title: A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas.
abstract: Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.
authors.affiliation: Institute for Systems Biology, Seattle, WA 98109, USA. tfarrah@systemsbiology.org
authors.forename: Terry
authors.initials: T
authors.lastname: Farrah
authors.forename: Eric W
authors.initials: EW
authors.lastname: Deutsch
authors.forename: Gilbert S
authors.initials: GS
authors.lastname: Omenn
authors.forename: David S
authors.initials: DS
authors.lastname: Campbell
authors.forename: Zhi
authors.initials: Z
authors.lastname: Sun
authors.forename: Julie A
authors.initials: JA
authors.lastname: Bletz
authors.forename: Parag
authors.initials: P
authors.lastname: Mallick
authors.forename: Jonathan E
authors.initials: JE
authors.lastname: Katz
authors.forename: Johan
authors.initials: J
authors.lastname: Malmström
authors.forename: Reto
authors.initials: R
authors.lastname: Ossola
authors.forename: Julian D
authors.initials: JD
authors.lastname: Watts
authors.forename: Biaoyang
authors.initials: B
authors.lastname: Lin
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.forename: Robert L
authors.initials: RL
authors.lastname: Moritz
authors.forename: Ruedi
authors.initials: R
authors.lastname: Aebersold
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000097:blood*; D001798:Blood Proteins / Q000032:analysis / Q000737:chemistry / Q000592:standards; D002853:Chromatography, Liquid; D030562:Databases, Protein; D006801:Humans; D013058:Mass Spectrometry; D010455:Peptides / Q000097:blood / Q000737:chemistry / Q000592:standards; D010949:Plasma / Q000737:chemistry*; D020543:Proteome / Q000032:analysis* / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D012015:Reference Standards; D012984:Software; D014357:Trypsin / Q000378:metabolism
chemical_list: D015415:Biomarkers; D001798:Blood Proteins; D010455:Peptides; D020543:Proteome; D014357:Trypsin