Document Detail


Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics.
MedLine Citation:
PMID:  17617667     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
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
Related Documents :
15669717 - Spectral quality assessment for high-throughput tandem mass spectrometry proteomics.
11155137 - Molecular epidemiology.
20610277 - Statistical consideration for clinical biomarker research in bladder cancer.
20399287 - Towards proteome standards: the use of absolute quantitation in high-throughput biomark...
21846307 - Haptic discrimination of different types of pencils during writing.
23598917 - The predictability of consumer visitation patterns.
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2007-07-07
Journal Detail:
Title:  Molecular & cellular proteomics : MCP     Volume:  6     ISSN:  1535-9476     ISO Abbreviation:  Mol. Cell Proteomics     Publication Date:  2007 Oct 
Date Detail:
Created Date:  2007-10-04     Completed Date:  2007-11-09     Revised Date:  2007-12-03    
Medline Journal Info:
Nlm Unique ID:  101125647     Medline TA:  Mol Cell Proteomics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1741-8     Citation Subset:  IM    
Affiliation:
Departments of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA. amol@cs.washington.edu
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Angiotensin II / pharmacology
Animals
Bias (Epidemiology)
Biological Markers / metabolism
Cell Cycle / drug effects
Chromatography, Liquid
Disease Models, Animal
Freezing
Humans
Huntington Disease / metabolism
Mass Spectrometry*
Mice
Proteomics / methods*
Reproducibility of Results
Research Design*
Saccharomyces cerevisiae / cytology,  drug effects
Time Factors
Grant Support
ID/Acronym/Agency:
1U54 AI57141-01/AI/NIAID NIH HHS; P30ES07033/ES/NIEHS NIH HHS
Chemical
Reg. No./Substance:
0/Biological Markers; 11128-99-7/Angiotensin II

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Nerve growth factor receptor TrkA signaling in breast cancer cells involves Ku70 to prevent apoptosi...
Next Document:  The chromatin remodeling protein, SRCAP, is critical for deposition of the histone variant H2A.Z at ...