Cornelia Hedeler

Comparative genome analysis of filamentous fungi reveals gene family expansions associated with fungal pathogenesis.

Fungi and oomycetes are the causal agents of many of the most serious diseases of plants. Here we report a detailed comparative analysis of the genome sequences of thirty-six species of fungi and oomycetes, including seven plant pathogenic species, that aims to explore the common genetic features associated with plant disease-causing species. The predicted translational products of each genome have been clustered into groups of potential orthologues using Markov Chain Clustering and the data integrated into the e-Fungi object-oriented data warehouse (http://www.e-fungi.org.uk/). Analysis of the species distribution of members of these clusters has identified proteins that are specific to filamentous fungal species and a group of proteins found only in plant pathogens. By comparing the gene inventories of filamentous, ascomycetous phytopathogenic and free-living species of fungi, we have identified a set of gene families that appear to have expanded during the evolution of phytopathogens and may therefore serve important roles in plant disease. We have also characterised the predicted set of secreted proteins encoded by each genome and identified a set of protein families which are significantly over-represented in the secretomes of plant pathogenic fungi, including putative effector proteins that might perturb host cell biology during plant infection. The results demonstrate the potential of comparative genome analysis for exploring the evolution of eukaryotic microbial pathogenesis.

Identification of novel genes in intestinal tissue that are regulated after infection with an intestinal nematode parasite.

ABSTRACT Infection of resistant or susceptible mice with Trichuris muris provokes mesenteric lymph node responses which are polarized towards Th2 or Th1, respectively. These responses are well documented in the literature. In contrast, little is known about the local responses occurring within the infected intestine. Through microarray analyses, we demonstrate that the gene expression profile of infected gut tissue differs according to whether the parasite is expelled or not. Genes differentially regulated postinfection in resistant BALB/c mice include several antimicrobial genes, in particular, intelectin (Itln). In contrast, analyses in AKR mice which ultimately progress to chronic infection provide evidence for a Th1-dominated mucosa with up-regulated expression of genes regulated by gamma interferon. Increases in the expression of genes associated with tryptophan metabolism were also apparent with the coinduction of tryptophanyl tRNA synthetase (Wars) and indoleamine-2,3-dioxygenase (Indo). With the emerging literature on the role of these gene products in the suppression of T-cell responses in vitro and in vivo, their up-regulated expression here may suggest a role for tryptophan metabolism in the parasite survival strategy.

A systematic strategy for large-scale analysis of genotype phenotype correlations: identification of candidate genes involved in African trypanosomiasis.

It is increasingly common to combine Microarray and Quantitative Trait Loci data to aid the search for candidate genes responsible for phenotypic variation. Workflows provide a means of systematically processing these large datasets and also represent a framework for the re-use and the explicit declaration of experimental methods. In this article, we highlight the issues facing the manual analysis of microarray and QTL data for the discovery of candidate genes underlying complex phenotypes. We show how automated approaches provide a systematic means to investigate genotype–phenotype correlations. This methodology was applied to a use case of resistance to African trypanosomiasis in the mouse. Pathways represented in the results identified Daxx as one of the candidate genes within the Tir1 QTL region. Subsequent re-sequencing in Daxx identified a deletion of an amino acid, identified in susceptible mouse strains, in the Daxx–p53 protein-binding region. This supports recent experimental evidence that apoptosis could be playing a role in the trypanosomiasis resistance phenotype. Workflows developed in this investigation, including a guide to loading and executing them with example data, are available at http://workflows.mygrid.org.uk/repository/myGrid/PaulFisher/.

Feedback-based annotation, selection and refinement of schema mappings for dataspaces

The specification of schema mappings has proved to be time and resource consuming, and has been recognized as a critical bottleneck to the large scale deployment of data integration systems. In an attempt to address this issue, dataspaces have been proposed as a data management abstraction that aims to reduce the up-front cost required to setup a data integration system by gradually specifying schema mappings through interaction with end users in a pay-as-you-go fashion. As a step in this direction, we explore an approach for incrementally annotating schema mappings using feedback obtained from end users. In doing so, we do not expect users to examine mapping specifications; rather, they comment on results to queries evaluated using the mappings. Using annotations computed on the basis of user feedback, we present a method for selecting from the set of candidate mappings, those to be used for query evaluation considering user requirements in terms of precision and recall. In doing so, we cast mapping selection as an optimization problem. Mapping annotations may reveal that the quality of schema mappings is poor. We also show how feedback can be used to support the derivation of better quality mappings from existing mappings through refinement. An evolutionary algorithm is used to efficiently and effectively explore the large space of mappings that can be obtained through refinement. The results of evaluation exercises show the effectiveness of our solution for annotating, selecting and refining schema mappings.

e-Fungi: a data resource for comparative analysis of fungal genomes

BackgroundThe number of sequenced fungal genomes is ever increasing, with about 200 genomes already fully sequenced or in progress. Only a small percentage of those genomes have been comprehensively studied, for example using techniques from functional genomics. Comparative analysis has proven to be a useful strategy for enhancing our understanding of evolutionary biology and of the less well understood genomes. However, the data required for these analyses tends to be distributed in various heterogeneous data sources, making systematic comparative studies a cumbersome task. Furthermore, comparative analyses benefit from close integration of derived data sets that cluster genes or organisms in a way that eases the expression of requests that clarify points of similarity or difference between species.DescriptionTo support systematic comparative analyses of fungal genomes we have developed the e-Fungi database, which integrates a variety of data for more than 30 fungal genomes. Publicly available genome data, functional annotations, and pathway information has been integrated into a single data repository and complemented with results of comparative analyses, such as MCL and OrthoMCL cluster analysis, and predictions of signaling proteins and the sub-cellular localisation of proteins. To access the data, a library of analysis tasks is available through a web interface. The analysis tasks are motivated by recent comparative genomics studies, and aim to support the study of evolutionary biology as well as community efforts for improving the annotation of genomes. Web services for each query are also available, enabling the tasks to be incorporated into workflows.ConclusionThe e-Fungi database provides fungal biologists with a resource for comparative studies of a large range of fungal genomes. Its analysis library supports the comparative study of genome data, functional annotation, and results of large scale analyses over all the genomes stored in the database. The database is accessible at http://www.e-fungi.org.uk, as is the WSDL for the web services.

Information quality in proteomics

Proteomics, the study of the protein complement of a biological system, is generating increasing quantities of data from rapidly developing technologies employed in a variety of different experimental workflows. Experimental processes, e.g. for comparative 2D gel studies or LC-MS/MS analyses of complex protein mixtures, involve a number of steps: from experimental design, through wet and dry lab operations, to publication of data in repositories and finally to data annotation and maintenance. The presence of inaccuracies throughout the processing pipeline, however, results in data that can be untrustworthy, thus offsetting the benefits of high-throughput technology. While researchers and practitioners are generally aware of some of the information quality issues associated with public proteomics data, there are few accepted criteria and guidelines for dealing with them. In this article, we highlight factors that impact on the quality of experimental data and review current approaches to information quality management in proteomics. Data quality issues are considered throughout the lifecycle of a proteomics experiment, from experiment design and technique selection, through data analysis, to archiving and sharing.

Incrementally improving dataspaces based on user feedback

One aspect of the vision of dataspaces has been articulated as providing various benefits of classical data integration with reduced up-front costs. In this paper, we present techniques that aim to support schema mapping specification through interaction with end users in a pay-as-you-go fashion. In particular, we show how schema mappings, that are obtained automatically using existing matching and mapping generation techniques, can be annotated with metrics estimating their fitness to user requirements using feedback on query results obtained from end users. Using the annotations computed on the basis of user feedback, and given user requirements in terms of precision and recall, we present a method for selecting the set of mappings that produce results meeting the stated requirements. In doing so, we cast mapping selection as an optimization problem. Feedback may reveal that the quality of schema mappings is poor. We show how mapping annotations can be used to support the derivation of better quality mappings from existing mappings through refinement. An evolutionary algorithm is used to efficiently and effectively explore the large space of mappings that can be obtained through refinement. User feedback can also be used to annotate the results of the queries that the user poses against an integration schema. We show how estimates for precision and recall can be computed for such queries. We also investigate the problem of propagating feedback about the results of (integration) queries down to the mappings used to populate the base relations in the integration schema.

Pay-as-you-go mapping selection in dataspaces

The vision of dataspaces proposes an alternative to classical data integration approaches with reduced up-front costs followed by incremental improvement on a pay-as-you-go basis. In this paper, we demonstrate DSToolkit, a system that allows users to provide feedback on results of queries posed over an integration schema. Such feedback is then used to annotate the mappings with their respective precision and recall. The system then allows a user to state the expected levels of precision (or recall) that the query results should exhibit and, in order to produce those results, the system selects those mappings that are predicted to meet the stated constraints.

A systematic strategy for the discovery of candidate genes responsible for phenotypic variation.

IntroductionThe use of Quantitative Trait Loci (QTL) data is increasingly used to aid in the discovery of candidate genes involved in phenotypic variation. Tens to hundreds of genes, however, may lie within even well defined QTL. It is therefore vital that the identification, selection and functional testing of candidate Quantitative Trait genes (QTg) are carried out systematically, and without bias [1]. With the advent of microarrays, researchers are able to directly examine the expression of all genes on a genome wide scale, including those underlying QTL regions.The scale of data being generated by such high-throughput experiments has led some investigators to follow a hypothesis-driven approach [2]. Although these techniques for candidate gene identification are valid, they run the risk of overlooking genes that have less obvious associations with the phenotype. By making selections based on prior assumptions of what processes may be involved, the genes that may actually be involved in the phenotype can be overlooked. A further complication is that the use of ad hoc methods for candidate gene identification are inherently difficult to replicate and are compounded by poor documentation of the methods used to generate and capture the data from such investigations in published literature.With an ever increasing number of institutes offering programmatic access to their resources in the form of web services, however, experiments previously conducted manually can now be replaced by automated experiments, capable of processing a far greater volume of data. By reconstructing the original investigation methods in the form of workflows, we are now able to pass data directly from one service to the next. This enables us to process the data in a much more systematic, un-biased, and explicit manner.MethodsWe propose a data-driven methodology that identifies the known pathways that intersect a QTL and those derived from a set of differentially expressed genes from a microarray study. This methodology is implemented systematically through the use of web services and workflows. For the purpose of implementing this systematic pathway-driven approach, we have chosen to use the Taverna workbench [3].Results and DiscussionPreliminary studies into the modes of resistance to African Trypanosomiasis were carried out for the mouse model organism. These studies illustrated how the large-scale analysis of microarray gene expression and QTL data, investigated at the level of biological pathways, enables links between genotype and phenotype to be successfully established [4]. This approach was implemented systematically through the use of explicitly defined workflows.

Model-driven user interfaces for bioinformatics data resources: regenerating the wheel as an alternative to reinventing it

BackgroundThe proliferation of data repositories in bioinformatics has resulted in the development of numerous interfaces that allow scientists to browse, search and analyse the data that they contain. Interfaces typically support repository access by means of web pages, but other means are also used, such as desktop applications and command line tools. Interfaces often duplicate functionality amongst each other, and this implies that associated development activities are repeated in different laboratories. Interfaces developed by public laboratories are often created with limited developer resources. In such environments, reducing the time spent on creating user interfaces allows for a better deployment of resources for specialised tasks, such as data integration or analysis. Laboratories maintaining data resources are challenged to reconcile requirements for software that is reliable, functional and flexible with limitations on software development resources.ResultsThis paper proposes a model-driven approach for the partial generation of user interfaces for searching and browsing bioinformatics data repositories. Inspired by the Model Driven Architecture (MDA) of the Object Management Group (OMG), we have developed a system that generates interfaces designed for use with bioinformatics resources. This approach helps laboratory domain experts decrease the amount of time they have to spend dealing with the repetitive aspects of user interface development. As a result, the amount of time they can spend on gathering requirements and helping develop specialised features increases. The resulting system is known as Pierre, and has been validated through its application to use cases in the life sciences, including the PEDRoDB proteomics database and the e-Fungi data warehouse.ConclusionMDAs focus on generating software from models that describe aspects of service capabilities, and can be applied to support rapid development of repository interfaces in bioinformatics. The Pierre MDA is capable of supporting common database access requirements with a variety of auto-generated interfaces and across a variety of repositories. With Pierre, four kinds of interfaces are generated: web, stand-alone application, text-menu, and command line. The kinds of repositories with which Pierre interfaces have been used are relational, XML and object databases.