Mass-spectrometry-based draft of the human proteome

Abstract

Proteomes are characterized by large protein-abundance differences, cell-type- and time-dependent expression patterns and post-translational modifications, all of which carry biological information that is not accessible by genomics or transcriptomics. Here we present a mass-spectrometry-based draft of the human proteome and a public, high-performance, in-memory database for real-time analysis of terabytes of big data, called ProteomicsDB. The information assembled from human tissues, cell lines and body fluids enabled estimation of the size of the protein-coding genome, and identified organ-specific proteins and a large number of translated lincRNAs (long intergenic non-coding RNAs). Analysis of messenger RNA and protein-expression profiles of human tissues revealed conserved control of protein abundance, and integration of drug-sensitivity data enabled the identification of proteins predicting resistance or sensitivity. The proteome profiles also hold considerable promise for analysing the composition and stoichiometry of protein complexes. ProteomicsDB thus enables navigation of proteomes, provides biological insight and fosters the development of proteomic technology.

Quantitative analysis of human centrosome architecture by targeted proteomics and fluorescence imaging

“…Comprehensive studies on the proteomes of cells, tissues, or organisms have been reported (e.g. Addona et al , 2009; Nilsson et al , 2010; Beck et al , 2011; Nagaraj et al , 2011; for review, see Bensimon et al , 2012; Kulak et al , 2014; Wilhelm et al , 2014; Hein et al , 2015; Richards et al , 2015), but accurate quantitative information on proteins expressed at low levels remains scarce. As a consequence, published estimates for the abundance of specific proteins sometimes vary over several orders of magnitude.…”

Exploring the potential of public proteomics data

“…Several such generic repositories are now available, for example PRIDE 4, GPMDB 5, PeptideAtlas 6, and MassIVE (http://massive.ucsd.edu/ProteoSAFe) for shotgun results; and PASSEL 7, SRMAtlas (http://www.srmatlas.org), and Panorama 8 for targeted proteomics quantification data. More specific databases have also been established, related to: diseases, for example TBDB for tuberculosis 9; organisms, for example ProteomicsDB 10 and the Human Proteome Map 11 for the human proteome, and pep2pro for Arabidopsis 12; or subproteomes, for example CSF‐PR 13 for cerebrospinal fluid or TOPPR 14 and TopFIND 15 for in vivo cleaved proteins. For a comprehensive overview of the current proteomics databases and repositories, please see Perez‐Riverol et al 16.…”

“…Note that it is also possible to develop tools to look for novel transitions in publicly available shotgun datasets. For example, MRMaid 34, PeptidePicker 35, 36, and ProteomicsDB 10 do this by reusing identification data coming from PRIDE and other sources.…”

“…More recently, there is an ongoing debate about the two drafts of the human proteome published in Nature in 2014 10, 11. Both studies provided an exemplary precedent by sharing all generated data (available as datasets PXD000561 and PXD000865 in PRIDE).…”

“…ProteomicsDB is one of the main outputs of the draft human proteome by Wilhelm et al 10, and represents a nice example of data reprocessing. For their analysis, they combined their own generated experimental results with publicly available data.…”