Life scientists are increasingly turning to high-throughput sequencing technologies in their research programs, owing to the enormous potential of these methods. In a parallel manner, the number of core facilities that provide bioinformatics support are also increasing. Notably, the generation of complex large datasets has necessitated the development of bioinformatics support core facilities that aid laboratory scientists with cost-effective and efficient data management, analysis, and interpretation. In this article, we address the challenges-related to communication, good laboratory practice, and data handling-that may be encountered in core support facilities when providing bioinformatics support, drawing on our own experiences working as support bioinformaticians on multidisciplinary research projects. Most importantly, the article proposes a list of guidelines that outline how these challenges can be preemptively avoided and effectively managed to increase the value of outputs to the end user, covering the entire research project lifecycle, including experimental design, data analysis, and management (i.e., sharing and storage). In addition, we highlight the importance of clear and transparent communication, comprehensive preparation, appropriate handling of samples and data using monitoring systems, and the employment of appropriate tools and standard operating procedures to provide effective bioinformatics support.

BACKGROUND: Currently, formal mechanisms for bioinformatics support are limited. The H3Africa Bioinformatics Network has implemented a public and freely available Helpdesk (HD), which provides generic bioinformatics support to researchers through an online ticketing platform. The following article reports on the H3ABioNet HD (H3A-HD)'s development, outlining its design, management, usage and evaluation framework, as well as the lessons learned through implementation. RESULTS: The H3A-HD evaluated using automatically generated usage logs, user feedback and qualitative ticket evaluation. Evaluation revealed that communication methods, ticketing strategies and the technical platforms used are some of the primary factors which may influence the effectivity of HD. CONCLUSION: To continuously improve the H3A-HD services, the resource should be regularly monitored and evaluated. The H3A-HD design, implementation and evaluation framework could be easily adapted for use by interested stakeholders within the Bioinformatics community and beyond.

The authors and Venaille (1996) developed a prototype for visual robot positioning, based on global image descriptors and neural networks. Now, a procedure to automatically select subsets of image features most relevant to determine pose variations along each of the six degrees of freedom (DOFs) has been incorporated into the prototype. This procedure is based on a statistical measure of variable interdependence, called mutual information. Three families of features are considered in this paper: geometric moments, eigenfeatures and pose-image covariance vectors. The experimental results described show the quantitative and qualitative benefits of carrying out this feature selection prior to training the neural network: fewer network inputs need to be considered, thus considerably shortening training times; the DOFs that would yield larger errors can be determined beforehand, so that more informative features can be looked for; the ordering of the features selected for each DOF often admits a very natural interpretation, which in turn helps to provide insights for devising features tailored to each DOF.