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1.  Low level genome mistranslations deregulate the transcriptome and translatome and generate proteotoxic stress in yeast 
BMC Biology  2012;10:55.
Background
Organisms use highly accurate molecular processes to transcribe their genes and a variety of mRNA quality control and ribosome proofreading mechanisms to maintain intact the fidelity of genetic information flow. Despite this, low level gene translational errors induced by mutations and environmental factors cause neurodegeneration and premature death in mice and mitochondrial disorders in humans. Paradoxically, such errors can generate advantageous phenotypic diversity in fungi and bacteria through poorly understood molecular processes.
Results
In order to clarify the biological relevance of gene translational errors we have engineered codon misreading in yeast and used profiling of total and polysome-associated mRNAs, molecular and biochemical tools to characterize the recombinant cells. We demonstrate here that gene translational errors, which have negligible impact on yeast growth rate down-regulate protein synthesis, activate the unfolded protein response and environmental stress response pathways, and down-regulate chaperones linked to ribosomes.
Conclusions
We provide the first global view of transcriptional and post-transcriptional responses to global gene translational errors and we postulate that they cause gradual cell degeneration through synergistic effects of overloading protein quality control systems and deregulation of protein synthesis, but generate adaptive phenotypes in unicellular organisms through activation of stress cross-protection. We conclude that these genome wide gene translational infidelities can be degenerative or adaptive depending on cellular context and physiological condition.
doi:10.1186/1741-7007-10-55
PMCID: PMC3391182  PMID: 22715922
Yeast; mistranslation; tRNA; protein synthesis; mRNA profiling; stress; proteotoxic stress; protein misfolding; unfolded protein response
2.  GeneSigDB: a manually curated database and resource for analysis of gene expression signatures 
Nucleic Acids Research  2011;40(Database issue):D1060-D1066.
GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a ‘basket’ feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org.
doi:10.1093/nar/gkr901
PMCID: PMC3245038  PMID: 22110038
3.  Gene Expression Atlas update—a value-added database of microarray and sequencing-based functional genomics experiments 
Nucleic Acids Research  2011;40(Database issue):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
4.  OntoCAT -- simple ontology search and integration in Java, R and REST/JavaScript 
BMC Bioinformatics  2011;12:218.
Background
Ontologies have become an essential asset in the bioinformatics toolbox and a number of ontology access resources are now available, for example, the EBI Ontology Lookup Service (OLS) and the NCBO BioPortal. However, these resources differ substantially in mode, ease of access, and ontology content. This makes it relatively difficult to access each ontology source separately, map their contents to research data, and much of this effort is being replicated across different research groups.
Results
OntoCAT provides a seamless programming interface to query heterogeneous ontology resources including OLS and BioPortal, as well as user-specified local OWL and OBO files. Each resource is wrapped behind easy to learn Java, Bioconductor/R and REST web service commands enabling reuse and integration of ontology software efforts despite variation in technologies. It is also available as a stand-alone MOLGENIS database and a Google App Engine application.
Conclusions
OntoCAT provides a robust, configurable solution for accessing ontology terms specified locally and from remote services, is available as a stand-alone tool and has been tested thoroughly in the ArrayExpress, MOLGENIS, EFO and Gen2Phen phenotype use cases.
Availability
http://www.ontocat.org
doi:10.1186/1471-2105-12-218
PMCID: PMC3129328  PMID: 21619703
5.  A pipeline for RNA-seq data processing and quality assessment 
Bioinformatics  2011;27(6):867-869.
Summary: We present an R based pipeline, ArrayExpressHTS, for pre-processing, expression estimation and data quality assessment of high-throughput sequencing transcriptional profiling (RNA-seq) datasets. The pipeline starts from raw sequence files and produces standard Bioconductor R objects containing gene or transcript measurements for downstream analysis along with web reports for data quality assessment. It may be run locally on a user's own computer or remotely on a distributed R-cloud farm at the European Bioinformatics Institute. It can be used to analyse user's own datasets or public RNA-seq datasets from the ArrayExpress Archive.
Availability: The R package is available at www.ebi.ac.uk/tools/rcloud with online documentation at www.ebi.ac.uk/Tools/rwiki/, also available as supplementary material.
Contact: angela.goncalves@ebi.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr012
PMCID: PMC3051320  PMID: 21233166
6.  A global map of human gene expression 
Nature biotechnology  2010;28(4):322-324.
doi:10.1038/nbt0410-322
PMCID: PMC2974261  PMID: 20379172
7.  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
8.  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
9.  Importing ArrayExpress datasets into R/Bioconductor 
Bioinformatics  2009;25(16):2092-2094.
Summary:ArrayExpress is one of the largest public repositories of microarray datasets. R/Bioconductor provides a comprehensive suite of microarray analysis and integrative bioinformatics software. However, easy ways for importing datasets from ArrayExpress into R/Bioconductor have been lacking. Here, we present such a tool that is suitable for both interactive and automated use.
Availability: The ArrayExpress package is available from the Bioconductor project at http://www.bioconductor.org. A users guide and examples are provided with the package.
Contact: audrey@ebi.ac.uk
Supplementary information:Supplementary data are available Bioinformatics online.
doi:10.1093/bioinformatics/btp354
PMCID: PMC2723004  PMID: 19505942
10.  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
11.  4DXpress: a database for cross-species expression pattern comparisons 
Nucleic Acids Research  2007;36(Database issue):D847-D853.
In the major animal model species like mouse, fish or fly, detailed spatial information on gene expression over time can be acquired through whole mount in situ hybridization experiments. In these species, expression patterns of many genes have been studied and data has been integrated into dedicated model organism databases like ZFIN for zebrafish, MEPD for medaka, BDGP for Drosophila or GXD for mouse. However, a central repository that allows users to query and compare gene expression patterns across different species has not yet been established. Therefore, we have integrated expression patterns for zebrafish, Drosophila, medaka and mouse into a central public repository called 4DXpress (expression database in four dimensions). Users can query anatomy ontology-based expression annotations across species and quickly jump from one gene to the orthologues in other species. Genes are linked to public microarray data in ArrayExpress. We have mapped developmental stages between the species to be able to compare developmental time phases. We store the largest collection of gene expression patterns available to date in an individual resource, reflecting 16 505 annotated genes. 4DXpress will be an invaluable tool for developmental as well as for computational biologists interested in gene regulation and evolution. 4DXpress is available at http://ani.embl.de/4DXpress.
doi:10.1093/nar/gkm797
PMCID: PMC2238840  PMID: 17916571
12.  Expression Profiler: next generation—an online platform for analysis of microarray data 
Nucleic Acids Research  2004;32(Web Server issue):W465-W470.
Expression Profiler (EP, http://www.ebi.ac.uk/expressionprofiler) is a web-based platform for microarray gene expression and other functional genomics-related data analysis. The new architecture, Expression Profiler: next generation (EP:NG), modularizes the original design and allows individual analysis-task-related components to be developed by different groups and yet still seamlessly to work together and share the same user interface look and feel. Data analysis components for gene expression data preprocessing, missing value imputation, filtering, clustering methods, visualization, significant gene finding, between group analysis and other statistical components are available from the EBI (European Bioinformatics Institute) web site. The web-based design of Expression Profiler supports data sharing and collaborative analysis in a secure environment. Developed tools are integrated with the microarray gene expression database ArrayExpress and form the exploratory analytical front-end to those data. EP:NG is an open-source project, encouraging broad distribution and further extensions from the scientific community.
doi:10.1093/nar/gkh470
PMCID: PMC441608  PMID: 15215431
13.  Unraveling nature's networks 
Genome Biology  2003;4(10):341.
A report on the meeting 'Unravelling Nature's Networks: from Microarray and Proteomic Analysis to Systems Biology', Sheffield, UK, 21-22 July 2003.
A report on the meeting 'Unravelling Nature's Networks: from Microarray and Proteomic Analysis to Systems Biology', Sheffield, UK, 21-22 July 2003.
PMCID: PMC328448  PMID: 14519194
14.  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

Results 1-14 (14)