@article{81c72367421b429c8789501274924661,
title = "Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome",
abstract = "The ability to reliably and reproducibly measure any protein of the human proteome in any tissue or cell type would be transformative for understanding systems-level properties as well as specific pathways in physiology and disease. Here, we describe the generation and verification of a compendium of highly specific assays that enable quantification of 99.7% of the 20,277 annotated human proteins by the widely accessible, sensitive, and robust targeted mass spectrometric method selected reaction monitoring, SRM. This human SRMAtlas provides definitive coordinates that conclusively identify the respective peptide in biological samples. We report data on 166,174 proteotypic peptides providing multiple, independent assays to quantify any human protein and numerous spliced variants, non-synonymous mutations, and post-translational modifications. The data are freely accessible as a resource at http://www.srmatlas.org/, and we demonstrate its utility by examining the network response to inhibition of cholesterol synthesis in liver cells and to docetaxel in prostate cancer lines.",
author = "Ulrike Kusebauch and Campbell, {David S.} and Deutsch, {Eric W.} and Chu, {Caroline S.} and Spicer, {Douglas A.} and Brusniak, {Mi Youn} and Joseph Slagel and Zhi Sun and Jeffrey Stevens and Barbara Grimes and David Shteynberg and Hoopmann, {Michael R.} and Peter Blattmann and Ratushny, {Alexander V.} and Oliver Rinner and Paola Picotti and Christine Carapito and Huang, {Chung Ying} and Meghan Kapousouz and Henry Lam and Tommy Tran and Emek Demir and Aitchison, {John D.} and Chris Sander and Leroy Hood and Ruedi Aebersold and Moritz, {Robert L.}",
note = "Funding Information: This work was performed in part with federal funds from the American Recovery and Reinvestment Act (ARRA) funds through NIH, from the National Human Genome Research Institute grant RC2HG005805 (to R.L.M.), the National Institute of General Medical Sciences under grant R01GM087221, S10RR027584 and 2P50 GM076547/Center for Systems Biology (to R.L.M.), the Luxembourg Centre for Systems Biomedicine/University Luxembourg (to L.H.), the European Research Council grant ERC-2008-AdG 233226 and ERC-2014-AdG 670821 and the Swiss National Science Foundation (grant #31003A-130530) (to R.A.), and DAAD (fellowship to U.K.). We kindly thank K. Miller and C. Miller (Agilent Technologies), J. Louette, Drs. G. Sulyok and A. Schierholt (Thermo-Fisher), Dr. H. Wenschuh and L. Eckler (JPT) for their support, Dr. S. Carr for early access to ESP predictor, Drs. P. Gaudet and A. Bairoch for supporting the integration of neXtProt, and T. Farrah, S-T. Kwok, A. Aksoy, and P. Shannon for excellent technical support. Publisher Copyright: {\textcopyright} 2016 Elsevier Inc.",
year = "2016",
month = jul,
day = "28",
doi = "10.1016/j.cell.2016.06.041",
language = "English (US)",
volume = "166",
pages = "766--778",
journal = "Cell",
issn = "0092-8674",
publisher = "Cell Press",
number = "3",
}