A Bioinformatics Reference Model: Towards a Framework for Developing and Organising Bioinformatic Resources

Life Science research faces the constant challenge of how to effectively handle an ever-growing body of bioinformatics software and online resources. The users and developers of bioinformatics resources have a diverse set of competing demands on how these resources need to be developed and organised. Unfortunately, there does not exist an adequate community-wide framework to integrate such competing demands. The problems that arise from this include unstructured standards development, the emergence of tools that do not meet specific needs of researchers, and often times a communications gap between those who use the tools and those who supply them. This paper presents an overview of the different functions and needs of bioinformatics stakeholders to determine what may be required in a community-wide framework. A Bioinformatics Reference Model is proposed as a basis for such a framework. The reference model outlines the functional relationship between research usage and technical aspects of bioinformatics resources. It separates important functions into multiple structured layers, clarifies how they relate to each other, and highlights the gaps that need to be addressed for progress towards a diverse, manageable, and sustainable body of resources. The relevance of this reference model to the bioscience research community, and its implications in progress for organising our bioinformatics resources, are discussed

Visualising the evolutionary dynamics of molecular data using self organising feature maps

No abstract availabl

An information system for whole genome data

No abstract availabl

Magnetic Properties of Molecular and Nanoscale Magnets

The idea of miniaturizing devices down to the nanoscale where quantum ffeffects become relevant demands a detailed understanding of the interplay between classical and quantum properties. Therefore, characterization of newly produced nanoscale materials is a very important part of the research in this fifield. Studying structural and magnetic properties of nano- and molecular magnets and the interplay between these properties reveals new interesting effects and suggests ways to control and optimize the respective material. The main task of this thesis is investigating the magnetic properties of molecular magnetic clusters and magnetic nanoparticles recently synthesized by several collaborating groups. This thesis contains two main parts focusing on each of these two topics. In the first part the fundamental studies on novel metal-organic molecular complexes is presented. Several newly synthesized magnetic complexes were investigated by means of different experimental techniques, in particular, by electron spin resonance spectroscopy. Chapter 1 in this part provides the theoretical background which is necessary for the interpretation of the effects observed in single molecular magnetic clusters. Chapter 2 introduces the experimental techniques applied in the studies. Chapter 3 contains the experimental results and their discussion. Firstly, the magnetic properties of two Ni-based complexes are presented. The complexes possess different ligand structures and arrangements of the Ni-ions in the metal cores. This difffference dramatically affffects the magnetic properties of the molecules such as the ground state and the magnetic anisotropy. Secondly, a detailed study of the Mn2Ni3 single molecular magnet is described. The complex has a bistable magnetic ground state with a high spin value of S = 7 and shows slow relaxation and quantum tunnelling of the magnetization. The third section concentrates on a Mn(III)-based single chain magnet showing ferromagnetic ordering of the Mn-spins and a strong magnetic anisotropy which leads to a hysteretic behavior of the magnetization. The last section describes a detailed study of the static and dynamic magnetic properties of three Mn-dimer molecular complexes by means of static magnetization, continuous wave and pulse electron spin resonance measurements. The results indicate a systematic dependence of the magnetic properties on the nearest ligands surrounding of the Mn ions. The second part of the thesis addresses magnetic properties of nano-scaled magnets such as carbon nanotubes fifilled with magnetic materials and carbon-coated magnetic nanoparticles. These studies are eventually aiming at the possible application of these particles as agents for magnetic hyperthermia. In this respect, their behavior in static and alternating magnetic fifields is investigated and discussed. Moreover, two possible hyperthermia applications of the studied magnetic nanoparticles are presented, which are the combination of a hyperthermia agents with an anticancer drug and the possibility to spatially localize the hyperthermia effffect by applying specially designed static magnetic fifields

GRID-enabled bioinformatics applications for comparative genomic analysis at the CBBC

Bioinformatics is an important application area for Grid computing. The Grid computing issues required to tackle current bioinformatics challenges include processing power, large-scale data access and management, security, application integration, data integrity and curation, control/automation/ tracking of workflows, data format consistency and resource discovery. In this poster, we describe preliminary steps taken to develop a Grid environment to advance Bioinformatics research. We developed a system called Grendel, with the aims of providing Bioinformatics researchers transparent access to basic computational resources used in their research. Grendel is a platform and language independent web-services based system for distributed resource management utilising Sun Grid Engine that provides a single entry point for computational tasks while keeping the actual resources transparent to the user. Grendel is developed in Java and deployed using the Tomcat. Client libraries have been developed in Perl and Java to provide access to computation resource exported via Grendel

ORBIT: An integrated environment for user-customized bioinformatics tools

MOTIVATION: There are a large number of computational programs freely available to bioinformaticians via a client/server, web-based environment. However, the client interface to these tools (typically an html form page) cannot be customized from the client side as it is created by the service provider. The form page is usually generic enough to cater for a wide range of users. However, this implies that a user cannot set as 'default' advanced program parameters on the form or even customize the interface to his/her specific requirements or preferences. Currently, there is a lack of end-user interface environments that can be modified by the user when accessing computer programs available on a remote server running on an intranet or over the Internet. RESULTS: We have implemented a client/server system called ORBIT (Online Researcher's Bioinformatics Interface Tools) where individual clients can have interfaces created and customized to command-line-driven, server-side programs. Thus, Internet-based interfaces can be tailored to a user's specific bioinformatic needs. As interfaces are created on the client machine independent of the server, there can be different interfaces to the same server-side program to cater for different parameter settings. The interface customization is relatively quick (between 10 and 60 min) and all client interfaces are integrated into a single modular environment which will run on any computer platform supporting Java. The system has been developed to allow for a number of future enhancements and features. ORBIT represents an important advance in the way researchers gain access to bioinformatics tools on the Internet

Grendel: A bioinformatics web service-based architecture for accessing HPC resources

Bioinformatics is an important application area for grid computing. Bioinformatics applications exported over a grid have the potential to facilitate transparent access to high performance computing (HPC) resources to a range of stakeholders. These stakeholders include application users, application developers, system administrators as well as service providers. In this paper we describe a Web Service based system called Grendel. Grendel acts as a single access point to HPC and is used to explore gridenablement issues for bioinformatics tools. It uses a language and platform independent mechanism for remotely interacting with the HPC environment. In this paper, we provide details of Grendel's architecture as well as lessons learnt in developing and deploying the system

MHC haplotype analysis by artificial neural networks

Conventional matching is based on numbers of alleles shared between donor and recipient. This approach, however, ignores the degree of relationship between alleles and haplotypes, and therefore the actual degree of difference. To address this problem, we have compared family members using a block matching technique which reflects differences in genomic sequences. All parents and siblings had been genotyped using conventional MHC typing so that haplotypes could be assigned and relatives could be classified as sharing 0, 1 or 2 haplotypes. We trained an Artificial Neural Network (ANN) with subjects from 6 families (85 comparisons) to distinguish between relatives. Using the outputs of the ANN, we developed a score, the Histocompatibility Index (HI), as a measure of the degree of difference. Subjects from a further 3 families (106 profile comparisons) were tested. The HI score for each comparison was plotted. We show that the HI score is trimodal allowing the definition of three populations corresponding to approximately 0, 1 or 2 haplotype sharing. The means and standard deviations of the three populations were found. As expected, comparisons between family members sharing 2 haplotypes resulted in high HI scores with one exception. More interestingly, this approach distinguishes between the 1 and 0 haplotype groups, with some informative exceptions. This distinction was considered too difficult to attempt visually. The approach provides promise in the quantification of degrees of histo-compatibility

MHC Haplotype Analysis by Artificial Neural Networks

Conventional matching is based on numbers of alleles shared between donor and recipient. This approach, however, ignores the degree of relationship between alleles and haplotypes, and therefore the actual degree of difference. To address this problem, we have compared family members using a block matching technique which reflects differences in genomic sequences. All parents and siblings had been genotyped using conventional MHC typing so that haplotypes could be assigned and relatives could be classified as sharing 0, 1 or 2 haplotypes. We trained an Artificial Neural Network (ANN) with subjects from 6 families (85 comparisons) to distinguish between relatives. Using the outputs of the ANN, we developed a score, the Histocompatibility Index (HI), as a measure of the degree of difference. Subjects from a further 3 families (106 profile comparisons) were tested. The HI score for each comparison was plotted. We show that the HI score is trimodal allowing the definition of three populations corresponding to approximately 0, 1 or 2 haplotype sharing. The means and standard deviations of the three populations were found. As expected, comparisons between family members sharing 2 haplotypes resulted in high HI scores with one exception. More interestingly, this approach distinguishes between the 1 and 0 haplotype groups, with some informative exceptions. This distinction was considered too difficult to attempt visually. The approach provides promise in the quantification of degrees of histo-compatibility