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

Biologists meet statisticians: A workshop for young scientists to foster interdisciplinary team work

Life science and statistics have necessarily become essential partners. The need to plan complex, structured experiments, involving elaborated designs, and the need to analyse datasets in the era of systems biology and high throughput technologies has to build upon professional statistical expertise. On the other hand, conducting such analyses and also developing improved or new methods, also for novel kinds of data, has to build upon solid biological understanding and practise. However, the meeting of scientists of both fields is often hampered by a variety of communicative hurdles - which are based on field-specific working languages and cultural differences. As a step towards a better mutual understanding, we developed a workshop concept bringing together young experimental biologists and statisticians, to work as pairs and learn to value each others competences and practise interdisciplinary communication in a casual atmosphere. The first implementation of our concept was a cooperation of the German Region of the International Biometrical Society and the Leibnitz Institute DSMZ-German Collection of Microorganisms and Cell Cultures (short: DSMZ), Braunschweig, Germany. We collected feedback in form of three questionnaires, oral comments, and gathered experiences for the improvement of this concept. The long-term challenge for both disciplines is the establishment of systematic schedules and strategic partnerships which use the proposed workshop concept to foster mutual understanding, to seed the necessary interdisciplinary cooperation network, and to start training the indispensable communication skills at the earliest possible phase of education

Computational Strategies for Scalable Genomics Analysis.

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications

New Scientific and Technological Developments of Relevance to the Fifth Review Conference

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Establishment of computational biology in Greece and Cyprus: Past, present, and future.

We review the establishment of computational biology in Greece and Cyprus from its inception to date and issue recommendations for future development. We compare output to other countries of similar geography, economy, and size—based on publication counts recorded in the literature—and predict future growth based on those counts as well as national priority areas. Our analysis may be pertinent to wider national or regional communities with challenges and opportunities emerging from the rapid expansion of the field and related industries. Our recommendations suggest a 2-fold growth margin for the 2 countries, as a realistic expectation for further expansion of the field and the development of a credible roadmap of national priorities, both in terms of research and infrastructure funding

SymGRASS: a database of sugarcane orthologous genes involved in arbuscular mycorrhiza and root nodule symbiosis : from Seventh International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, (CIBB 2010), Palermo, Italy, 16 - 18 September 2010

Background: The rationale for gathering information from plants procuring nitrogen through symbiotic interactions controlled by a common genetic program for a sustainable biofuel production is the high energy demanding application of synthetic nitrogen fertilizers. We curated sequence information publicly available for the biofuel plant sugarcane, performed an analysis of the common SYM pathway known to control symbiosis in other plants, and provide results, sequences and literature links as an online database. Methods: Sugarcane sequences and informations were downloaded from the nucEST database, cleaned and trimmed with seqclean, assembled with TGICL plus translating mapping method, and annotated. The annotation is based on BLAST searches against a local formatted plant Uniprot90 generated with CD-HIT for functional assignment, rpsBLAST to CDD database for conserved domain analysis, and BLAST search to sorghum's for Gene Ontology (GO) assignment. Gene expression was normalized according the Unigene standard, presented as ESTs/100 kb. Protein sequences known in the SYM pathway were used as queries to search the SymGRASS sequence database. Additionally, antimicrobial peptides described in the PhytAMP database served as queries to retrieve and generate expression profiles of these defense genes in the libraries compared to the libraries obtained under symbiotic interactions. Results: We describe the SymGRASS, a database of sugarcane orthologous genes involved in arbuscular mycorrhiza (AM) and root nodule (RN) symbiosis. The database aggregates knowledge about sequences, tissues, organ, developmental stages and experimental conditions, and provides annotation and level of gene expression for sugarcane transcripts and SYM orthologous genes in sugarcane through a web interface. Several candidate genes were found for all nodes in the pathway, and interestingly a set of symbiosis specific genes was found. Conclusions: The knowledge integrated in SymGRASS may guide studies on molecular, cellular and physiological mechanisms by which sugarcane controls the establishment and efficiency of endophytic associations. We believe that the candidate sequences for the SYM pathway together with the pool of exclusively expressed tentative consensus (TC) sequences are crucial for the design of molecular studies to unravel the mechanisms controlling the establishment of symbioses in sugarcane, ultimately serving as a basis for the improvement of grass crops

Stochastic dynamic modeling of short gene expression time-series data

Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, the expectation maximization (EM) algorithm is applied for modeling the gene regulatory network from gene time-series data. The gene regulatory network is viewed as a stochastic dynamic model, which consists of the noisy gene measurement from microarray and the gene regulation first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, both the model parameters and the actual values of the gene expression levels can be identified simultaneously. Moreover, the algorithm can deal with the sparse parameter identification and the noisy data in an efficient way. It is also shown that the EM algorithm can handle the microarray gene expression data with large number of variables but a small number of observations. The gene expression stochastic dynamic models for four real-world gene expression data sets are constructed to demonstrate the advantages of the introduced algorithm. Several indices are proposed to evaluate the models of inferred gene regulatory networks, and the relevant biological properties are discussed