Literature DB >> 21898822

An improved method for the construction of decoy peptide MS/MS spectra suitable for the accurate estimation of false discovery rates.

Erik Ahrné1, Yuki Ohta, Frederic Nikitin, Alexander Scherl, Frederique Lisacek, Markus Müller.   

Abstract

The relevance of libraries of annotated MS/MS spectra is growing with the amount of proteomic data generated in high-throughput experiments. These reference libraries provide a fast and accurate way to identify newly acquired MS/MS spectra. In the context of multiple hypotheses testing, the control of the number of false-positive identifications expected in the final result list by means of the calculation of the false discovery rate (FDR). In a classical sequence search where experimental MS/MS spectra are compared with the theoretical peptide spectra calculated from a sequence database, the FDR is estimated by searching randomized or decoy sequence databases. Despite on-going discussion on how exactly the FDR has to be calculated, this method is widely accepted in the proteomic community. Recently, similar approaches to control the FDR of spectrum library searches were discussed. We present in this paper a detailed analysis of the similarity between spectra of distinct peptides to set the basis of our own solution for decoy library creation (DeLiberator). It differs from the previously published results in some key points, mainly in implementing new methods that prevent decoy spectra from being too similar to the original library spectra while keeping important features of real MS/MS spectra. Using different proteomic data sets and library creation methods, we evaluate our approach and compare it with alternative methods.
Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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Year:  2011        PMID: 21898822     DOI: 10.1002/pmic.201000665

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


  5 in total

1.  Clustering and filtering tandem mass spectra acquired in data-independent mode.

Authors:  Huisong Pak; Frederic Nikitin; Florent Gluck; Frederique Lisacek; Alexander Scherl; Markus Muller
Journal:  J Am Soc Mass Spectrom       Date:  2013-09-05       Impact factor: 3.109

2.  Protein profile of rice (Oryza sativa) seeds.

Authors:  Yanhua Yang; Li Dai; Hengchuan Xia; Keming Zhu; Haijun Liu; Keping Chen
Journal:  Genet Mol Biol       Date:  2013-03-04       Impact factor: 1.771

3.  Mass spectrometry-based proteomics for the analysis of chromatin structure and dynamics.

Authors:  Monica Soldi; Alessandro Cuomo; Michael Bremang; Tiziana Bonaldi
Journal:  Int J Mol Sci       Date:  2013-03-06       Impact factor: 5.923

Review 4.  Open source libraries and frameworks for mass spectrometry based proteomics: a developer's perspective.

Authors:  Yasset Perez-Riverol; Rui Wang; Henning Hermjakob; Markus Müller; Vladimir Vesada; Juan Antonio Vizcaíno
Journal:  Biochim Biophys Acta       Date:  2013-03-01

5.  The quantitative and condition-dependent Escherichia coli proteome.

Authors:  Alexander Schmidt; Karl Kochanowski; Silke Vedelaar; Erik Ahrné; Benjamin Volkmer; Luciano Callipo; Kèvin Knoops; Manuel Bauer; Ruedi Aebersold; Matthias Heinemann
Journal:  Nat Biotechnol       Date:  2015-12-07       Impact factor: 54.908

  5 in total

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