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PROTEOMER: A workflow-optimized laboratory information management system for 2-D electrophoresis-centered proteomics.

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Kreitler,  Thomas
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50637

Wierling,  Christoph
Systems Biology (Christoph Wierling), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

Giavalisco,  Patrick
Max Planck Society;

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Citation

Nebrich, G., Herrmann, M., Hartl, D., Diedrich, M., Kreitler, T., Wierling, C., et al. (2009). PROTEOMER: A workflow-optimized laboratory information management system for 2-D electrophoresis-centered proteomics. Proteomics, 9(7), 1795-1808. doi:10.1002/pmic.200800522.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-7DFF-E
Abstract
In recent years proteomics became increasingly important to functional genomics. Although a large amount of data is generated by high throughput large-scale techniques, a connection of these mostly heterogeneous data from different analytical platforms and of different experiments is limited. Data mining procedures and algorithms are often insufficient to extract meaningful results from large datasets and therefore limit the exploitation of the generated biological information. In our proteomic core facility, which almost exclusively focuses on 2-DE/MS-based proteomics, we developed a proteomic database custom tailored to our needs aiming at connecting MS protein identification information to 2-DE derived protein expression profiles. The tools developed should not only enable an automatic evaluation of single experiments, but also link multiple 2-DE experiments with MS-data on different levels and thereby helping to create a comprehensive network of our proteomics data. Therefore the key feature of our PROTEOMER database is its high cross-referencing capacity, enabling integration of a wide range of experimental data. To illustrate the workflow and utility of the system, two practical examples are provided to demonstrate that proper data cross-referencing can transform information into biological knowledge.