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IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics

Higdon, Roger and Reiter, Lukas and Hather, Gregory and Haynes, Winston and Kolker, Natali and Stewart, Elizabeth and Bauman, Andrew T. and Picotti, Paola and Schmidt, Alexander and van Belle, Gerald and Aebersold, Ruedi and Kolker, Eugene. (2011) IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics. Journal of Proteomics , 75 (1). pp. 116-121.

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Official URL: http://edoc.unibas.ch/47682/

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Abstract

In high-throughput mass spectrometry proteomics, peptides and proteins are not simply identified as present or not present in a sample, rather the identifications are associated with differing levels of confidence. The false discovery rate (FDR) has emerged as an accepted means for measuring the confidence associated with identifications. We have developed the Systematic Protein Investigative Research Environment (SPIRE) for the purpose of integrating the best available proteomics methods. Two successful approaches to estimating the FDR for MS protein identifications are the MAYU and our current SPIRE methods. We present here a method to combine these two approaches to estimating the FDR for MS protein identifications into an integrated protein model (IPM). We illustrate the high quality performance of this IPM approach through testing on two large publicly available proteomics datasets. MAYU and SPIRE show remarkable consistency in identifying proteins in these datasets. Still, IPM results in a more robust FDR estimation approach and additional identifications, particularly among low abundance proteins. IPM is now implemented as a part of the SPIRE system.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Services Biozentrum > Proteomics (Schmidt)
UniBasel Contributors:Schmidt, Alexander
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Elsevier
ISSN:1874-3919
e-ISSN:1876-7737
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:30 Nov 2017 08:03
Deposited On:30 Nov 2017 08:03

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