BABAR: an R package to simplify the normalisation of common reference design microarray-based transcriptomic datasets

Background: The development of DNA microarrays has facilitated the generation of hundreds of thousands of transcriptomic datasets. The use of a common reference microarray design allows existing transcriptomic data to be readily compared and re-analysed in the light of new data, and the combination of this design with large datasets is ideal for 'systems' level analyses. One issue is that these datasets are typically collected over many years and may be heterogeneous in nature, containing different microarray file formats and gene array layouts, dye-swaps, and showing varying scales of log(2)- ratios of expression between microarrays. Excellent software exists for the normalisation and analysis of microarray data but many data have yet to be analysed as existing methods struggle with heterogeneous datasets; options include normalising microarrays on an individual or experimental group basis. Our solution was to develop the Batch Anti-Banana Algorithm in R (BABAR) algorithm and software package which uses cyclic loess to normalise across the complete dataset. We have already used BABAR to analyse the function of Salmonella genes involved in the process of infection of mammalian cells. Results: The only input required by BABAR is unprocessed GenePix or BlueFuse microarray data files. BABAR provides a combination of 'within' and 'between' microarray normalisation steps and diagnostic boxplots. When applied to a real heterogeneous dataset, BABAR normalised the dataset to produce a comparable scaling between the microarrays, with the microarray data in excellent agreement with RT-PCR analysis. When applied to a real non-heterogeneous dataset and a simulated dataset, BABAR's performance in identifying differentially expressed genes showed some benefits over standard techniques. Conclusions: BABAR is an easy-to-use software tool, simplifying the simultaneous normalisation of heterogeneous two-colour common reference design cDNA microarray-based transcriptomic datasets. We show BABAR transforms real and simulated datasets to allow for the correct interpretation of these data, and is the ideal tool to facilitate the identification of differentially expressed genes or network inference analysis from transcriptomic datasets

Logistic support provided to Australian disaster medical assistance teams: results of a national survey of team members

Background: It is likely that calls for disaster medical assistance teams (DMATs) continue in response to international disasters. As part of a national survey, the present study was designed to evaluate the Australian DMAT experience and the need for logistic support.\ud \ud Methods: Data were collected via an anonymous mailed survey distributed via State and Territory representatives on the Australian Health Protection Committee, who identified team members associated with Australian DMAT deployments from the 2004 Asian Tsunami disaster.\ud \ud Results: The response rate for this survey was 50% (59/118). Most of the personnel had deployed to the South East Asian Tsunami affected areas. The DMAT members had significant clinical and international experience. There was unanimous support for dedicated logistic support with 80% (47/59) strongly agreeing. Only one respondent (2%) disagreed with teams being self sufficient for a minimum of 72 hours. Most felt that transport around the site was not a problem (59%; 35/59), however, 34% (20/59) felt that transport to the site itself was problematic. Only 37% (22/59) felt that pre-deployment information was accurate. Communication with local health providers and other agencies was felt to be adequate by 53% (31/59) and 47% (28/59) respectively, while only 28% (17/59) felt that documentation methods were easy to use and reliable. Less than half (47%; 28/59) felt that equipment could be moved easily between areas by team members and 37% (22/59) that packaging enabled materials to be found easily. The maximum safe container weight was felt to be between 20 and 40 kg by 58% (34/59).\ud \ud Conclusions: This study emphasises the importance of dedicated logistic support for DMAT and the need for teams to be self sufficient for a minimum period of 72 hours. There is a need for accurate pre deployment information to guide resource prioritisation with clearly labelled pre packaging to assist access on site. Container weights should be restricted to between 20 and 40 kg, which would assist transport around the site, while transport to the site was seen as problematic. There was also support for training of all team members in use of basic equipment such as communications equipment, tents and shelters and water purification systems