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Proteomic resources: integrating biomedical information in humans.
Shubha Suresh1, S Sujatha Mohan, Goparani Mishra
1Institute of Bioinformatics, International Tech Park Ltd., Bangalore 560 066, India.
Gene
|October 6, 2005
Summary
High-throughput proteomic technologies generate massive datasets, posing challenges for data management and sharing. This review covers common proteomic techniques, data sharing issues, standardization efforts, and resources for proteomic data analysis.
Area of Science:
- Proteomics
- Bioinformatics
- Systems Biology
Background:
- High-throughput proteomic technologies have advanced significantly, leading to large-scale data generation.
- Effective management and sharing of this vast proteomic data remain significant challenges.
Purpose of the Study:
- To discuss commonly used high-throughput proteomic techniques.
- To review challenges in proteomic data sharing and dissemination.
- To highlight community initiatives for data standardization and available analysis resources.
Main Methods:
- Review of current high-throughput proteomic technologies.
- Analysis of issues related to proteomic data sharing and dissemination.
- Overview of standardization efforts (formats, ontologies).
- Compilation of web-based resources and databases for proteomic data analysis.
Main Results:
- Identification of key high-throughput proteomic techniques.
- Enumeration of major obstacles in proteomic data management and sharing.
- Description of ongoing community efforts to standardize proteomic data.
- Presentation of available resources for proteomic data analysis.
Conclusions:
- Standardization of data formats and ontologies is crucial for effective proteomic data sharing.
- Integration of proteomic data with genomic and transcriptomic data will enable systems biology approaches.
- Web-based resources facilitate the analysis of complex proteomic datasets.