Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace

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Abstract

Integrative analysis of multiple data types to address complex biomedical questions requires the use of multiple software tools in concert and remains an enormous challenge for most of the biomedical research community. Here we introduce GenomeSpace ( http://www.genomespace.org), a cloud-based, cooperative community resource. Seeded as a collaboration of six of the most popular genomics analysis tools, GenomeSpace now supports the streamlined interaction of 20 bioinformatics tools and data resources. To facilitate the ability of non-programming users’ to leverage GenomeSpace in integrative analysis, it offers a growing set of ‘recipes’, short workflows involving a few tools and steps to guide investigators through high utility analysis tasks.

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Module map of stem cell genes guides creation of epithelial cancer stem cells.

David J. Wong, Helen Liu, Todd W Ridky … (2008)

Self-renewal is a hallmark of stem cells and cancer, but existence of a shared stemness program remains controversial. Here, we construct a gene module map to systematically relate transcriptional programs in embryonic stem cells (ESCs), adult tissue stem cells, and human cancers. This map reveals two predominant gene modules that distinguish ESCs and adult tissue stem cells. The ESC-like transcriptional program is activated in diverse human epithelial cancers and strongly predicts metastasis and death. c-Myc, but not other oncogenes, is sufficient to reactivate the ESC-like program in normal and cancer cells. In primary human keratinocytes transformed by Ras and I kappa B alpha, c-Myc increases the fraction of tumor-initiating cells by 150-fold, enabling tumor formation and serial propagation with as few as 500 cells. c-Myc-enhanced tumor initiation is cell-autonomous and independent of genomic instability. Thus, activation of an ESC-like transcriptional program in differentiated adult cells may induce pathologic self-renewal characteristic of cancer stem cells.

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A module map showing conditional activity of expression modules in cancer.

Eran Segal, Nir Friedman, Daphne Koller … (2004)

DNA microarrays are widely used to study changes in gene expression in tumors, but such studies are typically system-specific and do not address the commonalities and variations between different types of tumor. Here we present an integrated analysis of 1,975 published microarrays spanning 22 tumor types. We describe expression profiles in different tumors in terms of the behavior of modules, sets of genes that act in concert to carry out a specific function. Using a simple unified analysis, we extract modules and characterize gene-expression profiles in tumors as a combination of activated and deactivated modules. Activation of some modules is specific to particular types of tumor; for example, a growth-inhibitory module is specifically repressed in acute lymphoblastic leukemias and may underlie the deregulated proliferation in these cancers. Other modules are shared across a diverse set of clinical conditions, suggestive of common tumor progression mechanisms. For example, the bone osteoblastic module spans a variety of tumor types and includes both secreted growth factors and their receptors. Our findings suggest that there is a single mechanism for both primary tumor proliferation and metastasis to bone. Our analysis presents multiple research directions for diagnostic, prognostic and therapeutic studies.

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The Gaggle: An open-source software system for integrating bioinformatics software and data sources

Paul T Shannon, David J Reiss, Richard A. Bonneau … (2006)

Background Systems biologists work with many kinds of data, from many different sources, using a variety of software tools. Each of these tools typically excels at one type of analysis, such as of microarrays, of metabolic networks and of predicted protein structure. A crucial challenge is to combine the capabilities of these (and other forthcoming) data resources and tools to create a data exploration and analysis environment that does justice to the variety and complexity of systems biology data sets. A solution to this problem should recognize that data types, formats and software in this high throughput age of biology are constantly changing. Results In this paper we describe the Gaggle -a simple, open-source Java software environment that helps to solve the problem of software and database integration. Guided by the classic software engineering strategy of separation of concerns and a policy of semantic flexibility, it integrates existing popular programs and web resources into a user-friendly, easily-extended environment. We demonstrate that four simple data types (names, matrices, networks, and associative arrays) are sufficient to bring together diverse databases and software. We highlight some capabilities of the Gaggle with an exploration of Helicobacter pylori pathogenesis genes, in which we identify a putative ricin-like protein -a discovery made possible by simultaneous data exploration using a wide range of publicly available data and a variety of popular bioinformatics software tools. Conclusion We have integrated diverse databases (for example, KEGG, BioCyc, String) and software (Cytoscape, DataMatrixViewer, R statistical environment, and TIGR Microarray Expression Viewer). Through this loose coupling of diverse software and databases the Gaggle enables simultaneous exploration of experimental data (mRNA and protein abundance, protein-protein and protein-DNA interactions), functional associations (operon, chromosomal proximity, phylogenetic pattern), metabolic pathways (KEGG) and Pubmed abstracts (STRING web resource), creating an exploratory environment useful to 'web browser and spreadsheet biologists', to statistically savvy computational biologists, and those in between. The Gaggle uses Java RMI and Java Web Start technologies and can be found at .

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Journal

Journal ID (nlm-journal-id): 101215604

Journal ID (pubmed-jr-id): 32338

Journal ID (nlm-ta): Nat Methods

Journal ID (iso-abbrev): Nat. Methods

Title: Nature methods

ISSN (Print): 1548-7091

ISSN (Electronic): 1548-7105

Publication date Nihms-submitted: 22 December 2015

Publication date (Electronic): 18 January 2016

Publication date (Print): March 2016

Publication date PMC-release: 18 July 2016

Volume: 13

Issue: 3

Pages: 245-247

Affiliations

[1 ]Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA

[2 ]The Broad Institute of MIT and Harvard, Cambridge, MA, USA

[3 ]Department of Medicine, University of California, San Diego, La Jolla, USA

[4 ]Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

[5 ]Program in Translational NeuroPsychiatric Genomics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

[6 ]Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel

[7 ]Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

[8 ]Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA, USA

[9 ]UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA

[10 ]Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

[11 ]Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

Author notes

Correspondence should be addressed to J.P.M. ( jmesirov@ 123456ucsd.edu ).

Article

Manuscript ID: NIHMS746273

DOI: 10.1038/nmeth.3732

PMC ID: 4767623

PubMed ID: 26780094

SO-VID: 7ee557eb-3c31-450c-ac99-604abdac3bfd

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Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace

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Most cited references 6

Module map of stem cell genes guides creation of epithelial cancer stem cells.

A module map showing conditional activity of expression modules in cancer.

The Gaggle: An open-source software system for integrating bioinformatics software and data sources

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