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1.  Expression Atlas update—a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments 
Nucleic Acids Research  2013;42(D1):D926-D932.
Expression Atlas (http://www.ebi.ac.uk/gxa) is a value-added database providing information about gene, protein and splice variant expression in different cell types, organism parts, developmental stages, diseases and other biological and experimental conditions. The database consists of selected high-quality microarray and RNA-sequencing experiments from ArrayExpress that have been manually curated, annotated with Experimental Factor Ontology terms and processed using standardized microarray and RNA-sequencing analysis methods. The new version of Expression Atlas introduces the concept of ‘baseline’ expression, i.e. gene and splice variant abundance levels in healthy or untreated conditions, such as tissues or cell types. Differential gene expression data benefit from an in-depth curation of experimental intent, resulting in biologically meaningful ‘contrasts’, i.e. instances of differential pairwise comparisons between two sets of biological replicates. Other novel aspects of Expression Atlas are its strict quality control of raw experimental data, up-to-date RNA-sequencing analysis methods, expression data at the level of gene sets, as well as genes and a more powerful search interface designed to maximize the biological value provided to the user.
doi:10.1093/nar/gkt1270
PMCID: PMC3964963  PMID: 24304889
2.  Semantic integration of physiology phenotypes with an application to the Cellular Phenotype Ontology 
Bioinformatics  2012;28(13):1783-1789.
Motivation: The systematic observation of phenotypes has become a crucial tool of functional genomics, and several large international projects are currently underway to identify and characterize the phenotypes that are associated with genotypes in several species. To integrate phenotype descriptions within and across species, phenotype ontologies have been developed. Applying ontologies to unify phenotype descriptions in the domain of physiology has been a particular challenge due to the high complexity of the underlying domain.
Results: In this study, we present the outline of a theory and its implementation for an ontology of physiology-related phenotypes. We provide a formal description of process attributes and relate them to the attributes of their temporal parts and participants. We apply our theory to create the Cellular Phenotype Ontology (CPO). The CPO is an ontology of morphological and physiological phenotypic characteristics of cells, cell components and cellular processes. Its prime application is to provide terms and uniform definition patterns for the annotation of cellular phenotypes. The CPO can be used for the annotation of observed abnormalities in domains, such as systems microscopy, in which cellular abnormalities are observed and for which no phenotype ontology has been created.
Availability and implementation: The CPO and the source code we generated to create the CPO are freely available on http://cell-phenotype.googlecode.com.
Contact: rh497@cam.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/bts250
PMCID: PMC3381966  PMID: 22539675
3.  The challenges of delivering bioinformatics training in the analysis of high-throughput data 
Briefings in Bioinformatics  2013;14(5):538-547.
High-throughput technologies are widely used in the field of functional genomics and used in an increasing number of applications. For many ‘wet lab’ scientists, the analysis of the large amount of data generated by such technologies is a major bottleneck that can only be overcome through very specialized training in advanced data analysis methodologies and the use of dedicated bioinformatics software tools. In this article, we wish to discuss the challenges related to delivering training in the analysis of high-throughput sequencing data and how we addressed these challenges in the hands-on training courses that we have developed at the European Bioinformatics Institute.
doi:10.1093/bib/bbt018
PMCID: PMC3771233  PMID: 23543353
bioinformatics training; high-throughput sequencing analysis; statistical methodologies; practical courses; open-source software
4.  ArrayExpress update—trends in database growth and links to data analysis tools 
Nucleic Acids Research  2012;41(D1):D987-D990.
The ArrayExpress Archive of Functional Genomics Data (http://www.ebi.ac.uk/arrayexpress) is one of three international functional genomics public data repositories, alongside the Gene Expression Omnibus at NCBI and the DDBJ Omics Archive, supporting peer-reviewed publications. It accepts data generated by sequencing or array-based technologies and currently contains data from almost a million assays, from over 30 000 experiments. The proportion of sequencing-based submissions has grown significantly over the last 2 years and has reached, in 2012, 15% of all new data. All data are available from ArrayExpress in MAGE-TAB format, which allows robust linking to data analysis and visualization tools, including Bioconductor and GenomeSpace. Additionally, R objects, for microarray data, and binary alignment format files, for sequencing data, have been generated for a significant proportion of ArrayExpress data.
doi:10.1093/nar/gks1174
PMCID: PMC3531147  PMID: 23193272
5.  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
6.  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
7.  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
8.  The Fission Yeast Homeodomain Protein Yox1p Binds to MBF and Confines MBF-Dependent Cell-Cycle Transcription to G1-S via Negative Feedback 
PLoS Genetics  2009;5(8):e1000626.
The regulation of the G1- to S-phase transition is critical for cell-cycle progression. This transition is driven by a transient transcriptional wave regulated by transcription factor complexes termed MBF/SBF in yeast and E2F-DP in mammals. Here we apply genomic, genetic, and biochemical approaches to show that the Yox1p homeodomain protein of fission yeast plays a critical role in confining MBF-dependent transcription to the G1/S transition of the cell cycle. The yox1 gene is an MBF target, and Yox1p accumulates and preferentially binds to MBF-regulated promoters, via the MBF components Res2p and Nrm1p, when they are transcriptionally repressed during the cell cycle. Deletion of yox1 results in constitutively high transcription of MBF target genes and loss of their cell cycle–regulated expression, similar to deletion of nrm1. Genome-wide location analyses of Yox1p and the MBF component Cdc10p reveal dozens of genes whose promoters are bound by both factors, including their own genes and histone genes. In addition, Cdc10p shows promiscuous binding to other sites, most notably close to replication origins. This study establishes Yox1p as a new regulatory MBF component in fission yeast, which is transcriptionally induced by MBF and in turn inhibits MBF-dependent transcription. Yox1p may function together with Nrm1p to confine MBF-dependent transcription to the G1/S transition of the cell cycle via negative feedback. Compared to the orthologous budding yeast Yox1p, which indirectly functions in a negative feedback loop for cell-cycle transcription, similarities but also notable differences in the wiring of the regulatory circuits are evident.
Author Summary
Cells proliferate by growth and division, which is supported by different gene groups that are periodically induced at specific times when they are required during the cell cycle. These genes not only need to be induced at the right time but also repressed when they are no longer required; mistakes in gene regulation can lead to problems in cell proliferation and diseases such as cancer. A well-known regulatory complex functions just before cells replicate their DNA to induce genes required for this important transition. We show that in fission yeast this regulatory complex (MBF) induces a gene whose encoded protein (Yox1p) in turn binds to MBF and represses MBF-regulated genes. In the absence of Yox1p, the MBF-regulated genes do not fluctuate during the cell cycle but remain constantly induced. Thus, MBF sets up not only the induction but also the timely repression of its target genes via Yox1p. We also provide a global analysis of all the genes regulated by Yox1p and MBF. Together, our data uncover a new negative control loop, further highlighting the sophistication of gene regulation during the cell cycle, and illustrating regulatory similarities and differences between organisms.
doi:10.1371/journal.pgen.1000626
PMCID: PMC2726434  PMID: 19714215
9.  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
10.  Global transcriptional responses of fission and budding yeast to changes in copper and iron levels: a comparative study 
Genome Biology  2007;8(5):R73.
Analysis of genome-wide responses to changing copper and iron levels in budding and fission yeast reveals conservation of only a small core set of genes and remarkable differences in the responses of the two yeasts to excess copper.
Background
Recent studies in comparative genomics demonstrate that interspecies comparison represents a powerful tool for identifying both conserved and specialized biologic processes across large evolutionary distances. All cells must adjust to environmental fluctuations in metal levels, because levels that are too low or too high can be detrimental. Here we explore the conservation of metal homoeostasis in two distantly related yeasts.
Results
We examined genome-wide gene expression responses to changing copper and iron levels in budding and fission yeast using DNA microarrays. The comparison reveals conservation of only a small core set of genes, defining the copper and iron regulons, with a larger number of additional genes being specific for each species. Novel regulatory targets were identified in Schizosaccharomyces pombe for Cuf1p (pex7 and SPAC3G6.05) and Fep1p (srx1, sib1, sib2, rds1, isu1, SPBC27B12.03c, SPAC1F8.02c, and SPBC947.05c). We also present evidence refuting a direct role of Cuf1p in the repression of genes involved in iron uptake. Remarkable differences were detected in responses of the two yeasts to excess copper, probably reflecting evolutionary adaptation to different environments.
Conclusion
The considerable evolutionary distance between budding and fission yeast resulted in substantial diversion in the regulation of copper and iron homeostasis. Despite these differences, the conserved regulation of a core set of genes involved in the uptake of these metals provides valuable clues to key features of metal metabolism.
doi:10.1186/gb-2007-8-5-r73
PMCID: PMC1929147  PMID: 17477863
11.  Whole-genome microarrays of fission yeast: characteristics, accuracy, reproducibility, and processing of array data 
BMC Genomics  2003;4:27.
Background
The genome of the fission yeast Schizosaccharomyces pombe has recently been sequenced, setting the stage for the post-genomic era of this increasingly popular model organism. We have built fission yeast microarrays, optimised protocols to improve array performance, and carried out experiments to assess various characteristics of microarrays.
Results
We designed PCR primers to amplify specific probes (180–500 bp) for all known and predicted fission yeast genes, which are printed in duplicate onto separate regions of glass slides together with control elements (~13,000 spots/slide). Fluorescence signal intensities depended on the size and intragenic position of the array elements, whereas the signal ratios were largely independent of element properties. Only the coding strand is covalently linked to the slides, and our array elements can discriminate transcriptional direction. The microarrays can distinguish sequences with up to 70% identity, above which cross-hybridisation contributes to the signal intensity. We tested the accuracy of signal ratios and measured the reproducibility of array data caused by biological and technical factors. Because the technical variability is lower, it is best to use samples prepared from independent biological experiments to obtain repeated measurements with swapping of fluorochromes to prevent dye bias. We also developed a script that discards unreliable data and performs a normalization to correct spatial artefacts.
Conclusions
This paper provides data for several microarray properties that are rarely measured. The results define critical parameters for microarray design and experiments and provide a framework to optimise and interpret array data. Our arrays give reproducible and accurate expression ratios with high sensitivity. The scripts for primer design and initial data processing as well as primer sequences and detailed protocols are available from our website.
doi:10.1186/1471-2164-4-27
PMCID: PMC179895  PMID: 12854975

Results 1-11 (11)