Albert Burger

EMAP and EMAGE: a framework for understanding spatially organized data.

The Edinburgh Mouse Atlas Project (EMAP) is a time-series of mouse-embryo volumetric models. The models provide a context-free spatial framework onto which structural interpretations and experimental data can be mapped. This enables collation, comparison, and query of complex spatial patterns with respect to each other and with respect to known or hypothesized structure. The atlas also includes a time-dependent anatomical ontology and mapping between the ontology and the spatial models in the form of delineated anatomical regions or tissues. The models provide a natural, graphical context for browsing and visualizing complex data.The Edinburgh Mouse Atlas Gene-Expression Database (EMAGE) is one of the first applications of the EMAP framework and provides a spatially mapped gene-expression database with associated tools for data mapping, submission, and query. In this article, we describe the underlying principles of the Atlas and the gene-expression database, and provide a practical introduction to the use of the EMAP and EMAGE tools, including use of new techniques for whole body gene-expression data capture and mapping.

An internet-accessible database of mouse developmental anatomy based on a systematic nomenclature

This paper reports an internet-accessible database of mouse developmental anatomy (DMDA) that currently holds a hierarchy of the names and synonyms of the tissues in the first 22 Theiler stages of development (E1-E13.5), together with other appropriate information. The purposes of the database are to provide, first, a nomenclature for analyzing normal and mutant mouse anatomy, and second a language for inputting, storing and querying gene-expression and other spatially organized data. DMDA currently contains some 6900 named and staged tissues (e.g. 360 and 1161 tissues in Theiler stage (TS) 14 (E9) and TS22 (E13.5) embryos). DMDA will be extended to include further lineage and other data when it becomes available. The database can be interactively accessed over the internet using either a Java or a non-Java WWW browser at http://genex.hgu.mrc.ac.uk/.

Digital Atlasing and Standardization in the Mouse Brain

Digital brain atlases are used in neuroscience to characterize the spatial organization of neuronal structures [1]–[3], for planning and guidance during neurosurgery [4], [5], and as a reference for interpreting other modalities such as gene expression or proteomic data [6]–[9]. The field of digital atlasing is extensive, and includes high quality brain atlases of the mouse [10], rat [11], rhesus macaque [12], human [13], [14], and several other model organisms. In addition to atlases based on histology, [11], [15], [16], magnetic resonance imaging [10], [17], and positron emission tomography [11], modern digital atlases often use probabilistic and multimodal techniques [18], [19], as well as sophisticated visualization software [20], [21]. Whether atlases involve detailed visualization of structures of a single or small group of specimens [6], [22], [23] or averages over larger populations [18], [24], much of the work in developing digital brain atlases is from the perspective of the user of a single resource. This is often due largely to the challenges of data generation, maintenance, and resources management [25], [26]. A more recent goal of many neuroscientists is to connect multiple and diverse resources to work in a collaborative manner using an atlas based framework [2], [19]. This vision is appealing as, ideally, researchers would be able to share their data and analyses with others, regardless of where they or the data are located. An important step in this direction is the specification of a common frame of reference across specimens and resources (either as coordinate, ontology, or region of interest) that is adopted by the community. In this perspective, we propose a collaborative digital atlasing framework for coordinating mouse brain research that allows access to data, tools, and analyses from multiple sources.

Bioinformatics integration and agent technology

Vast amounts of life sciences data are scattered around the world in the form of a variety of heterogeneous data sources. The need to be able to co-relate relevant information is fundamental to increase the overall knowledge and understanding of a specific subject. Bioinformaticians aspire to find ways to integrate biological data sources for this purpose and system integration is a very important research topic. The purpose of this paper is to provide an overview of important integration issues that should be considered when designing a bioinformatics integration system. The currently prevailing approach for integration is presented with examples of bioinformatics information systems together with their main characteristics. Here, we introduce agent technology and we argue why it provides an appropriate solution for designing bioinformatics integration systems.

Anatomy Ontologies for Bioinformatics

Existing Anatomy Ontologies for Human, Model Organisms and Plants.- Anatomical Ontologies for Model Organisms: The Fungi and Animals.- Plant Structure Ontology (PSO)- A Morphological and Anatomical Ontology of Flowering Plants.- Anatomy for Clinical Terminology.- The Foundational Model of Anatomy Ontology.- Towards a Disease Ontology.- Engineering and Linking of Anatomy Ontologies.- Ontology Alignment and Merging.- COBrA and COBrA-CT: Ontology Engineering Tools.- XSPAN - A Cross-Species Anatomy Network.- Searching Biomedical Literature with Anatomy Ontologies.- Anatomy Ontologies and Spatio-Temporal Atlases.- Anatomical Ontologies: Linking Names to Places in Biology.- Time in Anatomy.- The Edinburgh Mouse Atlas.- The Smart Atlas: Spatial and Semantic Strategies for Multiscale Integration of Brain Data.- Anatomy Ontologies - Modelling Principles.- Modelling Principles and Methodologies - Relations in Anatomical Ontologies.- Modeling Principles and Methodologies - Spatial Representation and Reasoning.- CARO - The Common Anatomy Reference Ontology.

Formalization of mouse embryo anatomy

MOTIVATION The Edinburgh Mouse Atlas and Gene Expression Database project has developed a digital atlas of mouse development to provide a spatio-temporal framework for spatially mapped data such as in situ gene expression and cell lineage. As part of this database, a mouse embryo anatomy ontology has been created. A formalization of this anatomy is required to document its precise semantics and how it is used in the context of the Mouse Atlas. RESULTS The paper describes the existing anatomy ontology and formalizes aspects of it using a predicate logic based approach. It therefore provides a guide for users of the current version of the ontology, as well as the basis for a description of the anatomy using an ontology language, such as OWL, thus enabling future work on reasoning about the Mouse Atlas in the context of an intelligent gene expression bioinformatics workflow system. The logic has been implemented in a Prolog prototype. AVAILABILITY The Mouse Atlas is available on-line at http://genex.hgu.mrc.ac.uk

Advanced distributed simulation: a review of developments and their implication for data collection and analysis

Abstract The field of advanced distributed simulation (ADS) has emerged and evolved since the 1980s. Developments are driven largely from within the United States (US) Department of Defense (DoD). This paper summarizes the history and evolution of ADS, detailing recent developments, notably the high level architecture (HLA). The implications of the HLA for data collection and analysis are discussed. Current approaches are reviewed including one of the largest ADS projects to date, the synthetic theatre of war (STOW), and its approach to data collection. We consider the impact of ADS beyond military training and discuss future development of ADS technology.

Performance of multiversion and distributed two-phase locking concurrency control mechanisms in distributed databases

In this paper, we have presented a detailed simulation study of a distributed multiversion and a distributed two-phase locking concurrency control mechanism (CCM). Our experiment concentrated on measuring the effect of message overhead, read:write ratios, data partitioning, and partial replication on the performance of these mechanisms. The effect of these parameters has not been investigated in any previous work. We simultated a blind-write model for two reasons: (a) all other works studied the behavior of multiversion CCMs under read-before-write model and observed a similar result, and (b) the performance of any multiversion CCM has not been studied under a blind-write model. A blind-write model is not unrealistic, and intutively the multiversion should provide much better performance. We observed that multiversion outperforms wound-wait (WW) in both partitioned and partially replicated databases. Multi version (MV) handles read-only and write-only transactions efficiently, and after a certain write percentage the throughput improves with this percentage. The message overhead progressively becomes less significant as the MPL (multiprogramming level) increases, indicating that in a heavily loaded system the throughput is least sensitive to message cost. We found that in the partially replicated case, 50% write does not show the lowest performance, as observed in the partitioned case.

Performance measurement of main memory database recovery algorithms based on update-in-place and shadow approaches

The authors present a performance study of two main memory database recovery algorithms, one using a shadow approach and the other update-in-place. The results show that in main memory databases, shadow approach performs better than update-in-place. The authors introduce minor improvements to the shadow scheme and show that the modified algorithm does show further performance improvement. >

Biomedical semantics in the Semantic Web

The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences?We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th.