MOTIVATION: A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The gene lists resulting from a group of studies answering the same, or similar, questions can be combined by ranking aggregation methods to find a consensus or a more reliable answer. Evaluating a ranking aggregation method on a specific type of data before using it is required to support the reliability since the property of a dataset can influence the performance of an algorithm. Such evaluation on gene lists is usually based on a simulated database because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. RESULTS: In this study, a group of existing methods and their variations that are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data were used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomic data, with various heterogeneity of quality, noise level and a mix of unranked and ranked data using 20 000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (non-small cell lung cancer) and bacteria (macrophage apoptosis) was performed. We summarize the results of our evaluation in a simple flowchart to select a ranking aggregation method, and in an automated implementation using the meta-analysis by information content algorithm to infer heterogeneity of data quality across input datasets. AVAILABILITY AND IMPLEMENTATION: The code for simulated data generation and running edited version of algorithms: https://github.com/baillielab/comparison_of_RA_methods. Code to perform an optimal selection of methods based on the results of this review, using the MAIC algorithm to infer the characteristics of an input dataset, can be downloaded here: https://github.com/baillielab/maic. An online service for running MAIC: https://baillielab.net/maic. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The visualisation of genetic and genomic maps aligned within and between species and across data sources can be used to inform studies of genome evolution, assist genome assembly projects and aid gene discovery and identification. Whilst annotation, integration and exploration of assembled genome sequences is well supported, there are fewer tools available which can display genetic maps for less well-characterized species, and integrate these maps with annotated reference genomes to support cross species comparisons. RESULTS: We have developed a desktop application to draw and align genetic and genomic maps, retrieved from remote data sources or loaded as local files. Maps can be retrieved from our public map database ArkDB or from any Ensembl data source (i.e. Ensembl and Ensembl Genomes). By using the JEnsembl API, maps can be drawn for any release version of any of the thousands of species present in Ensembl data sources, allowing not only inter-specific comparisons, but also comparisons between different versions/revisions of assembled genomes. Maps can be aligned by relating identical or synonymous markers across maps, or through the gene homology/orthology relationship data stored in the Ensembl Compara databases, allowing ready visualization of regions of conserved synteny between species. The map drawing canvas is highly configurable, supports interactive exploration of maps, markers and relationships and allows export of publication quality graphics. CONCLUSIONS: ArkMAP allows users to draw and interactively explore gene and variation maps for any version of any annotated genome curated in the Ensembl data sources, and to integrate local mapping data. The maps and inter-map relationships drawn are highly configurable and ArkMAP may be used to produce publication quality graphics. ArkMAP is freely available as an auto-updating Java 'Web Start' application, or as a standalone archived application.

We describe a visual data cleansing application for pedigree genotype data, which is used to redeem otherwise unusable pedigree data sets. Biologists and bioinformaticians dynamically and iteratively mask pieces of information from a dirty data set and graduate towards a usable cleaned version of the data, which can then be saved and used in ongoing biological analyses. Cleansing of such data is complicated over and above simple error cleaning in that change or masking of the pedigree structure may shift errors to new parts of the pedigree. Thus a branching history of data manipulations is kept to allow users to restore the data and visualisation to any of the previous states it has travelled through.

MOTIVATION: The Ensembl Project provides release-specific Perl APIs for efficient high-level programmatic access to data stored in various Ensembl database schema. Although Perl scripts are perfectly suited for processing large volumes of text-based data, Perl is not ideal for developing large-scale software applications nor embedding in graphical interfaces. The provision of a novel Java API would facilitate type-safe, modular, object-orientated development of new Bioinformatics tools with which to access, analyse and visualize Ensembl data. RESULTS: The JEnsembl API implementation provides basic data retrieval and manipulation functionality from the Core, Compara and Variation databases for all species in Ensembl and EnsemblGenomes and is a platform for the development of a richer API to Ensembl datasources. The JEnsembl architecture uses a text-based configuration module to provide evolving, versioned mappings from database schema to code objects. A single installation of the JEnsembl API can therefore simultaneously and transparently connect to current and previous database instances (such as those in the public archive) thus facilitating better analysis repeatability and allowing 'through time' comparative analyses to be performed. AVAILABILITY: Project development, released code libraries, Maven repository and documentation are hosted at SourceForge (http://jensembl.sourceforge.net).

BACKGROUND: Pedigree genotype datasets are used for analysing genetic inheritance and to map genetic markers and traits. Such datasets consist of hundreds of related animals genotyped for thousands of genetic markers and invariably contain multiple errors in both the pedigree structure and in the associated individual genotype data. These errors manifest as apparent inheritance inconsistencies in the pedigree, and invalidate analyses of marker inheritance patterns across the dataset. Cleaning raw datasets of bad data points (incorrect pedigree relationships, unreliable marker assays, suspect samples, bad genotype results etc.) requires expert exploration of the patterns of exposed inconsistencies in the context of the inheritance pedigree. In order to assist this process we are developing VIPER (Visual Pedigree Explorer), a software tool that integrates an inheritance-checking algorithm with a novel space-efficient pedigree visualisation, so that reported inheritance inconsistencies are overlaid on an interactive, navigable representation of the pedigree structure. METHODS AND RESULTS: This paper describes an evaluation of how VIPER displays the different scales and types of dataset that occur experimentally, with a description of how VIPER's display interface and functionality meet the challenges presented by such data. We examine a range of possible error types found in real and simulated pedigree genotype datasets, demonstrating how these errors are exposed and explored using the VIPER interface and we evaluate the utility and usability of the interface to the domain expert.Evaluation was performed as a two stage process with the assistance of domain experts (geneticists). The initial evaluation drove the iterative implementation of further features in the software prototype, as required by the users, prior to a final functional evaluation of the pedigree display for exploring the various error types, data scales and structures. CONCLUSIONS: The VIPER display was shown to effectively expose the range of errors found in experimental genotyped pedigrees, allowing users to explore the underlying causes of reported inheritance inconsistencies. This interface will provide the basis for a full data cleaning tool that will allow the user to remove isolated bad data points, and reversibly test the effect of removing suspect genotypes and pedigree relationships.

Abstract Genetic analysis of a breeding animal population involves determining the inheritance pattern of genotypes for multiple genetic markers across the individuals in the population pedigree structure. However, experimental pedigree genotype data invariably contains errors in both the pedigree structure and in the associated individual genotypes, introducing inconsistencies into the dataset, rendering them useless for further analysis. The resolution of these errors requires consideration of genotype inheritance patterns in the context of the pedigree structure. Existing pedigree visualisations are typically more suited to human pedigrees and are less suitable for large complex animal pedigrees which may exhibit cross generational inbreeding. Similarly, table‐based viewers of genotype marker data can highlight where errors become apparent but lack the functionality and interactive visual feedback to allow users to locate the origin of errors within the pedigree. In this paper, we detail a design study steered by biologists who work with pedigree data, and describe successive iterations through approaches and prototypes for viewing genotyping errors in the context of a displayed pedigree. We describe how each approach performs with real pedigree genotype data and why eventually we deemed them unsuitable. Finally, a novel prototype visualisation for pedigrees, which we term the ‘sandwich view’, is detailed and we demonstrate how the approach effectively communicates errors in the pedigree context, supporting the biologist in the error identification task.

The Ruby programming language has a lot to offer to any scientist with electronic data to process. Not only is the initial learning curve very shallow, but its reflection and meta-programming capabilities allow for the rapid creation of relatively complex applications while still keeping the code short and readable. This paper provides a gentle introduction to this scripting language for researchers without formal informatics training such as many wet-lab scientists. We hope this will provide such researchers an idea of how powerful a tool Ruby can be for their data management tasks and encourage them to learn more about it.

BACKGROUND: Genomic analysis, particularly for less well-characterized organisms, is greatly assisted by performing comparative analyses between different types of genome maps and across species boundaries. Various providers publish a plethora of on-line resources collating genome mapping data from a multitude of species. Datasources range in scale and scope from small bespoke resources for particular organisms, through larger web-resources containing data from multiple species, to large-scale bioinformatics resources providing access to data derived from genome projects for model and non-model organisms. The heterogeneity of information held in these resources reflects both the technologies used to generate the data and the target users of each resource. Currently there is no common information exchange standard or protocol to enable access and integration of these disparate resources. Consequently data integration and comparison must be performed in an ad hoc manner. RESULTS: We have developed a simple generic XML schema (GenomicMappingData.xsd - GMD) to allow export and exchange of mapping data in a common lightweight XML document format. This schema represents the various types of data objects commonly described across mapping datasources and provides a mechanism for recording relationships between data objects. The schema is sufficiently generic to allow representation of any map type (for example genetic linkage maps, radiation hybrid maps, sequence maps and physical maps). It also provides mechanisms for recording data provenance and for cross referencing external datasources (including for example ENSEMBL, PubMed and Genbank.). The schema is extensible via the inclusion of additional datatypes, which can be achieved by importing further schemas, e.g. a schema defining relationship types. We have built demonstration web services that export data from our ArkDB database according to the GMD schema, facilitating the integration of data retrieval into Taverna workflows. CONCLUSION: The data exchange standard we present here provides a useful generic format for transfer and integration of genomic and genetic mapping data. The extensibility of our schema allows for inclusion of additional data and provides a mechanism for typing mapping objects via third party standards. Web services retrieving GMD-compliant mapping data demonstrate that use of this exchange standard provides a practical mechanism for achieving data integration, by facilitating syntactically and semantically-controlled access to the data.

Threshold devices present information gathered from the home's surroundings to give new views on the domestic situation. We built two prototypes of different threshold devices and studied them in field trials with participant households. The Local Barometer displays online text and images related to the home's locality depending on the local wind conditions to give an impression of the sociocultural surroundings. The Plane Tracker tracks aircraft passing overhead and imagines their flights onscreen to resource an understanding of the home's global links. Our studies indicated that the experiences they provided were compelling, that participants could and did interpret the devices in various ways, that their form designs were appropriate for domestic environments, that using ready-made information contributed to the richness of the experiences, and that situating the information they provided with respect to the home and its locality was important for the ways people engaged with them.

This article describes field trials of 3 electronic furniture prototypes designed to encourage ludic engagement in the home. The Drift Table shows slowly scrolling aerial photography controlled by the weight of the objects on its surface. The History Tablecloth creates slowly growing “halos” around things left on it. The Key Table measures the force with which people put things on it and tilts a picture frame to indicate their mood. The pieces were loaned to different households for periods of 1 to 3 months. Because they were designed for user appropriation, a hypothesis-testing paradigm is inappropriate for evaluating their success. The focus instead was on gathering rich, multilayered accounts of people's experience through ethnographic observations and documentary videos. The results helped assess the particular designs, draw lessons for ludic design more generally, and reflect on field methods for evaluating open-ended designs.

The History Tablecloth is a flexible substrate screen-printed with electroluminescent material forming a grid of lace-like elements. When objects are left on the table, cells beneath them light to form a halo that grows over a period of hours, highlighting the flow of objects in the home. The Tablecloth explores an approach to design that emphasises engaging, open-ended situations over defined utilitarian purposes. Long-term deployment of the History Tablecloth in a volunteer household revealed complex ways that people experienced and interacted with the Tablecloth. Beyond evoking reflection on the flow of objects over a particular table, the Tablecloth served as a ground for interpretative reflection about technology, an asset for social interaction, and an aesthetic object. Even behaviours we saw as system errors were interpreted by the users as interactively rich. Their experience highlights the subtlety of domestic ubiquitous computing, illustrating alternatives to traditional views of technology's domestic role.