The caBIG ® Life Sciences Distribution

caBIG® is a virtual network of organizations developing and adopting interoperable databases and analytical tools to facilitate translational cancer research (von Eschenbach and Buetow 2007). It is an open-source, open-access program, and all the tools and resources are freely available to the research community. The National Cancer Institute is developing resources to assist enterprise-wide adoption of the caBIG® tools. To this end, we have bundled mature software tools together to facilitate easy adoption and installation. The Life Sciences Distribution (LSD) is comprised of tools to support the continuum of translational research: caArray, for the management and annotation of microarray data; caTissue, to support the collection, annotation, and distribution of biospecimens; the Clinical Trials Object Data System, for the sharing of clinical trials information; the National Biomedical Imaging Archive, for annotation, storage, and sharing of in vivo images; cancer Genome Wide Association Studies, for publishing and mining data from GWAS studies; and geWorkbench, supporting the integrated analysis and annotation of expression and sequence data. All the LSD tools are connected to caGrid (Saltz et al. 2006), which makes it possible for the databases at multiple institutions to be interconnected to support data sharing and integration.

[1]  Chris F. Taylor,et al.  The MGED Ontology: a resource for semantics-based description of microarray experiments , 2006, Bioinform..

[2]  Ian T. Foster Globus Toolkit Version 4: Software for Service-Oriented Systems , 2005, NPC.

[3]  K. Buetow,et al.  Cancer Informatics Vision: caBIG™ , 2006, Cancer informatics.

[4]  Jason E. Stewart,et al.  Minimum information about a microarray experiment (MIAME)—toward standards for microarray data , 2001, Nature Genetics.

[5]  Wei Keat Lim,et al.  A Context-Specific Network of Protein-DNA and Protein-Protein Interactions Reveals New Regulatory Motifs in Human B Cells , 2006, Systems Biology and Computational Proteomics.

[6]  Gilberto Fragoso,et al.  caCORE version 3: Implementation of a model driven, service-oriented architecture for semantic interoperability , 2008, J. Biomed. Informatics.

[7]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[8]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[9]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[10]  Michael Krauthammer,et al.  GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data , 2004, J. Biomed. Informatics.

[11]  Julie Evans,et al.  Model Formulation: The BRIDG Project: A Technical Report , 2008, J. Am. Medical Informatics Assoc..

[12]  Lucas D. Ward,et al.  Predicting functional transcription factor binding through alignment-free and affinity-based analysis of orthologous promoter sequences , 2008, ISMB.

[13]  Geoffrey J. Barton,et al.  Jalview Version 2—a multiple sequence alignment editor and analysis workbench , 2009, Bioinform..

[14]  Paul T. Spellman,et al.  A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB , 2006, BMC Bioinformatics.

[15]  Kai Wang,et al.  Dissecting the Interface Between Signaling and Transcriptional Regulation in Human B Cells , 2008, Pacific Symposium on Biocomputing.

[16]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[17]  Joel H. Saltz,et al.  caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid , 2006, Bioinform..