Educating the educators: Incorporating bioinformatics into biological science education in Malaysia

Bioinformatics can be defined as a fusion of computational and biological sciences. The urgency to process and analyse the deluge of data created by proteomics and genomics studies has caused bioinformatics to gain prominence and importance. However, its multidisciplinary nature has created a unique demand for specialist trained in both biology and computing. In this review, we described the components that constitute the bioinformatics field and distinctive education criteria that are required to produce individuals with bioinformatics training. This paper will also provide an introduction and overview of bioinformatics in Malaysia. The existing bioinformatics scenario in Malaysia was surveyed to gauge its advancement and to plan for future bioinformatics education strategies. For comparison, we surveyed methods and strategies used in education by other countries so that lessons can be learnt to further improve the implementation of bioinformatics in Malaysia. It is believed that accurate and sufficient steerage from the academia and industry will enable Malaysia to produce quality bioinformaticians in the future

MACiE: a database of enzyme reaction mechanisms.

SUMMARY: MACiE (mechanism, annotation and classification in enzymes) is a publicly available web-based database, held in CMLReact (an XML application), that aims to help our understanding of the evolution of enzyme catalytic mechanisms and also to create a classification system which reflects the actual chemical mechanism (catalytic steps) of an enzyme reaction, not only the overall reaction. AVAILABILITY: http://www-mitchell.ch.cam.ac.uk/macie/.EPSRC (G.L.H. and J.B.O.M.), the BBSRC (G.J.B. and J.M.T.—CASE studentship in association with Roche Products Ltd; N.M.O.B. and J.B.O.M.—grant BB/C51320X/1), the Chilean Government’s Ministerio de Planificacio´n y Cooperacio´n and Cambridge Overseas Trust (D.E.A.) for funding and Unilever for supporting the Centre for Molecular Science Informatics.application note restricted to 2 printed pages web site: http://www-mitchell.ch.cam.ac.uk/macie

Bioinformatics Education—Perspectives and Challenges

This article discusses the evolution of curriculum, instructional methodologies and initiatives supporting the dissemination of bioinformatics. Building on the early applications of informatics to the field of biology, bioinformatics research entails input from the diverse disciplines of mathematics and statistics, physics and chemistry and medicine and pharmacology. Training in bioinformatics remains the oldest and most important rapid introduction approach to learning bioinformatics skills.2 page(s

Bioinformatics Databases: State of the Art and Research Perspectives

Bioinformatics or computational biology, i.e. the application of mathematical and computer science methods to solving problems in molecular biology that require large scale data, computation, and analysis, is a research area currently receiving a considerable attention. Databases play an essential role in molecular biology and consequently in bioinformatics. molecular biology data are often relatively cheap to produce, leading to a proliferation of databases: the number of bioinformatics databases accessible worldwide probably lies between 500 and 1.000. Not only molecular biology data, but also molecular biology literature and literature references are stored in databases. Bioinformatics databases are often very large (e.g. the sequence database GenBank contains more than 4 × 10 6 nucleotide sequences) and in general grows rapidly (e.g. about 8000 abstracts are added every month to the literature database PubMed). Bioinformatics databases are heterogeneous in their data, in their data modeling paradigms, in their management systems, and in the data analysis tools they supports. Furthermore, bioinformatics databases are often implemented, queried, updated, and managed using methods rarely applied for other databases. This presentation aims at introducing in current bioinformatics databases, stressing their aspects departing from conventional databases. A more detailed survey can be found in [1] upon which thi

Cloud Bioinformatics in a private cloud deployment

This chapter describes service portability for a private cloud deployment, including a detailed case study about Cloud Bioinformatics services developed as part of the Cloud Computing Adoption Framework (CCAF). The Cloud Bioinformatics design and deployment is based on Storage Area Network (SAN) technologies, details of which include functionalities, technical implementation, architecture, and user support. Bioinformatics applications are written on the SAN-based private cloud, which can simulate complex biological sciences and present them in a way that anyone without prior knowledge can understand. Several bioinformatics results are discussed, particularly brain segmentation, which demonstrates different parts of the brain simulated by the private cloud. In addition, benefits of CCAF are illustrated using several bioinformatics examples such as tumour modelling, brain imaging, insulin molecules, and simulations for medical training. The Cloud Bioinformatics solution offers cost reduction, time-saving, and user friendliness. </jats:p

Quantitative method for the assignment of hinge and shear mechanism in protein domain movements

Motivation: A popular method for classification of protein domain movements apportions them into two main types: those with a ‘hinge’ mechanism and those with a ‘shear’ mechanism. The intuitive assignment of domain movements to these classes has limited the number of domain movements that can be classified in this way. Furthermore, whether intended or not, the term ‘shear’ is often interpreted to mean a relative translation of the domains. Results: Numbers of occurrences of four different types of residue contact changes between domains were optimally combined by logistic regression using the training set of domain movements intuitively classified as hinge and shear to produce a predictor for hinge and shear. This predictor was applied to give a 10-fold increase in the number of examples over the number previously available with a high degree of precision. It is shown that overall a relative translation of domains is rare, and that there is no difference between hinge and shear mechanisms in this respect. However, the shear set contains significantly more examples of domains having a relative twisting movement than the hinge set. The angle of rotation is also shown to be a good discriminator between the two mechanisms. Availability and implementation: Results are free to browse at http:// www.cmp.uea.ac.uk/dyndom/interface/. Supplementary information: Supplementary data are available at Bioinformatics online

Agents in Bioinformatics

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarise and reflect on the presentations and discussions

Chemistry in Bioinformatics

A preprint of an invited submission to BioMedCentral Bioinformatics. This short manuscript is an overview or the current problems and opportunities in publishing chemical information. Full details of technology are given in the sibling manuscript http://www.dspace.cam.ac.uk/handle/1810/34579 The manuscript is the authors' preprint although it has been automatically transformed into this archived PDF by the submission system. The authors are not responsible for the formattingChemical information is now seen as critical for most areas of life sciences. But unlike Bioinformatics, where data is Openly available and freely re−usable, most chemical information is closed and cannot be re−distributed without permission. This has led to a failure to adopt modern informatics and software techniques and therefore paucity of chemistry in bioinformatics. New technology, however, offers the hope of making chemical data (compounds and properties) Free during the authoring process. We argue that the technology is already available; we require a collective agreement to enhance publication protocols