2DDB - a bioinformatics solution for analysis of quantitative proteomics data.

Lars Malmström,Thomas Laurell,György Marko-Varga,Gunilla Westergren-Thorsson,Johan Malmström

doi:10.1186/1471-2105-7-158

Lars Malmström, Thomas Laurell + Show 3 more

Open Access

https://doi.org/10.1186/1471-2105-7-158

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Journal: BMC bioinformatics	Publication Date: Mar 20, 2006
Citations: 43	License type: cc-by

Affiliation: Lund University, ETH Zurich

Abstract

BackgroundWe present 2DDB, a bioinformatics solution for storage, integration and analysis of quantitative proteomics data. As the data complexity and the rate with which it is produced increases in the proteomics field, the need for flexible analysis software increases.Results2DDB is based on a core data model describing fundamentals such as experiment description and identified proteins. The extended data models are built on top of the core data model to capture more specific aspects of the data. A number of public databases and bioinformatical tools have been integrated giving the user access to large amounts of relevant data. A statistical and graphical package, R, is used for statistical and graphical analysis. The current implementation handles quantitative data from 2D gel electrophoresis and multidimensional liquid chromatography/mass spectrometry experiments.ConclusionThe software has successfully been employed in a number of projects ranging from quantitative liquid-chromatography-mass spectrometry based analysis of transforming growth factor-beta stimulated fi-broblasts to 2D gel electrophoresis/mass spectrometry analysis of biopsies from human cervix. The software is available for download at SourceForge.

Highlights

We present 2DDB, a bioinformatics solution for storage, integration and analysis of quantitative proteomics data
The data models are implemented in a relational database [28] and comprise tables ranging from experimental meta-data tables to tables holding results from statistical and bioinformatical analysis
Basing the database on amino acid sequence alleviates a number of problems

Summary

Conclusion

We have developed a software tool that enables fast and accurate analysis of quantitative proteomics data. As more technologies were incorporated it became clear that certain features are universal whereas others are not. The universal features became the core data model and the other features ended up in complementary, technology-specific tables. As a result of this development, the software contains large number of features that are beyond the scope of this article. JM, TL, GMW and GWT have been involved in the design of the analysis tools, especially protein explorer. They contributed with valuable discussions and feedback during the development.

Background

Results and discussion

21. Jones DT