TAMEE: data management and analysis for tissue microarrays

Gerhard G Thallinger¹ , Kerstin Baumgartner¹ , Martin Pirklbauer¹ , Martina Uray³ , Elke Pauritsch³ , Gabor Mehes⁴ , Charles R Buck⁴^,5 , Kurt Zatloukal⁶ and Zlatko Trajanoski¹^,2

¹Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria

²Christian Doppler Laboratory for Genomics and Bioinformatics, Petersgasse 14, 8010 Graz, Austria

³Department of Mathematics C, Graz University of Technology, Steyrergasse 30, 8010 Graz, Austria

⁴ORIDIS Biomed GmbH, Stiftingtalstrasse 3-5, 8010 Graz, Austria

⁵Bindley Bioscience Center, Purdue University, 475 Stadium Mall Drive, West Lafayette, IN 47907, USA

⁶Institute of Pathology, Medical University of Graz, Auenbruggerplatz 25, 8036 Graz, Austria

author email corresponding author email

BMC Bioinformatics 2007, 8:81doi:10.1186/1471-2105-8-81


Published:	7 March 2007

Abstract

Background

With the introduction of tissue microarrays (TMAs) researchers can investigate gene and protein expression in tissues on a high-throughput scale. TMAs generate a wealth of data calling for extended, high level data management. Enhanced data analysis and systematic data management are required for traceability and reproducibility of experiments and provision of results in a timely and reliable fashion. Robust and scalable applications have to be utilized, which allow secure data access, manipulation and evaluation for researchers from different laboratories.

Results

TAMEE (Tissue Array Management and Evaluation Environment) is a web-based database application for the management and analysis of data resulting from the production and application of TMAs. It facilitates storage of production and experimental parameters, of images generated throughout the TMA workflow, and of results from core evaluation. Database content consistency is achieved using structured classifications of parameters. This allows the extraction of high quality results for subsequent biologically-relevant data analyses. Tissue cores in the images of stained tissue sections are automatically located and extracted and can be evaluated using a set of predefined analysis algorithms. Additional evaluation algorithms can be easily integrated into the application via a plug-in interface. Downstream analysis of results is facilitated via a flexible query generator.

Conclusion

We have developed an integrated system tailored to the specific needs of research projects using high density TMAs. It covers the complete workflow of TMA production, experimental use and subsequent analysis. The system is freely available for academic and non-profit institutions from http://genome.tugraz.at/Software/TAMEE webcite.