Gabriella Rustici

ArrayExpress update—from an archive of functional genomics experiments to the atlas of gene expression

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.

ArrayExpress update—simplifying data submissions

The ArrayExpress Archive of Functional Genomics Data (http://www.ebi.ac.uk/arrayexpress) is an international functional genomics database at the European Bioinformatics Institute (EMBL-EBI) recommended by most journals as a repository for data supporting peer-reviewed publications. It contains data from over 7000 public sequencing and 42 000 array-based studies comprising over 1.5 million assays in total. The proportion of sequencing-based submissions has grown significantly over the last few years and has doubled in the last 18 months, whilst the rate of microarray submissions is growing slightly. All data in ArrayExpress are available in the MAGE-TAB format, which allows robust linking to data analysis and visualization tools and standardized analysis. The main development over the last two years has been the release of a new data submission tool Annotare, which has reduced the average submission time almost 3-fold. In the near future, Annotare will become the only submission route into ArrayExpress, alongside MAGE-TAB format-based pipelines. ArrayExpress is a stable and highly accessed resource. Our future tasks include automation of data flows and further integration with other EMBL-EBI resources for the representation of multi-omics data.

Expression Atlas update—a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments

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.

Gene Expression Atlas at the European Bioinformatics Institute

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.

The Fission Yeast Homeodomain Protein Yox1p Binds to MBF and Confines MBF-Dependent Cell-Cycle Transcription to G1-S via Negative Feedback

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.

Tumor cell migration screen identifies SRPK1 as breast cancer metastasis determinant

Tumor cell migration is a key process for cancer cell dissemination and metastasis that is controlled by signal-mediated cytoskeletal and cell matrix adhesion remodeling. Using a phagokinetic track assay with migratory H1299 cells, we performed an siRNA screen of almost 1,500 genes encoding kinases/phosphatases and adhesome- and migration-related proteins to identify genes that affect tumor cell migration speed and persistence. Thirty candidate genes that altered cell migration were validated in live tumor cell migration assays. Eight were associated with metastasis-free survival in breast cancer patients, with integrin β3-binding protein (ITGB3BP), MAP3K8, NIMA-related kinase (NEK2), and SHC-transforming protein 1 (SHC1) being the most predictive. Examination of genes that modulate migration indicated that SRPK1, encoding the splicing factor kinase SRSF protein kinase 1, is relevant to breast cancer outcomes, as it was highly expressed in basal breast cancer. Furthermore, high SRPK1 expression correlated with poor breast cancer disease outcome and preferential metastasis to the lungs and brain. In 2 independent murine models of breast tumor metastasis, stable shRNA-based SRPK1 knockdown suppressed metastasis to distant organs, including lung, liver, and spleen, and inhibited focal adhesion reorganization. Our study provides comprehensive information on the molecular determinants of tumor cell migration and suggests that SRPK1 has potential as a drug target for limiting breast cancer metastasis.

Semantic integration of physiology phenotypes with an application to the Cellular Phenotype Ontology

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.

Image Data Resource: a bioimage data integration and publication platform

Access to primary research data is vital for the advancement of science. To extend the data types supported by community repositories, we built a prototype Image Data Resource (IDR). IDR links data from several imaging modalities, including high-content screening, multi-dimensional microscopy and digital pathology, with public genetic or chemical databases and cell and tissue phenotypes expressed using controlled ontologies. Using this integration, IDR facilitates the analysis of gene networks and reveals functional interactions that are inaccessible to individual studies. To enable reanalysis, we also established a computational resource based on Jupyter notebooks that allows remote access to the entire IDR. IDR is also an open-source platform for publishing imaging data. Thus IDR provides an online resource and a software infrastructure that promotes and extends publication and reanalysis of scientific image data.

Data Storage and Analysis in ArrayExpress and Expression Profiler

ArrayExpress at the European Bioinformatics Institute is a public database for MIAME‐compliant microarray and transcriptomics data. It consists of two parts: the ArrayExpress Repository, which is a public archive of microarray data, and the ArrayExpress Warehouse of Gene Expression Profiles, which contains additionally curated subsets of data from the Repository. Archived experiments can be queried by experimental attributes, such as keywords, species, array platform, publication details, or accession numbers. Gene expression profiles can be queried by gene names and properties, such as Gene Ontology terms, allowing expression profiles visualization. The data can be exported and analyzed using the online data analysis tool named Expression Profiler. Data analysis components, such as data preprocessing, filtering, differentially expressed gene finding, clustering methods, and ordination‐based techniques, as well as other statistical tools are all available in Expression Profiler, via integration with the statistical package R. Curr. Protoc. Bioinform. 23:7.13.1‐7.13.27.

The cellular microscopy phenotype ontology

BackgroundPhenotypic data derived from high content screening is currently annotated using free-text, thus preventing the integration of independent datasets, including those generated in different biological domains, such as cell lines, mouse and human tissues.DescriptionWe present the Cellular Microscopy Phenotype Ontology (CMPO), a species neutral ontology for describing phenotypic observations relating to the whole cell, cellular components, cellular processes and cell populations. CMPO is compatible with related ontology efforts, allowing for future cross-species integration of phenotypic data. CMPO was developed following a curator-driven approach where phenotype data were annotated by expert biologists following the Entity-Quality (EQ) pattern. These EQs were subsequently transformed into new CMPO terms following an established post composition process.ConclusionCMPO is currently being utilized to annotate phenotypes associated with high content screening datasets stored in several image repositories including the Image Data Repository (IDR), MitoSys project database and the Cellular Phenotype Database to facilitate data browsing and discoverability.