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1.  Gene Expression Atlas update—a value-added database of microarray and sequencing-based functional genomics experiments 
Nucleic Acids Research  2011;40(D1):D1077-D1081.
Gene Expression Atlas (http://www.ebi.ac.uk/gxa) is an added-value database providing information about gene expression in different cell types, organism parts, developmental stages, disease states, sample treatments and other biological/experimental conditions. The content of this database derives from curation, re-annotation and statistical analysis of selected data from the ArrayExpress Archive and the European Nucleotide Archive. A simple interface allows the user to query for differential gene expression either by gene names or attributes or by biological conditions, e.g. diseases, organism parts or cell types. Since our previous report we made 20 monthly releases and, as of Release 11.08 (August 2011), the database supports 19 species, which contains expression data measured for 19 014 biological conditions in 136 551 assays from 5598 independent studies.
doi:10.1093/nar/gkr913
PMCID: PMC3245177  PMID: 22064864
2.  ArrayExpress update—an archive of microarray and high-throughput sequencing-based functional genomics experiments 
Nucleic Acids Research  2010;39(Database issue):D1002-D1004.
The ArrayExpress Archive (http://www.ebi.ac.uk/arrayexpress) is one of the three international public repositories of functional genomics data supporting publications. It includes data generated by sequencing or array-based technologies. Data are submitted by users and imported directly from the NCBI Gene Expression Omnibus. The ArrayExpress Archive is closely integrated with the Gene Expression Atlas and the sequence databases at the European Bioinformatics Institute. Advanced queries provided via ontology enabled interfaces include queries based on technology and sample attributes such as disease, cell types and anatomy.
doi:10.1093/nar/gkq1040
PMCID: PMC3013660  PMID: 21071405
3.  Modeling sample variables with an Experimental Factor Ontology 
Bioinformatics  2010;26(8):1112-1118.
Motivation: Describing biological sample variables with ontologies is complex due to the cross-domain nature of experiments. Ontologies provide annotation solutions; however, for cross-domain investigations, multiple ontologies are needed to represent the data. These are subject to rapid change, are often not interoperable and present complexities that are a barrier to biological resource users.
Results: We present the Experimental Factor Ontology, designed to meet cross-domain, application focused use cases for gene expression data. We describe our methodology and open source tools used to create the ontology. These include tools for creating ontology mappings, ontology views, detecting ontology changes and using ontologies in interfaces to enhance querying. The application of reference ontologies to data is a key problem, and this work presents guidelines on how community ontologies can be presented in an application ontology in a data-driven way.
Availability: http://www.ebi.ac.uk/efo
Contact: malone@ebi.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btq099
PMCID: PMC2853691  PMID: 20200009
4.  Gene Expression Atlas at the European Bioinformatics Institute 
Nucleic Acids Research  2009;38(Database issue):D690-D698.
The Gene Expression Atlas (http://www.ebi.ac.uk/gxa) is an added-value database providing information about gene expression in different cell types, organism parts, developmental stages, disease states, sample treatments and other biological/experimental conditions. The content of this database derives from curation, re-annotation and statistical analysis of selected data from the ArrayExpress Archive of Functional Genomics Data. A simple interface allows the user to query for differential gene expression either (i) by gene names or attributes such as Gene Ontology terms, or (ii) by biological conditions, e.g. diseases, organism parts or cell types. The gene queries return the conditions where expression has been reported, while condition queries return which genes are reported to be expressed in these conditions. A combination of both query types is possible. The query results are ranked using various statistical measures and by how many independent studies in the database show the particular gene-condition association. Currently, the database contains information about more than 200 000 genes from nine species and almost 4500 biological conditions studied in over 30 000 assays from over 1000 independent studies.
doi:10.1093/nar/gkp936
PMCID: PMC2808905  PMID: 19906730
5.  ArrayExpress update—from an archive of functional genomics experiments to the atlas of gene expression 
Nucleic Acids Research  2008;37(Database issue):D868-D872.
ArrayExpress http://www.ebi.ac.uk/arrayexpress consists of three components: the ArrayExpress Repository—a public archive of functional genomics experiments and supporting data, the ArrayExpress Warehouse—a database of gene expression profiles and other bio-measurements and the ArrayExpress Atlas—a new summary database and meta-analytical tool of ranked gene expression across multiple experiments and different biological conditions. The Repository contains data from over 6000 experiments comprising approximately 200 000 assays, and the database doubles in size every 15 months. The majority of the data are array based, but other data types are included, most recently—ultra high-throughput sequencing transcriptomics and epigenetic data. The Warehouse and Atlas allow users to query for differentially expressed genes by gene names and properties, experimental conditions and sample properties, or a combination of both. In this update, we describe the ArrayExpress developments over the last two years.
doi:10.1093/nar/gkn889
PMCID: PMC2686529  PMID: 19015125
6.  MAGETabulator, a suite of tools to support the microarray data format MAGE-TAB 
Bioinformatics  2008;25(2):279-280.
Summary: The MAGE-TAB format for microarray data representation and exchange has been proposed by the microarray community to replace the more complex MAGE-ML format. We present a suite of tools to support MAGE-TAB generation and validation, conversion between existing formats for data exchange, visualization of the experiment designs encoded by MAGE-TAB documents and the mining of such documents for semantic content.
Availability: Software is available from http://tab2mage.sourceforge.net/
Contact: tfrayner@gmail.com
doi:10.1093/bioinformatics/btn617
PMCID: PMC2638998  PMID: 19038988
7.  A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB 
BMC Bioinformatics  2006;7:489.
Background
Sharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGE-ML by the MGED Society. However, the complexity of the MAGE-ML format has made its use impractical for laboratories lacking dedicated bioinformatics support.
Results
We propose a simple tab-delimited, spreadsheet-based format, MAGE-TAB, which will become a part of the MAGE microarray data standard and can be used for annotating and communicating microarray data in a MIAME compliant fashion.
Conclusion
MAGE-TAB will enable laboratories without bioinformatics experience or support to manage, exchange and submit well-annotated microarray data in a standard format using a spreadsheet. The MAGE-TAB format is self-contained, and does not require an understanding of MAGE-ML or XML.
doi:10.1186/1471-2105-7-489
PMCID: PMC1687205  PMID: 17087822
8.  ArrayExpress—a public repository for microarray gene expression data at the EBI 
Nucleic Acids Research  2003;31(1):68-71.
ArrayExpress is a new public database of microarray gene expression data at the EBI, which is a generic gene expression database designed to hold data from all microarray platforms. ArrayExpress uses the annotation standard Minimum Information About a Microarray Experiment (MIAME) and the associated XML data exchange format Microarray Gene Expression Markup Language (MAGE-ML) and it is designed to store well annotated data in a structured way. The ArrayExpress infrastructure consists of the database itself, data submissions in MAGE-ML format or via an online submission tool MIAMExpress, online database query interface, and the Expression Profiler online analysis tool. ArrayExpress accepts three types of submission, arrays, experiments and protocols, each of these is assigned an accession number. Help on data submission and annotation is provided by the curation team. The database can be queried on parameters such as author, laboratory, organism, experiment or array types. With an increasing number of organisations adopting MAGE-ML standard, the volume of submissions to ArrayExpress is increasing rapidly. The database can be accessed at http://www.ebi.ac.uk/arrayexpress.
PMCID: PMC165538  PMID: 12519949
9.  From Genotype to Phenotype: Linking Bioinformatics and Medical Informatics Ontologies 
A small group of around 40 people came together at the Chancellors Conference Centre in Manchester for the Ontologies Workshop, chaired by Alan Rector and Robert Stevens. The workshop was, rather strangely, spread over 2 half days. In hindsight, this programme worked very well as it gave people the opportunity to chat over a drink on the Saturday evening and share ideas, before launching into the second half on the following day. The participants were from various walks of life, all with a common interest in finding out more about ontologies and promoting collaborations between the medical informatics and bioinformatics ontology communities.
doi:10.1002/cfg.181
PMCID: PMC2447338  PMID: 18629054

Results 1-9 (9)