Representing default knowledge in biomedical ontologies: application to the integration of anatomy and phenotype ontologies

Background  

Current efforts within the biomedical ontology community focus on achieving interoperability between various biomedical ontologies that cover a range of diverse domains. Achieving this interoperability will contribute to the creation of a rich knowledge base that can be used for querying, as well as generating and testing novel hypotheses. The OBO Foundry principles, as applied to a number of biomedical ontologies, are designed to facilitate this interoperability. However, semantic extensions are required to meet the OBO Foundry interoperability goals. Inconsistencies may arise when ontologies of properties – mostly phenotype ontologies – are combined with ontologies taking a canonical view of a domain – such as many anatomical ontologies. Currently, there is no support for a correct and consistent integration of such ontologies.     

Populous: a tool for building OWL ontologies from templates

Background

Computing accurate nucleic acid melting temperatures has become a crucial step for the efficiency and the optimisation of numerous molecular biology techniques such as in situ hybridization, PCR, antigene targeting, and microarrays. MELTING is a free open source software which computes the enthalpy, entropy and melting temperature of nucleic acids. MELTING 4.2 was able to handle several types of hybridization such as DNA/DNA, RNA/RNA, DNA/RNA and provided corrections to melting temperatures due to the presence of sodium. The program can use either an approximative approach or a more accurate Nearest-Neighbor approach.

Results

Two new versions of the MELTING software have been released. MELTING 4.3 is a direct update of version 4.2, integrating newly available thermodynamic parameters for inosine, a modified adenine base with an universal base capacity, and incorporates a correction for magnesium. MELTING 5 is a complete reimplementation which allows much greater flexibility and extensibility. It incorporates all the thermodynamic parameters and corrections provided in MELTING 4.x and introduces a large set of thermodynamic formulae and parameters, to facilitate the calculation of melting temperatures for perfectly matching sequences, mismatches, bulge loops, CNG repeats, dangling ends, inosines, locked nucleic acids, 2-hydroxyadenines and azobenzenes. It also includes temperature corrections for monovalent ions (sodium, potassium, Tris), magnesium ions and commonly used denaturing agents such as formamide and DMSO.

Conclusions

MELTING is a useful and very flexible tool for predicting melting temperatures using approximative formulae or Nearest-Neighbor approaches, where one can select different sets of Nearest-Neighbor parameters, corrections and formulae. Both versions are freely available at http://sourceforge.net/projects/melting/and at http://www.ebi.ac.uk/compneur-srv/melting/under the terms of the GPL license.     

Panalysis: a new spreadsheet-based tool for pandemic planning

Publicly available influenza modeling tools are of limited use to hospitals and local communities in planning for a severe pandemic. We developed Panalysis, a new tool to estimate the likely healthcare consequences of a pandemic and to aid hospitals in the development of mitigation and response strategies. By way of example, we demonstrate how Panalysis can be used to plan for a 1918-like flu pandemic. We discuss potential future applications of this tool.     

Creating a knowledge translation trainee collaborative: from conceptualization to lessons learned in the first year

Background

Patient involvement in healthcare represents the means by which to achieve a healthcare system that is responsive to patient needs and values. Characterization and evaluation of strategies for involving patients in their healthcare may benefit from a knowledge translation (KT) approach. The purpose of this knowledge synthesis is to develop a conceptual framework for patient-mediated KT interventions.

Methods

A preliminary conceptual framework for patient-mediated KT interventions was compiled to describe intended purpose, recipients, delivery context, intervention, and outcomes. A realist review will be conducted in consultation with stakeholders from the arthritis and cancer fields to explore how these interventions work, for whom, and in what contexts. To identify patient-mediated KT interventions in these fields, we will search MEDLINE, the Cochrane Library, and EMBASE from 1995 to 2010; scan references of all eligible studies; and examine five years of tables of contents for journals likely to publish quantitative or qualitative studies that focus on developing, implementing, or evaluating patient-mediated KT interventions. Screening and data collection will be performed independently by two individuals.

Conclusions

The conceptual framework of patient-mediated KT options and outcomes could be used by healthcare providers, managers, educationalists, patient advocates, and policy makers to guide program planning, service delivery, and quality improvement and by us and other researchers to evaluate existing interventions or develop new interventions. By raising awareness of options for involving patients in improving their own care, outcomes based on using a KT approach may lead to greater patient-centred care delivery and improved healthcare outcomes.     

Populous: a tool for building OWL ontologies from templates

BACKGROUND: Ontologies are being developed for the life sciences to standardise the way we describe and interpret the wealth of data currently being generated. As more ontology based applications begin to emerge, tools are required that enable domain experts to contribute their knowledge to the growing pool of ontologies. There are many barriers that prevent domain experts engaging in the ontology development process and novel tools are needed to break down these barriers to engage a wider community of scientists. RESULTS: We present Populous, a tool for gathering content with which to construct an ontology. Domain experts need to add content, that is often repetitive in its form, but without having to tackle the underlying ontological representation. Populous presents users with a table based form in which columns are constrained to take values from particular ontologies. Populated tables are mapped to patterns that can then be used to automatically generate the ontology's content. These forms can be exported as spreadsheets, providing an interface that is much more familiar to many biologists. CONCLUSIONS: Populous's contribution is in the knowledge gathering stage of ontology development; it separates knowledge gathering from the conceptualisation and axiomatisation, as well as separating the user from the standard ontology authoring environments. Populous is by no means a replacement for standard ontology editing tools, but instead provides a useful platform for engaging a wider community of scientists in the mass production of ontology content.     

Biomedical ontologies: a functional perspective

The information explosion in biology makes it difficult for researchers to stay abreast of current biomedical knowledge and to make sense of the massive amounts of online information. Ontologies--specifications of the entities, their attributes and relationships among the entities in a domain of discourse--are increasingly enabling biomedical researchers to accomplish these tasks. In fact, bio-ontologies are beginning to proliferate in step with accruing biological data. The myriad of ontologies being created enables researchers not only to solve some of the problems in handling the data explosion but also introduces new challenges. One of the key difficulties in realizing the full potential of ontologies in biomedical research is the isolation of various communities involved: some workers spend their career developing ontologies and ontology-related tools, while few researchers (biologists and physicians) know how ontologies can accelerate their research. The objective of this review is to give an overview of biomedical ontology in practical terms by providing a functional perspective--describing how bio-ontologies can and are being used. As biomedical scientists begin to recognize the many different ways ontologies enable biomedical research, they will drive the emergence of new computer applications that will help them exploit the wealth of research data now at their fingertips.     

OnEX: Exploring changes in life science ontologies

Background  

Numerous ontologies have recently been developed in life sciences to support a consistent annotation of biological objects, such as genes or proteins. These ontologies underlie continuous changes which can impact existing annotations. Therefore, it is valuable for users of ontologies to study the stability of ontologies and to see how many and what kind of ontology changes occurred.     

Anatomical ontologies: names and places in biology

Ontology has long been the preserve of philosophers and logicians. Recently, ideas from this field have been picked up by computer scientists as a basis for encoding knowledge and with the hope of achieving interoperability and intelligent system behavior. In bioinformatics, ontologies might allow hitherto impossible query and data-mining activities. We review the use of anatomy ontologies to represent space in biological organisms, specifically mouse and human.     

MetNetGE: interactive views of biological networks and ontologies

Background  

Linking high-throughput experimental data with biological networks is a key step for understanding complex biological systems. Currently, visualization tools for large metabolic networks often result in a dense web of connections that is difficult to interpret biologically. The MetNetGE application organizes and visualizes biological networks in a meaningful way to improve performance and biological interpretability.     

Phenotype ontologies: the bridge between genomics and evolution

Understanding the developmental and genetic underpinnings of particular evolutionary changes has been hindered by inadequate databases of evolutionary anatomy and by the lack of a computational approach to identify underlying candidate genes and regulators. By contrast, model organism studies have been enhanced by ontologies shared among genomic databases. Here, we suggest that evolutionary and genomics databases can be developed to exchange and use information through shared phenotype and anatomy ontologies. This would facilitate computing on evolutionary questions pertaining to the genetic basis of evolutionary change, the genetic and developmental bases of correlated characters and independent evolution, biomedical parallels to evolutionary change, and the ecological and paleontological correlates of particular types of change in genes, gene networks and developmental pathways.     

遨游学海淡泊人生

薛全福教授是一位著名的病理生理学家。他致力于肺心病发病机制和南极医学的研究,取得多项成果。他娓娓道来亲赴南极的实地体验,给人以振奋与力量。他不避艰难及善于团结协作的精神,值得发扬。     

OntoSlug: a dynamic visual front-end program for ontologies

The display of ontological information has become a crucial factor over the last decade in systems biology. The possibility to compare different ontological systems in a single application has however not been answered with an appropriate application. OntoSlug is an easy to use application that tries to fill this need. OntoSlug has been developed for use in classroom settings and scientific laboratory environment.     

Bioinformatics: from genome data to biological knowledge

Recently, molecular biologists have sequenced about a dozen bacterial genomes and the first eukaryotic genome. We can now obtain answers to detailed questions about the complete set of genes of an organism. Bioinformatics methods are increasingly used for attaching biological knowledge to long lists of genes, assigning genes to biological pathways, comparing the gene sets of different species, identifying specificity factors, and describing sets of highly conserved proteins common to all domains of life. Substantial progress has recently been made in the availability of primary and added-value databases, in the development of algorithms and of network information services for genome analysis. The pharmaceutical industry has greatly benefited from the accumulation of sequence data through the identification of targets and candidates for the development of drugs, vaccines, diagnostic markers and therapeutic proteins.     

From genotype to phenotype: linking bioinformatics and medical informatics ontologies

A small group of around 40 people came together at the Chancellors Conference Centre in Manchester for the Ontologies Workshop, chaired by Alan Rector and Robert Stevens. The workshop was, rather strangely, spread over 2 half days. In hindsight, this programme worked very well as it gave people the opportunity to chat over a drink on the Saturday evening and share ideas, before launching into the second half on the following day. The participants were from various walks of life, all with a common interest in finding out more about ontologies and promoting collaborations between the medical informatics and bioinformatics ontology communities.     

MarC-V: a spreadsheet-based tool for analysis, normalization, and visualization of single cDNA microarray experiments

The comprehensive analysis and visualization of data extracted from cDNA microarrays can be a time-consuming and error-prone process that becomes increasingly tedious with increased number of gene elements on a particular microarray. With the increasingly large number of gene elements on today's microarrays, analysis tools must be developed to meet this challenge. Here, we present MarC-V, a Microsoft Excel spreadsheet tool with Visual Basic macros to automate much of the visualization and calculation involved in the analysis process while providing the familiarity and flexibility of Excel. Automated features of this tool include (i) lower-bound thresholding, (ii) data normalization, (iii) generation of ratio frequency distribution plots, (iv) generation of scatter plots color-coded by expression level, (v) ratio scoring based on intensity measurements, (vi) filtering of data based on expression level or specific gene interests, and (vii) exporting data for subsequent multi-array analysis. MarC-V also has an importing function included for GenePix results (GPR) raw data files.