Abstract
Cancer genome characterization studies, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), are producing a flood of diverse data including ...whole-genome sequencing scans, expression profiles, high-resolution SNP and copy number data, and epigenetic profiles. These diverse datasets are enabling researchers to study the cancer genome in unprecedented detail. However, their sheer size and diversity presents significant challenges to downstream interpretation. While much progress has been made in automated analysis, human review enabled by intuitive and responsive visualizations remains an essential component of data analysis. The Integrative Genomics Viewer (IGV) was developed to address this need by providing researchers with an intuitive, user-friendly, interactive visualization tool for exploration of diverse genomic datasets. IGV has extensive support for both next-generation and micro-array based platforms, and for integration of these data types with clinical and phenotypic data. It provides an intuitive interface similar to online mapping tools such as Google Maps, enabling smooth zooming and panning at all resolution scales, from whole genome to base pairs. Data can be annotated, filtered, grouped, and sorted in a variety of ways. This ability to dynamically and flexibly integrate multiple different datasets, and view them at any scale, allows investigators to elucidate complex biological relationships that are not otherwise readily apparent. In this presentation we describe the IGV, with emphasis on recent features focused on data integration and interpretation, developed in close collaboration with cancer researchers. Specifically (1) a multi-locus pathway view which supports simultaneous viewing of data in multiple genomic regions defined by pathways or gene sets, (2) integration with external tools such as Mutation Assessor and PolyPhen, to provide views and data to asses the functional significance of somatic events, and (3) a flexible, interactive charting capability to enable detailed exploration of inter-dependencies between data types at a specific locus or set of loci. These features will be illustrated in the context of use cases drawn from the TCGA glioblastoma multiforme and ovarian datasets.
Citation Format: {Authors}. {Abstract title} abstract. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3968. doi:1538-7445.AM2012-3968
Abstract
As the availability of genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific ...insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The The Jupyter Notebook environment has become the de facto medium for this purpose due to its ease in combining scientific exposition with executable code to form a single reproducible “research narrative” document. However, analyses are often compute-intensive, requiring more resources than are frequently available within a notebook environment running on a desktop or laptop computer. Additionally, thousands of tools, modules, and plugins are readily available on integrative software platforms such as Galaxy, GenePattern, and Cytoscape, outside the notebook paradigm. Finally, many biomedical investigators lack the programming expertise required to fully realize the benefits and utility of the notebook metaphor. To address these issues, we have released Genomics to Notebook (g2nb), which builds on JupyterLab to add access to bioinformatics platforms and other functionality for the non-programmer through the components described below, while retaining all programmatic features of JupyterLab.The g2nb environment incorporates bioinformatics software platforms within the notebook interface, allowing a single notebook to contain a workflow spanning multiple tools and servers. When run, the entire analysis appears to execute seamlessly within the notebook. To achieve this, we developed a new analysis cell type that provides an interface within the notebook to tools that are hosted on a remote Galaxy or GenePattern server. Analysis cells present a web form-like interface, similar to that of the original platforms, requiring an investigator to provide only the input parameters and data. The popular visualization tools Cytoscape and Integrative Genomics Viewer (IGV) are also supported in their web-based formats as notebook cells, with additional platforms and visualizers added regularly. The g2nb environment is freely available at the g2nb workspace, http://g2nb.org, where scientists can use all of the g2nb functionality with only a web browser. Those who wish to use g2nb locally can use the provided Docker container or install the packages via the conda or pip package managers. The online workspace also includes a library of featured genomic analysis notebooks, including templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes.
Citation Format: Michael M. Reich, Thorin Tabor, John Liefeld, Jayadev Joshi, Forrest Kim, Helga Thorvaldsdottir, Daniel Blankenberg, Jill P. Mesirov. Genomics to Notebook (g2nb): Extending the electronic notebook to address the needs of cancer bioinformatics abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2073.
Abstract
In 2005, we introduced the Gene Set Enrichment Analysis (GSEA) method to enable the identification and estimation of significance of activated biological pathways and processes in molecular ...data. Serving a community of over 300,000 registered users, and contributing scientific analysis to more than 30,000 publications, GSEA has become ubiquitous in gene expression analysis and particularly in cancer research. The power of GSEA relies, in a large part, on the Molecular Signatures Database (MSigDB) that provides tens of thousands of expertly curated and annotated gene sets representing cellular processes, canonical pathways, response signatures, etc., derived from prior studies, public data, and curated pathway databases. Here we describe an important recent addition to the GSEA/MSigDB resource.
Historically, the GSEA-MSigDB resource has focused specifically on human data, offering guidance and support for analysis of model organism data through limited provision of orthology mapping files. In recognition of the importance of the mouse as a model organism for cancer research, we recently significantly expanded our support for mouse data in two ways.
(1) We introduced Mouse MSigDB with a total of about 15,000 sets that are provided in the native mouse gene symbol space and are derived from published mouse datasets and mouse-focused resources. This allows GSEA analysis of mouse datasets without the need for orthology conversion.
(2) For investigators who wish to use MSigDB’s human gene sets to analyze mouse data, or vice versa, we also implemented a new procedure for orthology mapping gene identifiers in an input dataset to match the identifiers of the gene sets. The new mapping considers the particular requirements of gene set enrichment analysis, and incorporates high confidence gene ortholog data from the Alliance of Genome Resources and ortholog-match refinement utilizing Ensembl’s ortholog datasets.
Together, these new additions offer a substantial step forward for making mouse a first-class citizen of the GSEA-MSigDB ecosystem. The new Mouse MSigDB, and the full GSEA-MSigDB resource, is available at gsea-msigdb.org.
Citation Format: Anthony S. Castanza, Jill M. Recla, David Eby, Alexander T. Wenzel, Helga Thorvaldsdottir, Carol J. Bult, Jill P. Mesirov. Extended support for model organisms: The next frontier for the molecular signatures database abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6568.
Abstract
As the availability of genetic and genomic data and analysis tools from large-scale cancer initiatives continues to increase, the need has become more urgent for a software environment that ...supports the entire “idea to dissemination” cycle of an integrative cancer genomics analysis. Such a system would need to provide access to a large number of analysis tools without the need for programming, be sufficiently flexible to accommodate the practices of non-programming biologists as well as experienced bioinformaticians, and would provide a way for researchers to encapsulate their work into a single “executable document” that included not only the analytical workflow but also the associated descriptive text, graphics, and supporting research. To address these needs, we have developed GenePattern Notebook, based on the GenePattern environment for integrative genomics and the Jupyter (formerly IPython) Notebook system. GenePattern Notebook unites the phases of in silico research - experiment design, collaborative analysis, and publication - into a single interface. GenePattern Notebook presents a familiar lab-notebook format that allows researchers to build a record of their work by creating “cells” containing text, graphics, or executable analyses. Researchers add, delete, and modify cells as the research evolves, supporting the initial research phases of prototyping and collaborative analysis. When an analysis is ready for publication, the same document that was used in the design and analysis phases becomes a research narrative that interleaves text, graphics, data, and executable analyses, serving as the complete, reproducible, in silico methods section for a publication. GenePattern Notebook also supports programmers and bioinformaticians, providing seamless interoperation between code blocks and GenePattern analyses within a notebook document. We are collaborating with cancer research laboratories to create GenePattern Notebook documents to serve as examples of integrative genomics research that can be enabled through this system. Potential notebook topics include characterization of intratumoral heterogeneity from single cell RNA-Seq data, effective clinical interpretation of comprehensive genomic profiling from whole exome sequencing of a patient's tumor and germ line samples, and identification of master regulators/transcription factors associated with the downstream transcriptional effects associated with the activation of an oncogene. We will make these and other notebooks available in an online GenePattern Notebooks repository, where other researchers may also contribute their notebooks.
Citation Format: Michael M. Reich, Thorin Tabor, John T. Liefeld, Peter Carr, Barbara Hill, Marc-Danie Nazaire, David Eby, Helga Thorvaldsdottir, Pablo Tamayo, Jill P. Mesirov. GenePattern Notebooks: an integrative analytical environment for cancer research. 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 5273.
Abstract
Over the past several years, cancer genome characterization initiatives such as The Cancer Genome Atlas and the International Cancer Genome Consortium have produced an explosion of genomic ...data. These efforts, together with the proliferation of repositories for small-molecule screens and large-scale data production efforts to discover the breadth of genomic variation in humans, offer a new era of potential for the understanding of the basic mechanisms of cancer and identification of novel treatments.
Comprehensive analysis of these datasets requires the coordinated use of Web-based applications and data repositories, desktop analysis tools and visualizers, and single-purpose algorithms. However, the effort required to transfer data between tools, convert between data formats, and manage results often prevents researchers from utilizing the wealth of methods available to them. Many integrative genomics and translational “bench to bedside” discoveries are possible with combinations of existing tools, but the necessary transitions between them puts them out of the reach of most researchers.
GenomeSpace, http://www.genomespace.org, is an environment that brings together diverse computational tools, enabling scientists without programming skills to easily combine their capabilities. It aims to offer a common space to create, manipulate and share an ever-growing range of genomic analysis tools. GenomeSpace features support for cloud-based data storage and analysis, multi-tool analytic workflows, automatic conversion of data formats, and ease of connecting new tools to the environment. A set of six “GenomeSpace-enabled” seed tools developed by collaborating organizations provides a comprehensive platform for the analysis of cancer data: Cytoscape (UCSD), Galaxy (Penn State University), GenePattern (Broad Institute), Genomica (Weitzmann Institute), Integrative Genomics Viewer (Broad Institute), and the UCSC Genome Browser (UCSC). Its open architecture has driven its adoption by a number of additional tools, including ArrayExpress, Cistrome, the Cancer Cell Line Encyclopedia, Project Achilles Portal, and Multiple Myeloma Genomics Portal, with new tools and resources being released regularly.
We show how researchers can use GenomeSpace to effortlessly combine the capabilities of all of these tools in several cancer research scenarios.
Citation Format: Michael M. Reich, John Liefeld, Marco Ocana, Donkeung Jang, James Robinson, Peter Carr, Barbara Hill, Thorin Tabor, Helga Thorvaldsdottir, Aviv Regev, Jill P. Mesirov. GenomeSpace: An environment for frictionless bioinformatics. abstract. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5331. doi:10.1158/1538-7445.AM2014-5331
Abstract
Over the past several years, cancer genome characterization initiatives such as The Cancer Genome Atlas, International Cancer Genome Consortium, and the Tumor Sequencing Project have ...produced an explosion of genomic data. The pace of data production has increased with the adoption of next-generation sequencing technologies and large-scale data production efforts to discover the breadth of genomic variation in humans. Comprehensive analysis of these datasets requires the coordinated use of Web-based data repositories and applications, desktop analysis tools and visualizers, and single-purpose algorithms. However, the effort required to transfer data between tools, convert between data formats, and manage results often prevents researchers from utilizing the wealth of methods available to them. Many integrative genomics and translational “bench to bedside” discoveries are possible with combinations of existing tools, but the necessary transitions between them puts them out of the reach of most researchers. Cloud technologies have produced a new wave of applications that transfer next-generation sequence data directly from sequencers to a compute cloud for storage and analysis, but these systems are at an early stage of maturity and many are tied to commercial sequencing vendors.
GenomeSpace, http://www.genomespace.org, is an environment that brings together diverse computational tools, enabling scientists without programming skills to easily combine their capabilities. It aims to offer a common space to create, manipulate and share an ever-growing range of genomic analysis tools. GenomeSpace features support for cloud-based data storage and analysis, multi-tool analytic workflows, automatic conversion of data formats, and ease of connecting new tools to the environment. A set of six “GenomeSpace-enabled” seed tools developed by collaborating organizations provides a comprehensive platform for the analysis of cancer data: Cytoscape (UCSD), Galaxy (Penn State University), GenePattern (Broad Institute), Genomica (Weitzmann Institute), Integrative Genomics Viewer (Broad Institute), and the UCSC Genome Browser (UCSC). The extensible format of the system has empowered a wider range of cancer analyses through the addition of ArrayExpress (European Bioinformatics Institute), InSilico DB (University of Brussels), geWorkbench (Columbia University), and Cistrome (Dana-Farber Cancer Institute).
We show how researchers can use GenomeSpace to effortlessly combine the capabilities of all of these tools in several cancer research scenarios.
Citation Format: Michael Reich, John Liefeld, Helga Thorvaldsdottir, Marco Ocana, Thorin Tabor, DK Jang, Jill P. Mesirov. GenomeSpace: an environment for frictionless bioinformatics. abstract. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5141. doi:10.1158/1538-7445.AM2013-5141
Abstract
Over the past several years, cancer genome characterization initiatives such as The Cancer Genome Atlas and the Tumor Sequencing Project have produced an explosion of genomic data. The pace ...of data production has recently increased with the adoption of next-generation sequencing technologies and large-scale data production efforts to discover the breadth of genomic variation in humans. Comprehensive analysis of these datasets requires the coordinated use of Web-based applications and data repositories, desktop analysis tools and visualizers, and single-purpose algorithms. However, the effort required to transfer data between tools, convert between data formats, and manage results often prevents researchers from utilizing the wealth of methods available to them. Many integrative genomics and translational “bench to bedside” discoveries are possible with combinations of existing tools, but the necessary transitions between them puts them out of the reach of most researchers. GenomeSpace, http://www.genomespace.org, is an environment that brings together diverse computational tools, enabling scientists without programming skills to easily combine their capabilities. It aims to offer a common space to create, manipulate and share an ever-growing range of genomic analysis tools. GenomeSpace features support for cloud-based data storage and analysis, multi-tool analytic workflows, automatic conversion of data formats, and ease of connecting new tools to the environment. A set of six “GenomeSpace-enabled” seed tools developed by collaborating organizations provides a comprehensive platform for the analysis of cancer data: Cytoscape (UCSD), Galaxy (Penn State University), GenePattern (Broad Institute), Genomica (Weitzmann Institute), Integrative Genomics Viewer (Broad Institute), and the UCSC Genome Browser (UCSC). We show how researchers can use GenomeSpace to effortlessly combine the capabilities of all of these tools in several cancer research scenarios.
Citation Format: {Authors}. {Abstract title} abstract. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3966. doi:1538-7445.AM2012-3966
Abstract C60: The integrative genomics viewer Robinson, James; Thorvaldsdottir, Helga; Nazaire, Marc-Danie ...
Cancer research (Chicago, Ill.),
12/2009, Letnik:
69, Številka:
23_Supplement
Journal Article
Recenzirano
Abstract
Rapid improvements in next generation sequencing and array-based profiling methods have created a paradigm shift in how scientists approach the study of the cancer genome. Projects such as ...The Cancer Genome Atlas (TCGA) are harnessing these advances to probe the cancer genome at unprecedented resolution, generating comprehensively catalogs of the genetic alterations of hundreds of tumors.
Visualization plays an important role in assimilating the large, muti-modal datasets produced by TCGA and similar projects. To be maximally effective tools must flexibly integrate genomic, functional, and phenotypic data from a variety of platforms, and efficiently handle large datasets. To address these critical challenges we developed the Integrative Genomics Viewer (IGV), a flexible, scalable, high-performance tool for the concurrent visualization of multiple large scale datasets. The IGV supports integrated views of any data type that can be mapped to genomic coordinates, including, but not limited to, next generation sequence data, copy number, gene expression, RNAi, ChIP-seq, RNA-seq, and genome annotations. Data can be annotated, filtered, grouped, and sorted according to clinical and phenotypic attributes.
A particular strength of IGV is its ability to handle very large datasets, for example deep coverage sequence data from whole genome scans, or highresolution copy number data from thousands of samples, on standard desktop computers. IGV permits the user to move seamlessly across these datasets at any level of detail, and quickly zoom out to whole genome or drill down to base pair resolution. The ability to dynamically and flexibly integrate multiple data modalities and view them at any scale allows the user to elucidate complex biological relationships that would not otherwise be apparent.
The IGV is freely available without restriction. It is currently under active development and use in The Cancer Genome Atlas, 1000 Genomes Project, and other large genomic projects.
Citation Information: Cancer Res 2009;69(23 Suppl):C60.
