Literature DB >> 21250827

Design and initial characterization of the SC-200 proteomics standard mixture.

Andrew Bauman1, Roger Higdon, Sean Rapson, Brenton Loiue, Jason Hogan, Robin Stacy, Alberto Napuli, Wenjin Guo, Wesley van Voorhis, Jared Roach, Vincent Lu, Elizabeth Landorf, Elizabeth Stewart, Natali Kolker, Frank Collart, Peter Myler, Gerald van Belle, Eugene Kolker.   

Abstract

High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate = 1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.

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Year:  2011        PMID: 21250827      PMCID: PMC3110723          DOI: 10.1089/omi.2010.0118

Source DB:  PubMed          Journal:  OMICS        ISSN: 1536-2310


  17 in total

1.  Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein.

Authors:  Yasushi Ishihama; Yoshiya Oda; Tsuyoshi Tabata; Toshitaka Sato; Takeshi Nagasu; Juri Rappsilber; Matthias Mann
Journal:  Mol Cell Proteomics       Date:  2005-06-14       Impact factor: 5.911

Review 2.  Protein identification and expression analysis using mass spectrometry.

Authors:  Eugene Kolker; Roger Higdon; Jason M Hogan
Journal:  Trends Microbiol       Date:  2006-04-17       Impact factor: 17.079

Review 3.  Experimental standards for high-throughput proteomics.

Authors:  Jason M Hogan; Roger Higdon; Eugene Kolker
Journal:  OMICS       Date:  2006

4.  A predictive model for identifying proteins by a single peptide match.

Authors:  Roger Higdon; Eugene Kolker
Journal:  Bioinformatics       Date:  2006-11-22       Impact factor: 6.937

5.  Experiment-specific estimation of peptide identification probabilities using a randomized database.

Authors:  Roger Higdon; Jason M Hogan; Natali Kolker; Gerald van Belle; Eugene Kolker
Journal:  OMICS       Date:  2007

Review 6.  Development of BIATECH-54 standard mixtures for assessment of protein identification and relative expression.

Authors:  Eugene Kolker; Jason M Hogan; Roger Higdon; Natali Kolker; Elizabeth Landorf; Alexander F Yakunin; Frank R Collart; Gerald van Belle
Journal:  Proteomics       Date:  2007-10       Impact factor: 3.984

Review 7.  Bacterial systems for production of heterologous proteins.

Authors:  Sarah Zerbs; Ashley M Frank; Frank R Collart
Journal:  Methods Enzymol       Date:  2009       Impact factor: 1.600

Review 8.  The Seattle Structural Genomics Center for Infectious Disease (SSGCID).

Authors:  P J Myler; R Stacy; L Stewart; B L Staker; W C Van Voorhis; G Varani; G W Buchko
Journal:  Infect Disord Drug Targets       Date:  2009-11

9.  A HUPO test sample study reveals common problems in mass spectrometry-based proteomics.

Authors:  Alexander W Bell; Eric W Deutsch; Catherine E Au; Robert E Kearney; Ron Beavis; Salvatore Sechi; Tommy Nilsson; John J M Bergeron
Journal:  Nat Methods       Date:  2009-06       Impact factor: 28.547

10.  The APEX Quantitative Proteomics Tool: generating protein quantitation estimates from LC-MS/MS proteomics results.

Authors:  John C Braisted; Srilatha Kuntumalla; Christine Vogel; Edward M Marcotte; Alan R Rodrigues; Rong Wang; Shih-Ting Huang; Erik S Ferlanti; Alexander I Saeed; Robert D Fleischmann; Scott N Peterson; Rembert Pieper
Journal:  BMC Bioinformatics       Date:  2008-12-09       Impact factor: 3.169

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  3 in total

1.  MOPED enables discoveries through consistently processed proteomics data.

Authors:  Roger Higdon; Elizabeth Stewart; Larissa Stanberry; Winston Haynes; John Choiniere; Elizabeth Montague; Nathaniel Anderson; Gregory Yandl; Imre Janko; William Broomall; Simon Fishilevich; Doron Lancet; Natali Kolker; Eugene Kolker
Journal:  J Proteome Res       Date:  2013-12-18       Impact factor: 4.466

2.  Dynamic Proteomic Analysis of Pancreatic Mesenchyme Reveals Novel Factors That Enhance Human Embryonic Stem Cell to Pancreatic Cell Differentiation.

Authors:  Holger A Russ; Limor Landsman; Christopher L Moss; Roger Higdon; Renee L Greer; Kelly Kaihara; Randy Salamon; Eugene Kolker; Matthias Hebrok
Journal:  Stem Cells Int       Date:  2015-11-22       Impact factor: 5.443

3.  Learning from decoys to improve the sensitivity and specificity of proteomics database search results.

Authors:  Amit Kumar Yadav; Dhirendra Kumar; Debasis Dash
Journal:  PLoS One       Date:  2012-11-26       Impact factor: 3.240

  3 in total

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