From Project-to-Peptides: Customizing a Commercial LIMS for LC-MS Proteomics

w2-3 Proteomic analysis using LC-MS has the capability of producing significant amounts of useful data, and advances in the technology have significantly increased the number of samples that can be processed.However, without the full context of the experiment from which the data was derived, and knowledge of the processes by which the samples were prepared, interpretation and cross-platform comparison of LC-MS data are limited.Current LC-MS operating software is effective in managing operation of the LC-MS instrument itself, but provide little in the way of capturing information related to the samples that were processed.Commercial off-the-shelf Laboratory Information Management Systems (LIMS) are numerous and effective in capturing sample-level information, but most lack the structure for capturing experimental information, and there is little in the software industry available for capturing research-based study information.In the Bioinformatics department at Pfizer (formerly Wyeth), we have developed and implemented a system architecture that allows scientists to apply context to their LC-MS data, by (1) fully defining and annotating their experimental design using a custom “Study Management System”, (2) capturing the unique sample preparation processes of LC-MS using a customized commercially-available LIMS, and (3) integrating these systems with the LC-MS operating software and the Mascot and Sequest search engines and storing the results of these search engines in a relational database.Through these enhancements, the Mascot and Sequest search workflows are simplified and our scientists are able link the peptides and proteins identified by search engines back to the source sample annotations.This makes data analysis easier for individual experiments and also enables, for the first time, wider-scale comparative analyses across multiple experiments.This presentation will provide an overview of this architecture and it will describe how it has enhanced the ability of our scientists to gain useful knowledge from their data.