Literature DB >> 17890649

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

Eugene Kolker1, Jason M Hogan, Roger Higdon, Natali Kolker, Elizabeth Landorf, Alexander F Yakunin, Frank R Collart, Gerald van Belle.   

Abstract

Mixtures of known proteins have been very useful in the assessment and validation of methods for high-throughput (HTP) MS (MS/MS) proteomics experiments. However, these test mixtures have generally consisted of few proteins at near equal concentration or of a single protein at varied concentrations. Such mixtures are too simple to effectively assess the validity of error rates for protein identification and differential expression in HTP MS/MS studies. This work aimed at overcoming these limitations and simulating studies of complex biological samples. We introduced a pair of 54-protein standard mixtures of variable concentrations with up to a 1000-fold dynamic range in concentration and up to ten-fold expression ratios with additional negative controls (infinite expression ratios). These test mixtures comprised 16 off-the-shelf Sigma-Aldrich proteins and 38 Shewanella oneidensis proteins produced in-house. The standard proteins were systematically distributed into three main concentration groups (high, medium, and low) and then the concentrations were varied differently for each mixture within the groups to generate different expression ratios. The mixtures were analyzed with both low mass accuracy LCQ and high mass accuracy FT-LTQ instruments. In addition, these 54 standard proteins closely follow the molecular weight distributions of both bacterial and human proteomes. As a result, these new standard mixtures allow for a much more realistic assessment of approaches for protein identification and label-free differential expression than previous mixtures. Finally, methodology and experimental design developed in this work can be readily applied in future to development of more complex standard mixtures for HTP proteomics studies.

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Year:  2007        PMID: 17890649     DOI: 10.1002/pmic.200700088

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  7 in total

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Authors:  Samuel L Volchenboum; Kolbrun Kristjansdottir; Donald Wolfgeher; Stephen J Kron
Journal:  Mol Cell Proteomics       Date:  2009-05-11       Impact factor: 5.911

2.  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

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

Authors:  Andrew Bauman; 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
Journal:  OMICS       Date:  2011-01-21

4.  Analyzing LC-MS/MS data by spectral count and ion abundance: two case studies.

Authors:  Thomas I Milac; Timothy W Randolph; Pei Wang
Journal:  Stat Interface       Date:  2012       Impact factor: 0.582

5.  A pilot study to evaluate the application of a generic protein standard panel for quality control of biomarker detection technologies.

Authors:  Susan Pang; Enamul S Ahsan; Hernan J Valdivia; Jesus Minguez; Carole A Foy
Journal:  BMC Res Notes       Date:  2011-08-11

6.  Estradiol induces cytochrome P450 2B6 expression at high concentrations: implication in estrogen-mediated gene regulation in pregnancy.

Authors:  Kwi Hye Koh; Steve Jurkovic; Kyunghee Yang; Su-Young Choi; Jin Woo Jung; Kwang Pyo Kim; Wei Zhang; Hyunyoung Jeong
Journal:  Biochem Pharmacol       Date:  2012-03-30       Impact factor: 5.858

7.  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

  7 in total

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