%0 Journal Article
%T LimsPortal and BonsaiLIMS: development of a lab information management system for translational medicine
%A Timothy G Bath
%A Selcuk Bozdag
%A Vackar Afzal
%A Daniel Crowther
%J Source Code for Biology and Medicine
%D 2011
%I BioMed Central
%R 10.1186/1751-0473-6-9
%X We have designed and developed a LIMS, BonsaiLIMS, around a very simple data model that can be easily implemented using a variety of technologies, and can be easily extended as specific requirements dictate. A reference implementation using Oracle 11 g database and the Python framework, Django is presented.By focusing on a minimal feature set and a modular design we have been able to deploy the BonsaiLIMS system very quickly. The benefits to our institute have been the avoidance of the prolonged implementation timescales, budget overruns, scope creep, off-specifications and user fatigue issues that typify many enterprise software implementations. The transition away from using local, uncontrolled records in spreadsheet and paper formats to a centrally held, secured and backed-up database brings the immediate benefits of improved data visibility, audit and overall data quality. The open-source availability of this software allows others to rapidly implement a LIMS which in itself might sufficiently address user requirements. In situations where this software does not meet requirements, it can serve to elicit more accurate specifications from end-users for a more heavyweight LIMS by acting as a demonstrable prototype.Within the core laboratory of the Translational Medicine Research Collaboration (TMRC) [1], we routinely profile human samples in order to identify molecular biomarkers. We need to track clinical samples during projects that often use multiple profiling technologies such as Mass Spectrometry based proteomics, ELISA immunoassays and Affymetrix profiling technologies on overlapping patient samples. The tracking of primary clinical samples and derived laboratory samples such as purified mRNA aliquots becomes arduous as the complexity and the sample number increases. Commercial LIMS solutions are available [2] which are not only powerful enough to handle these experimental data sets but are also robust and provide auditing functions to allow experimental labs
%U http://www.scfbm.org/content/6/1/9