Design and implementation of microarray gene expression markup language (MAGE-ML)

Spellman, Paul T.; Miller, Michael; Stewart, Jason; Troup, Charles; Sarkans, Ugis; Chervitz, Steve; Bernhart, Derek; Sherlock, Gavin; Ball, Catherine A.; Lepage, Marc; Świątek, M.; Marks, W; Goncalves, Jason; Markel, Scott; Iordan, Daniel; Shojatalab, Mohammadreza; Pizarro, Angel; White, Julie G.; Hubley, Robert; Deutsch, Eric W.; Senger, Martin; Aronow, Bruce J.; Robinson, Alan J.; Bassett, Douglas E.; Stoeckert, Christian J.; Brāzma, Alvis

doi:10.1186/gb-2002-3-9-research0046

Cited by 353 publications

(125 citation statements)

References 2 publications

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“…Standard data formats have also been adopted in order to ensure the transportability and reuse of data sets. Examples of such standards include the Microarray Gene Expression Markup Language (MAGE-ML) [17] and the Minimum Information About a Microarray Experiment (MIAME) [18].…”

Section: Data Repositories Standards and Analysis Toolsmentioning

confidence: 99%

Bioinformatics Approaches in the Study of Cancer

Hanauer¹,

Rhodes²,

Kumar‐Sinha³

et al. 2007

CMM

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A revolution is underway in the approach to studying the genetic basis of cancer. Massive amounts of data are now being generated via high-throughput techniques such as DNA microarray technology and new computational algorithms have been developed to aid in analysis. At the same time, standards-based repositories, including the Stanford Microarray Database and the Gene Expression Omnibus have been developed to store and disseminate the results of microarray experiments. Bioinformatics, the convergence of biology, information science, and computation, has played a key role in these developments. Recently developed techniques include Module Maps, SLAMS (Stepwise Linkage Analysis of Microarray Signatures), and COPA (Cancer Outlier Profile Analysis). What these techniques have in common is the application of novel algorithms to find high-level gene expression patterns across heterogeneous microarray experiments. Large-scale initiatives are underway as well. The Cancer Genome Atlas (TCGA) project is a logical extension of the Human Genome Project and is meant to produce a comprehensive atlas of genetic changes associated with cancer. The Cancer Biomedical Informatics Grid (caBIG), led by the NCI, also represents a colossal initiative involving virtually all aspects of cancer research and may help to transform the way cancer research is conducted and data are shared.

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Section: Data Repositories Standards and Analysis Toolsmentioning

confidence: 99%

Bioinformatics Approaches in the Study of Cancer

Hanauer¹,

Rhodes²,

Kumar‐Sinha³

et al. 2007

CMM

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“…MIAME is a checklist guiding researchers through the annotation process of microarray experiments. To further support the exchange of microarray data the MicroArray Gene Expression Object Model (MAGE-OM) and Markup Language (MAGE-ML) were proposed by MGED [32]. These data standards facilitate data storage and exchange via public data repositories.…”

Section: Raw Data Repositoriesmentioning

confidence: 99%

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

et al. 2006

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Differential gene expression analysis and proteomics have exerted significant impact on the elucidation of concerted cellular processes, as simultaneous measurement of hundreds to thousands of individual objects on the level of RNA and protein ensembles became technically feasible. The availability of such data sets has promised a profound understanding of phenomena on an aggregate level, expressed as the phenotypic response (observables) of cells, e.g., in the presence of drugs, or characterization of cells and tissue displaying distinct patho-physiological states. However, the step of transforming these data into context, i.e., linking distinct expression or abundance patterns with phenotypic observables -and furthermore enabling a sound biological interpretation on the level of reaction networks and concerted pathways, is still a major shortcoming. This finding is certainly based on the enormous complexity embedded in cellular reaction networks, but a variety of computational approaches have been developed over the last few years to overcome these issues. This review provides an overview on computational procedures for analysis of genomic and proteomic data introducing a sequential analysis workflow: Explorative statistics for deriving a first, from the purely statistical viewpoint, relevant candidate gene/protein list, followed by co-regulation and network analysis to biologically expand this core list toward functional networks and pathways. The review on these procedures is complemented by example applications tailored at identification of disease-associated proteins. Optimization of computational procedures involved, in conjunction with the continuous increase in additional biological data, clearly has the potential of boosting our understanding of processes on a cell-wide level.

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“…SMD provides researchers with tools to make their data fully compliant to the Minimal Information About a Microarray Experiment standards (2,3). SMD also has tools to read and write MAGE-ML files (4) and has a data pipeline that can communicate published data directly to ArrayExpress (5) and GEO (6). The public data can be selected, viewed, downloaded and analyzed by the public using most of the data analysis and quality assessment tools that are available to registered SMD users.…”

Section: Introductionmentioning

confidence: 99%

The Stanford Microarray Database: implementation of new analysis tools and open source release of software

Demeter

Beauheim

Gollub³

et al. 2007

Nucleic Acids Research

148

117

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The Stanford Microarray Database (SMD; ) is a research tool and archive that allows hundreds of researchers worldwide to store, annotate, analyze and share data generated by microarray technology. SMD supports most major microarray platforms, and is MIAME-supportive and can export or import MAGE-ML. The primary mission of SMD is to be a research tool that supports researchers from the point of data generation to data publication and dissemination, but it also provides unrestricted access to analysis tools and public data from 300 publications. In addition to supporting ongoing research, SMD makes its source code fully and freely available to others under an Open Source license, enabling other groups to create a local installation of SMD. In this article, we describe several data analysis tools implemented in SMD and we discuss features of our software release.

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Design and implementation of microarray gene expression markup language (MAGE-ML)

Cited by 353 publications

References 2 publications

Bioinformatics Approaches in the Study of Cancer

Bioinformatics Approaches in the Study of Cancer

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

The Stanford Microarray Database: implementation of new analysis tools and open source release of software

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