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

Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell‐lines

“…These typically offer higher levels of integration and standardization, but are sometimes also more specialized in terms of scope and coverage. They include GPMDB [12], MOPED [13], ProteomicsDB [14], MaxQB [15], and Human Proteome Map [16]. In addition, other databases whose primary focus is perhaps not exclusively on proteomics may also contain information on protein abundances, notably UniProt [17] and NextProt [18].…”

Dynamic Proteomics: Methodologies and Analysis

“…MaxQuant is a comprehensive software package widely used for the analysis and quantitation of MS-based proteomic data, including SILAC, that was created by Jurgen Cox and Matthias Mann [14,15]. It is made available as freeware and can be downloaded from http://maxquant.org/.…”

“…Using the MaxQuant software for data processing allows the grouping and separation of data from individual MS analyses [14,15]. MaxQuant can combine data from all the protein fractions from a sample (if it has been pre-fractionated before MS), and can separate different samples from different conditions, but combine and output the results in one excel sheet.…”

“…It is made available as freeware and can be downloaded from http://maxquant.org/. MaxQuant includes a search engine that can use raw MS data from the mass spectrometer, perform peak picking, mass recalibration, SILAC pair matching and quantification, label free quantification, database searching (using Andromeda), and output peptide and protein data in extensive detail [14,15]. While other commercial and freeware software options are also available for analysis of MS data we routinely use the MaxQuant package which works very well specifically for the protocols described here.…”

“…In an ideal world having a database with experimental values linked to reliable meta data describing the experimental parameters is the best case scenario for proteomic data management [2][3][4]15].…”