Summary
Objectives:
Tissue microarray (TMA) techniques are among the most promising developments in biomedicine during the last decade. Bioinformatics techniques are indispensable for storing and processing the masses of data related with tissue archive administration and investigation of raw data. Interrelationship between experimental and computational work will be shown.
Methods:
Tissue specimen arrays allow parallel analysis of huge amounts of samples. TMA techniques thus produce enormous masses of raw data, and optimal use of data can only be made using modern bioinformatics techniques based on huge storage systems, scalable multilayer software architecture and high-throughput algorithms for retrieval and statistical processing. Further crucial issues addressed by informatics techniques are specimen identification during the whole processing chain, and anonymization whenever scientific work is performed without regard to a certain patient.
Results:
TMA supported by bioinformatics methods has helped in identification of biomarkers, mainly in cancer diagnosis. Moreover, it provides powerful means of quality assurance and training in histopathology.
Conclusions:
Further statistical analyses seem to be necessary to detect if certain biomarkers are present in nearly all kinds of specimen of the concerned patient, which would allow effective mass screening based on easily accessible specimen. Some investigations showed low dependence on the specimen localization, whereas others suggest to be extremely careful in material selection for the recipient block.
Keywords
Computational biology - data collection - microarray analysis - pathology - biological markers introduction
