopart assembly standard for idempotent cloning provides accurate, singletier DNA assembly for synthetic biology. ACS Synth. Biol. 4, 781–787 (2015). open sharing of protocols. With a precise ontology to describe standardized protocols, it may be possible to share methods widely and create community standards. We envisage that in future individual research laboratories, or clusters of colocated laboratories, will have in-house, low-cost automation work cells but will access DNA foundries via the cloud to carry out complex experimental workflows. Technologies enabling this from companies such as Emerald Cloud Lab (S. San Francisco, CA, USA), Synthace (London) and Transcriptic (Menlo Park, CA, USA) could, for example, send experimental designs to foundries and return output data to a researcher. This ‘mixed economy’ should accelerate the development and sharing of standardized protocols and metrology standards and shift a growing proportion of molecular, cellular and synthetic biology into a fully quantitative and reproducible era.

Rapid and efficient analysis of 20,000 RNA-seq samples with Toil

Toil is portable, open-source workflow software that supports contemporary workflow definition languages and can be used to securely and reproducibly run scientific workflows efficiently at large-scale. To demonstrate Toil, we processed over 20,000 RNA-seq samples to create a consistent meta-analysis of five datasets free of computational batch effects that we make freely available. Nearly all the samples were analysed in under four days using a commercial cloud cluster of 32,000 preemptable cores.

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.

Abstract 2584: The UCSC Xena system for cancer genomics data visualization and interpretation

The UCSC Xena platform (http://xena.ucsc.edu/) allows biologists and bioinformaticians to securely analyze and visualize their private functional genomics data in the context of public genomic and clinical data sets. The Xena platform consists of a set of federated data hubs and the Xena browser, which integrates across hubs, providing one location to analyze and visualize all data. Our expanding public Xena Data Hubs currently hosts 1400+ data sets from more than 35 cancer types, as well as Pan-Cancer data sets. Our public data hubs serve seminal cancer genomics and functional genomics data set to the scientific community, including the latest TCGA, TARGET, ICGC, and GTEx data sets. We support most data types including somatic and germline SNPs, INDELs, large structural variants, CNV, gene-, transcript-, exon- protein-, miRNA-expression, DNA methylation, phenotypes, clinical data, subtype classifications and genomic biomarkers. Additionally, investigators’ own functional genomics data can be hosted on private hubs running on their laptop or behind the firewall. Data is integrated on the UCSC Xena Browser, allowing biologists to view and interpretation of their genomic data in the context of a large collection of cancer genomics data sets such as TCGA. The lightweight Xena data hubs are straightforward to install on Windows, Mac and Linux operating systems and loading data is easy using either our application or command line interface. This system of the browser and hubs helps researchers combine new or preliminary results from their laptops or internal servers, or even data from a new paper, securely with vetted data from the public sphere. Visualizations and analyses include dynamic Kaplan-Meier survival analysis to assess survival stratification by any information in addition to our visual spreadsheet, scatter plots and bar graphs. We seek feedback at our poster on new visualizations and functionalities. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. The UCSC Xena system for cancer genomics data visualization and interpretation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2584. doi:10.1158/1538-7445.AM2017-2584

Abstract 5270: The UCSC Xena system for integrating and visualizing functional genomics

UCSC Xena (http://xena.ucsc.edu/) is a bioinformatics tool to visualize functional genomics data from multiple sources simultaneously, including both public and private data. The Xena system consists of a set of federated data hubs and the Xena browser, which integrates across hubs, providing one location to analyze and visualize all data. The lightweight Xena data hubs are straightforward to install on Windows, Mac and Linux operating systems and easily allow hub administrators to authenticate users, ensuring that only authorized users have access to secure data. Loading data into a Xena hub is easy using either our application or command line interface. Hosting public data from major projects, such as TCGA, on public Xena hubs gives users access without having to download these large datasets. The Xena system makes it easy to aggregate across many hubs, allowing users to integrate public datasets and private secure data together or view them separately. This system of the browser and hubs helps researchers combine new or preliminary results from their laptops or internal servers, or even data from a new paper, securely with vetted data from the public sphere. The largest public Xena hub, based at UCSC, currently hosts an expanding set of searchable data, including 806 public datasets from several large consortiums including TCGA (The Cancer Genome Atlas), ICGC (International Cancer Genome Consortium), Treehouse Childhood Cancer Project, CCLE (Cancer Cell Line Encyclopedia), and more. Xena hubs are flexible enough to handle most data types, including gene, exon, miRNA and protein expression, copy number, DNA methylation and somatic mutation data along with phenotypes, subtype classifications and genomic biomarkers. Dynamic Kaplan-Meier survival analysis helps investigators to assess survival stratification by any information while scatter plots and bar graphs offer new insights into the data. Integration with Galaxy gives users access to a myriad of bioinformatics tools for further analysis and hypothesis testing. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, Teresa Swatloski, Melissa Cline, David Haussler. The UCSC Xena system for integrating and visualizing functional genomics. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5270.

Online resources for PCAWG data exploration, visualization, and discovery

The Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort provides a large, uniformly-analyzed, whole-genome dataset. The PCAWG Landing Page (http://docs.icgc.org/pcawg) focuses on four biologist-friendly, publicly-available web tools for exploring this data: The ICGC Data Portal, UCSC Xena, Expression Atlas, and PCAWG-Scout. They enable researchers to dynamically query the complex genomics data, explore tumors’ molecular landscapes, and include external information to facilitate interpretation.The Pan-Cancer Analysis of Whole Genomes (PCAWG) project has generated, to our knowledge, the largest whole-genome cancer sequencing resource to date. Here we provide a user9s guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper: The ICGC Data Portal, UCSC Xena, Expression Atlas, PCAWG-Scout, and Chromothripsis Explorer. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, as well as demonstrate how the tools can be used together to more deeply understand tumor biology. Together, these tools enable researchers to dynamically query complex genomics data and integrate external information, enabling and enhancing PCAWG data interpretation. More information on these tools and their capabilities is available from The PCAWG Data Portals and Visualizations Page (http://docs.icgc.org/pcawg).

Pan-cancer study of heterogeneous RNA aberrations

Pan-cancer studies have transformed our understanding of recurrent somatic mutations that contribute to cancer pathogenesis; however, there has yet to be a full investigation of the multiple mechanisms in which genes can be somatically altered, particularly at the transcriptome level. We present the most comprehensive catalogue of cancer-associated gene alterations through extensive characterization of tumor transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes (PCAWG) project with matched whole-genome sequence data. We processed the RNA-seq data with a unified analysis pipeline that included both sequence alignment and extensive quality control. Subsequently, we identified gene alterations through gene expression, alternative splicing, alternative transcription starts, allele-specific expression, RNA-edited sites, and gene fusions, and by comparing with RNA-Seq from a panel of normal tissue samples from the Genotype-Tissue Expression (GTEx) project. Our data represent an extensive pan-cancer catalog of RNA-level aberrations for each gene and will be the basis for further analyses within PCAWG. NOTE TO READERS: This is a draft of a marker paper from the PCAWG Transcriptome Working Group and is intended to describe technical aspects of RNA-Seq analysis associated with the PCAWG project. The full marker paper is currently in preparation.We present the most comprehensive catalogue of cancer-associated gene alterations through characterization of tumor transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes project. Using matched whole-genome sequencing data, we attributed RNA alterations to germline and somatic DNA alterations, revealing likely genetic mechanisms. We identified 444 associations of gene expression with somatic non-coding single-nucleotide variants. We found 1,872 splicing alterations associated with somatic mutation in intronic regions, including novel exonization events associated with Alu elements. Somatic copy number alterations were the major driver of total gene and allele-specific expression (ASE) variation. Additionally, 82% of gene fusions had structural variant support, including 75 of a novel class called “bridged” fusions, in which a third genomic location bridged two different genes. Globally, we observe transcriptomic alteration signatures that differ between cancer types and have associations with DNA mutational signatures. Given this unique dataset of RNA alterations, we also identified 1,012 genes significantly altered through both DNA and RNA mechanisms. Our study represents an extensive catalog of RNA alterations and reveals new insights into the heterogeneous molecular mechanisms of cancer gene alterations.