Evidence map›Paper›PMID 19200358›Full record

ArticleBMC bioinformatics2009

EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data.

Michael Dondrup, Stefan P Albaum, Thasso Griebel, Kolja Henckel, Sebastian Jünemann, Tim Kahlke, Christiane K Kleindt, Helge Küster, Burkhard Linke, Dominik Mertens and 7 more

Abstract read
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Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

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  7. Overexpression of the primary sigma factor geneMetabolic engineering communications · 2017
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  9. Systems Biology to Support Nanomaterial Grouping.Advances in experimental medicine and biology · 2017
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Michael DondrupComputational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de
Stefan P Albaum
Thasso Griebel
Kolja Henckel
Sebastian Jünemann
Tim Kahlke
Christiane K Kleindt
Helge Küster
Burkhard Linke
Dominik Mertens
Virginie Mittard-Runte
Heiko Neuweger
Kai J Runte
Andreas Tauch
Felix Tille
Alfred Pühler
Alexander Goesmann

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUnderstanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems.

resultsThe EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services.

conclusionOur model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

Indexed as

SoftwareDatabases, GeneticGene Expression ProfilingGenomeInternetOligonucleotide Array Sequence AnalysisUser-Computer Interface

Identifiers

PMID19200358
PMCPMC2645365

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.