Motivation: A variety of tools are available to predict the topology of transmembrane proteins. To date no independent evaluation of the performance of these tools has been published. A better understanding of the strengths and weaknesses of the different tools would guide both the biologist and the bioinformatician to make better predictions of membrane protein topology. Results: Here we present an evaluation of the performance of the currently best known and most widely used methods for the prediction of transmembrane regions in proteins. Our results show that TMHMM is currently the best performing transmembrane prediction program. Contact: moeller@ebi.ac.uk; croning@ebi.ac.uk; apweiler@ebi.ac.uk

Susceptibility to chronic inflammatory diseases is determined by immunogenetic and environmental risk factors. Resident microbial communities often differ between healthy and diseased states, but whether these differences are of primary aetiological importance or secondary to the altered inflammatory environment remains largely unknown. Here we provide evidence for host gene–microbiota interactions contributing to disease risk in a mouse model of epidermolysis bullosa acquisita, an autoantibody-induced inflammatory skin disease. Using an advanced intercross, we identify genetic loci contributing to skin microbiota variability, susceptibility to skin blistering and their overlap. Furthermore, by treating bacterial species abundances as covariates with disease we reveal a novel disease locus. The majority of the identified covariate taxa are characterized by reduced abundance being associated with increased disease risk, providing evidence of a primary role in protection from disease. Further characterization of these putative probiotic species or species assemblages offers promising potential for preventative and therapeutic treatment development.

The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.

Despite increasing research efforts, there is a lack of consensus on defining aging or health. To understand the underlying processes, and to foster the development of targeted interventions towards increasing one's health, there is an urgent need to find a broadly acceptable and useful definition of health, based on a list of (molecular) features; to operationalize features of health so that it can be measured; to identify predictive biomarkers and (molecular) pathways of health; and to suggest interventions, such as nutrition and exercise, targeted at putative causal pathways and processes. Based on a survey of the literature, we propose to define health as a state of an individual characterized by the core features of physiological, cognitive, physical and reproductive function, and a lack of disease. We further define aging as the aggregate of all processes in an individual that reduce its wellbeing, that is, its health or survival or both. We define biomarkers of health by their attribute of predicting future health better than chronological age. We define healthspan pathways as molecular features of health that relate to each other by belonging to the same molecular pathway. Our conceptual framework may integrate diverse operationalizations of health and guide precision prevention efforts.

Summary: A collection of transmembrane proteins with annotated transmembrane regions, for which good experimental evidence exist, was created as a test or training set for algorithms to predict transmembrane regions in proteins. Availability: ftp://ftp.ebi.ac.uk/databases/testsets/transmembrane Contact: moeller@ebi.ac.uk