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

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.

Fig. 1.

Database architecture and interfaces to other applications.

Fig. 2.

Number of proteins (red bars) and peptides (blue bars) identified in increasing number of cell lines. In total, 10,183 non-redundant proteins and 103,869 non-redundant peptides were identified (see text for details).

Fig. 3.

A, query proteins for human DNA polymerase epsilon subunits. B, select POLE and show details on this protein. C, histogram of protein expression across 11 cell lines. D, expression of POLE compared with expression of all other detected proteins in HEK293 cells. E, expression of the mouse ortholog across 28 mouse tissues.

Fig. 4.

Sequence coverage of POLE. The blue boxes are two c4-type domains. The gray boxes are in silico digested peptides with masses between 0.6 and 4 kDa. Detected peptides are colored by their label-free intensities across the 11 tested cell lines with three replicates each.

Fig. 5.

A, human karyotype. B, histogram of proteins identified by MS in the 11 cell line project (gray) and annotated proteins (blue) on chromosome 21.

Fig. 6.

A, query for unique peptides for CDK2 with a score greater 80 and no missed cleavages. B, the fragment spectrum with the best evidence for peptide AFGVPVR.

Fig. 7.

A, distribution of correlation values. For each protein group with two or more peptides identified, the Spearman correlation between the intensities of the peptides and the detection probability were calculated. B and C, examples of proteins with high correlation (0.92): Q8NFI3-ENGASE (B) and low correlation (0.27): Q92918-MAP4K1 (C).