MOTIVATION: Galaxy is a software application supporting high-throughput biology analyses and work flows, available as a free on-line service or as source code for local deployment. New tools can be written to extend Galaxy, and these can be shared using public Galaxy Tool Shed (GTS) repositories, but converting even simple scripts into tools requires effort from a skilled developer. RESULTS: The Tool Factory is a novel Galaxy tool that automates the generation of all code needed to execute user-supplied scripts, and wraps them into new Galaxy tools for upload to a GTS, ready for review and installation through the Galaxy administrative interface. AVAILABILITY AND IMPLEMENTATION: The Galaxy administrative interface supports automated installation from the main GTS. Source code and support are available at the project website, https://bitbucket.org/fubar/galaxytoolfactory. The Tool Factory is implemented as an installable Galaxy tool. CONTACT: [email protected].

BACKGROUND: With the recent development of microarray technologies, the comparability of gene expression data obtained from different platforms poses an important problem. We evaluated two widely used platforms, Affymetrix U133 Plus 2.0 and the Illumina HumanRef-8 v2 Expression Bead Chips, for comparability in a biological system in which changes may be subtle, namely fetal lung tissue as a function of gestational age. RESULTS: We performed the comparison via sequence-based probe matching between the two platforms. "Significance grouping" was defined as a measure of comparability. Using both expression correlation and significance grouping as measures of comparability, we demonstrated that despite overall cross-platform differences at the single gene level, increased correlation between the two platforms was found in genes with higher expression level, higher probe overlap, and lower p-value. We also demonstrated that biological function as determined via KEGG pathways or GO categories is more consistent across platforms than single gene analysis. CONCLUSION: We conclude that while the comparability of the platforms at the single gene level may be increased by increasing sample size, they are highly comparable ontologically even for subtle differences in a relatively small sample size. Biologically relevant inference should therefore be reproducible across laboratories using different platforms.

Health care providers are legally obliged to report cases of specified diseases to public health authorities, but existing manual, provider-initiated reporting systems generally result in incomplete, error-prone, and tardy information flow. Automated laboratory-based reports are more likely accurate and timely, but lack clinical information and treatment details. Here, we describe the Electronic Support for Public Health (ESP) application, a robust, automated, secure, portable public health detection and messaging system for cases of notifiable diseases. The ESP application applies disease specific logic to any complete source of electronic medical data in a fully automated process, and supports an optional case management workflow system for case notification control. All relevant clinical, laboratory and demographic details are securely transferred to the local health authority as an HL7 message. The ESP application has operated continuously in production mode since January 2007, applying rigorously validated case identification logic to ambulatory EMR data from more than 600,000 patients. Source code for this highly interoperable application is freely available under an approved open-source license at http://esphealth.org.

UNLABELLED: This paper reviews the central concepts and implementation of data structures and methods for studying genetics of gene expression with the GGtools package of Bioconductor. Illustration with a HapMap+expression dataset is provided. AVAILABILITY: Package GGtools is part of Bioconductor 1.9 (http://bioconductor.org). Open source with Artistic License.