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PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics
Author(s) -
Nebrich Grit,
Herrmann Marion,
Hartl Daniela,
Diedrich Madeleine,
Kreitler Thomas,
Wierling Christoph,
Klose Joachim,
Giavalisco Patrick,
Zabel Claus,
Mao Lei
Publication year - 2009
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200800522
Subject(s) - workflow , proteomics , computer science , identification (biology) , data mining , data science , database , chemistry , biology , biochemistry , botany , gene
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.