Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics.

Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics. Research Abstract Details 

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Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics. Abstract Text:

Amol Prakash,Brian Piening,Jeff Whiteaker,Heidi Zhang,Scott A Shaffer,Daniel Martin,Laura Hohmann,Kelly Cooke,James M Olson,Stacey Hansen,Mark R Flory,Hookeun Lee,Julian Watts,David R Goodlett,Ruedi Aebersold,Amanda Paulovich,Benno Schwikowski,

Mass spectrometry-based proteomics holds great promise as a discovery tool for biomarker candidates in the early detection of diseases. Recently much emphasis has been placed upon producing highly reliable data for quantitative profiling for which highly reproducible methodologies are indispensable. The main problems that affect experimental reproducibility stem from variations introduced by sample collection, preparation, and storage protocols and LC-MS settings and conditions. On the basis of a formally precise and quantitative definition of similarity between LC-MS experiments, we have developed Chaorder, a fully automatic software tool that can assess experimental reproducibility of sets of large scale LC-MS experiments. By visualizing the similarity relationships within a set of experiments, this tool can form the basis of systematic quality control and thus help assess the comparability of mass spectrometry data over time, across different laboratories, and between instruments. Applying Chaorder to data from multiple laboratories and a range of instruments, experimental protocols, and sample complexities revealed biases introduced by the sample processing steps, experimental protocols, and instrument choices. Moreover we show that reducing bias by correcting for just a few steps, for example randomizing the run order, does not provide much gain in statistical power for biomarker discovery.

Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics. Publishing Authors By Initials

A Prakash,B Piening,J Whiteaker,H Zhang,SA Shaffer,D Martin,L Hohmann,K Cooke,JM Olson,S Hansen,MR Flory,H Lee,J Watts,DR Goodlett,R Aebersold,A Paulovich,B Schwikowski,

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Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: Molecular & cellular proteomics : MCP

VOLUME: 6

Page Numbers: 1741-8

Journal Abbreviation: Mol. Cell Proteomics

ISSN: 1535-9476

DAY: 7

MONTH: 07

YEAR: 2007

Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics. Information

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LANGUAGE: eng

NlmUniqueID: 101125647

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Grant and Affiliation Information for Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics.

AFFILIATION: Departments of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA. amol@cs.washington.edu

Country: United States

AGENCY: United States NIEHS

GRANT: P30ES07033

ACRONYM: ES

MEDLINETA: Mol Cell Proteomics

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