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

Area of Science:

• Proteomics
• Functional Genomics
• Bioinformatics

Background:

• Proteomics generates vast, heterogeneous data from high-throughput techniques.
• Integrating data from different analytical platforms and experiments is challenging.
• Current data mining methods often fail to extract meaningful biological insights from large proteomic datasets.

Purpose of the Study:

• To develop a custom proteomic database for a core facility focused on 2-DE/MS-based proteomics.
• To connect MS protein identification data with 2-DE protein expression profiles.
• To enable automatic evaluation and cross-referencing of multiple experiments and data types.

Main Methods:

• Development of a custom proteomic database named "PROTEOMER".
• Implementation of high cross-referencing capacity for data integration.
• Focus on linking MS protein identification with 2-DE derived protein expression profiles.

Main Results:

• The PROTEOMER database facilitates the automatic evaluation of single and multiple 2-DE/MS experiments.
• High cross-referencing capacity enables the integration of diverse experimental data.
• Demonstrated transformation of proteomic information into biological knowledge through practical examples.

Conclusions:

• The PROTEOMER database effectively integrates heterogeneous proteomic data.
• The system supports the creation of a comprehensive network of proteomics data.
• Proper data cross-referencing is crucial for advancing biological understanding in proteomics.