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Analysis of high accuracy, quantitative proteomics data in the MaxQB database.

Schaab C, Geiger T, Stoehr G, Cox J, Mann M - Mol. Cell Proteomics (2012)

Bottom Line: We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes.Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation.This information can be used to pinpoint false protein identifications, independently of peptide database scores.

View Article: PubMed Central - PubMed

Affiliation: Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, D-82152 Martinsried, Germany.

ABSTRACT
MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment spectra, is accessible to the community via a user-friendly web interface at http://www.biochem.mpg.de/maxqb.

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Database architecture and interfaces to other applications.
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Related In: Results  -  Collection

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Figure 1: Database architecture and interfaces to other applications.

Mentions: MaxQB serves as a generic repository and analysis platform for high resolution MS-based proteomics experiments. As such, it stores details about protein and peptide identifications together with the corresponding high or low resolution fragment spectra and quantitative information, such as SILAC ratios or label-free intensities. To enable smooth upload of data, MaxQB is tightly integrated with MaxQuant (23) (Fig. 1). At the end of data processing, the user of MaxQuant is asked whether she wants to upload the data to the database. In this case, the data is submitted by calling a simple object-based protocol (SOAP)-based web service. Alternatively, the data can be manually uploaded through the user interface of MaxQB. In either case, the user is asked to enter additional meta information, such as the project name, experiment name, and workflow parameters. All of the data are stored in a relational SQL database running on an Oracle relational database management system. The user can browse, search, and retrieve the data through a web interface. Furthermore, the data can be accessed either through SQL queries or preferably through SOAP web services from visualization and data analysis tools like the Perseus module for bioinformatic analysis in MaxQuant, R (www.r-project.org), Matlab (The Mathworks, Natick, MA), or Spotfire (TIBCO, Palo Alto, CA).


Analysis of high accuracy, quantitative proteomics data in the MaxQB database.

Schaab C, Geiger T, Stoehr G, Cox J, Mann M - Mol. Cell Proteomics (2012)

Database architecture and interfaces to other applications.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3316731&req=5

Figure 1: Database architecture and interfaces to other applications.
Mentions: MaxQB serves as a generic repository and analysis platform for high resolution MS-based proteomics experiments. As such, it stores details about protein and peptide identifications together with the corresponding high or low resolution fragment spectra and quantitative information, such as SILAC ratios or label-free intensities. To enable smooth upload of data, MaxQB is tightly integrated with MaxQuant (23) (Fig. 1). At the end of data processing, the user of MaxQuant is asked whether she wants to upload the data to the database. In this case, the data is submitted by calling a simple object-based protocol (SOAP)-based web service. Alternatively, the data can be manually uploaded through the user interface of MaxQB. In either case, the user is asked to enter additional meta information, such as the project name, experiment name, and workflow parameters. All of the data are stored in a relational SQL database running on an Oracle relational database management system. The user can browse, search, and retrieve the data through a web interface. Furthermore, the data can be accessed either through SQL queries or preferably through SOAP web services from visualization and data analysis tools like the Perseus module for bioinformatic analysis in MaxQuant, R (www.r-project.org), Matlab (The Mathworks, Natick, MA), or Spotfire (TIBCO, Palo Alto, CA).

Bottom Line: We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes.Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation.This information can be used to pinpoint false protein identifications, independently of peptide database scores.

View Article: PubMed Central - PubMed

Affiliation: Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, D-82152 Martinsried, Germany.

ABSTRACT
MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment spectra, is accessible to the community via a user-friendly web interface at http://www.biochem.mpg.de/maxqb.

Show MeSH