Analysis of High Accuracy, Quantitative Proteomics Data in the MaxQB Database

By Christoph Schaab, T. Geiger, Gabriele Stoehr, Jüergen Cox, Matthias Mann. 2012

157 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Spectroscopy

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Citations in the corpus

UniProt: the universal protein knowledgebase (2016, methodology): “We have developed a pipeline that provides evidence annotations to UniProtKB records using proteomics datasets currently from PeptideAtlas (21) MaxQB (22) and Encyclopedia of Proteome Dynamics (23).”
Plasma Proteome Profiling to Assess Human Health and Disease. (2016, background): “The deep proteome data can be assessed in Table S5 and in the MaxQB database (Schaab et al., 2012), which also displays the mass spectrometric evidence and MS/MS transitions for all identified peptides.”
The MaxQuant computational platform for mass spectrometry-based shotgun proteomics (2016, methodology): “The MaxQB database 33 is used to map the MaxQuant output tables to a relational database scheme and lays the foundation for web-based visualization of customized in-house projects.”
2016 update of the PRIDE database and its related tools (2015, background): “In addition to the PX resources, there are other valuable proteomics databases and resources available, providing protein expression information derived from MS proteomics data, most notably the Global Proteome Machine Database (GPMDB) (10), ProteomicsDB (11), the Human Proteome Map (12), MaxQB (13)”
The UniProtKB guide to the human proteome (2016, background): “This includes peptides from PeptideAtlas (32) and MaxQB (33) for the human proteome as well as for a number of other species.”
Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. (2014, methodology): “In addition, all the identified phosphosites, phosphopeptides, and proteins, including their high-resolution fragment spectra, are easily accessible through the MaxQB database (Schaab et al., 2012) via a user-friendly web interface at http://maxqb.”
UniProt: the universal protein knowledgebase in 2021 (2020, background): “UniProt additionally integrates and visualizes unique and non-unique peptides identified by mass spectrometry proteomic data deposited through the ProteomeXchange Consortium (31) (e.g. PeptideAtlas (32), MassIVE (33) and jPOST (34)) and other large-scale initiatives (CPTAC (35), ProteomicsDB (36), M”
The Proteomics Identifications (PRIDE) database and associated tools: status in 2013 (2012, background): “Other MS data repositories, such as MaxQB (12), are more specialized [for an extensive review, see (13)] or are restricted to one particular analysis workﬂow.”
Comparative Proteomic Analysis of Eleven Common Cell Lines Reveals Ubiquitous but Varying Expression of Most Proteins (2012, background): “The complete dataset of the eleven proteomes is deposited in the MaxQB database, which is the subject of another paper in this issue (17).”
Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell‐lines (2015, methodology): “They include GPMDB [12], MOPED [13], ProteomicsDB [14], MaxQB [15], and Human Proteome Map [16].”