AGMIAL: implementing an annotation strategy for prokaryote genomes as a distributed system

We have implemented a genome annotation system for prokaryotes called AGMIAL. Our approach embodies a number of key principles. First, expert manual annotators are seen as a critical component of the overall system; user interfaces were cyclically refined to satisfy their needs. Second, the overall process should be orchestrated in terms of a global annotation strategy; this facilitates coordination between a team of annotators and automatic data analysis. Third, the annotation strategy should allow progressive and incremental annotation from a time when only a few draft contigs are available, to when a final finished assembly is produced. The overall architecture employed is modular and extensible, being based on the W3 standard Web services framework. Specialized modules interact with two independent core modules that are used to annotate, respectively, genomic and protein sequences. AGMIAL is currently being used by several INRA laboratories to analyze genomes of bacteria relevant to the food-processing industry, and is distributed under an open source license

Social techniques for effective interactions in open cooperative systems

Distributed systems are becoming increasingly popular, both in academic and commercial communities, because of the functionality they offer for sharing resources among participants of these communities. As individual systems with different purposes and functionalities are developed, and as data of many different kinds are generated, the value to be gained from sharing services with others rather than just personal use, increases dramatically. This, however, is only achievable if participants of open systems cooperate with each other, to ensure the longevity of the system and the richness of available services, and to make decisions about the services they use to ensure that they are of sufficient levels of quality. Moreover, the properties of distributed systems such as openness, dynamism, heterogeneity and resource-bounded providers bring a number of challenges to designing computational entities that cooperate effectively and efficiently. In particular, computational entities must deal with the diversity of available services, the possible resource limitations for service provision, and with finding providers willing to cooperate even in the absence of economic gains. This requires a means not only to provide non-monetary incentives for service providers, but also to account for the level of quality of cooperations, in terms of the quality of provided and received services. In support of this, entities must be capable of selecting among alternative interaction partners, since each will offer distinct properties, which may change due to the dynamism of the environment. With this in mind, our goal is to develop mechanisms to allow effective cooperation between agents operating in systems that are open, dynamic, heterogeneous, and cooperative. Such mechanisms are needed in the context of cooperative applications with services that are free of charge, such as those in bioinformatics. To achieve this, we propose a framework for non-monetary cooperative interactions, which provides non-monetary incentives for service provision and a means to analyse cooperations; an evaluation method, for evaluating dynamic services; a provider selection mechanism, for decision-making over service requests; and a requester selection mechanism, for decision-making over service provision.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A grid and cloud-based framework for high throughput bioinformatics

Recent advances in genome sequencing technologies have unleashed a flood of new data. As a result, the computational analysis of bioinformatics data sets has been rapidly moving from a labbased desktop computer environment to exhaustive analyses performed by large dedicated computing resources. Traditionally, large computational problems have been performed on dedicated clusters of high performance machines that are typically local to, and owned by, a particular institution. The current trend in Grid computing has seen institutions pooling their computational resources in order to offload excess computational work to remote locations during busy periods. In the last year or so, commercial Cloud computing initiatives have matured enough to offer a viable remote source of reliable computational power. Collections of idle desktop computers have also been used as a source of computational power in the form of ‘volunteer Grids’. The field of bioinformatics is highly dynamic, with new or updated versions of software tools and databases continually being developed. Several different tools and datasets must often be combined into a coherent, automated workflow or pipeline. While existing solutions are available for constructing workflows, there is a clear need for long-lived analyses consisting of many interconnected steps to be able to migrate among Grid and cloud computational resources dynamically. This project involved research into the principles underlying the design and architecture of flexible, high-throughput bioinformatics processes. Following extensive research into requirements gathering, a novel Grid-based platform, Microbase, has been implemented that is based on service-oriented architectures and peer-to-peer data transfer technology. This platform has been shown to be amenable to utilising a wide range of hardware from commodity desktop computers, to high-performance cloud infrastructure. The system has been shown to drastically reduce the bandwidth requirements of bioinformatics data distribution, and therefore reduces both the financial and computational costs associated with cloud computing. The system is inherently modular in nature, comprising a service based notification system, a data storage system scheduler and a job manager. In keeping with e-Science principles, each module can operate in physical isolation from each other, distributed within an intranet or Internet. Moreover, since each module is loosely coupled via Web services, modules have the potential to be used in combination with external service oriented components or in isolation as part of another system. In order to demonstrate the utility of such an open source system to the bioinformatics community, a pipeline of inter-connected bioinformatics applications was developed using the Microbase system to form a high throughput application for the comparative and visual analysis of microbial genomes. This application, Automated Genome Analyser (AGA) has been developed to operate without user interaction. AGA exposes its results via Web-services which can be used by further analytical stages within Microbase, by external computational resources via a Web service interface or which can be queried by users via an interactive genome browser. In addition to providing the necessary infrastructure for scalable Grid applications, a modular development framework has been provided, which simplifies the process of writing Grid applications. Microbase has been adopted by a number of projects ranging from comparative genomics to synthetic biology simulations.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Comparative genomics for studying the proteomes of mucosal microorganisms

A tremendous number of microorganisms are known to interact with their animal hosts. The outcome of the interactions between microbes and their animal hosts range from modulating the maintenance of homeostasis to the establishment of processes leading to pathogenesis. Of the numerous species known to inhabit humans, the great majority live on mucosal surfaces which are highly defended. Despite their importance in human health, little is known about the molecular and cellular basis of most host-microbe interactions across the tremendous diversity of mucosal-adapted microorganisms. The ever-increasing availability of genome sequence data allows systematic comparative genomics studies to identify proteins with potential important molecular functions at the host-microbe interface. In this study, a genome-wide analysis was performed on 3,021,490 protein sequences derived from 867 complete microbial genome sequences across the three domains of cellular life. The ability of microbes to thrive successfully in a mucosal environment was examined in relation to functional genomics data from a range of publicly available databases. Particular emphasis was placed on the extracytoplasmic proteins of microorganisms that thrive on human mucosal surfaces. These proteins form the interface between the complex host-microbe and microbe-microbe interactions. The large amounts of data involved, combined with the numerous analytical techniques that need to be performed makes the study intractable with conventional bioinformatics. The lack of habitat annotations for microorganisms further compounds the problem of identifying the microbial extracytoplasmic proteins playing important roles in the mucosal environments. In order to address these problems, a distributed high throughput computational workflow was developed, and a system for mining biomedical literature was trained to automatically identify microorganisms’ habitats. The workflow integrated existing bioinformatics tools to identify and characterise protein-targeting signals, cell surface-anchoring features, protein domains and protein families. This study successfully demonstrated a large-scale comparative genomics approach utilising a system called Microbase to harness Grid and Cloud computing technologies. A number of conserved protein domains and families that are significantly associated with a speiii iv cific set of mucosa-inhabiting microorganisms were identified. These conserved protein regions of which their functions were either characterised or unknown, were quite narrow in their coverage of taxa distribution, with only a few protein domains more widely distributed, suggesting that mucosal microorganisms evolved different solutions in their strategies and mechanisms for their survival in the host mucosal environments. Metabolic and biological processes common to many mucosal microorganisms included: carbohydrate and amino acid metabolisms, signal transduction, adhesion to host tissues or contents in mucosal environments (e.g. food remnants, mucins), and resistance to host defence mechanisms. Invasive or virulence factors were also identified in pathogenic strains. Several extracytoplasmic protein families were shared among prominent bacterial members of gut microbiota and microbial eukaryotes known to thrive in the same environment, suggesting that the ability of microbes to adapt to particular niches can be influenced by lateral gene transfer. A large number of conserved regions or protein families that potentially play important roles in the mucosa-microbe interactions were revealed by this study. Several of these candidates were proteins of unknown function. The identified candidates were subjected to more detailed computational analysis providing hypothesis for their function that will be tested experimentally in order to contribute to our understanding of the complex host-microbe interactions. Among the candidates of unknown function, a novel M60-like domain was identified. The domain was deposited in the Pfam database with accession number PF13402. The M60-like domain is shared amongst a broad range of mucosal microorganisms as well as their vertebrate hosts. Bioinformatics analyses of the M60-like domain suggested a potential catalytic function of the conserved motif as gluzincins metalloproteases. Targeting signals were detected across microbial M60-likecontaining proteins. Mucosa-related carbohydrate-binding modules (CBMs), CBM32 was also identified on several proteins containing M60-like domains encoded by known mucosal commensals and pathogens. The co-occurrence of the CBMs and M60-like domain, as well as annotated potential peptidase function unveiled a new functional context for the CBM, which is typically connected with carbohydrate processing enzymes but not proteases. The CBM domains linked with members of different protease families are likely to enable these proteases to bind to specific glycoproteins from host animals further highlighting the importance of proteases and CBMs (CBM32 and CBM5_12) in host-microbe interactions.EThOS - Electronic Theses Online ServiceMedical School, Newcastle UniversityGBUnited Kingdo