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.
A mass-spectrometry-based draft of the human proteome and a public database for analysis of proteome data are presented; assembled information is used to estimate the size of the protein-coding genome, to identify organ-specific proteins, proteins predicting drug resistance or sensitivity, and many translated long intergenic non-coding RNAs, and to reveal conserved control of protein abundance.
Mapping the human proteome
More than a decade after publication of the draft human genome sequence, there is no direct equivalent for the human proteome. But in this issue of
Nature
two groups present mass spectrometry-based analysis of human tissues, body fluids and cells mapping the large majority of the human proteome. Akhilesh Pandey and colleagues identified 17,294 protein-coding genes and provide evidence of tissue- and cell-restricted proteins through expression profiling. They highlight the importance of proteogenomic analysis by identifying translated proteins from annotated pseudogenes, non-coding RNAs and untranslated regions. The data set is available on
http://www.humanproteomemap.org
. Bernhard Kuster and colleagues have assembled protein evidence for 18,097 genes in ProteomicsDB (available on
https://www.proteomicsdb.org
) and highlight the utility of the data, for example the identification of hundreds of translated lincRNAs, drug-sensitivity markers and...

Mass-spectrometry-based draft of the human proteome

10.1038/nature13319