Literature DB >> 17237091

TOPP--the OpenMS proteomics pipeline.

Oliver Kohlbacher1, Knut Reinert, Clemens Gröpl, Eva Lange, Nico Pfeifer, Ole Schulz-Trieglaff, Marc Sturm.   

Abstract

MOTIVATION: Experimental techniques in proteomics have seen rapid development over the last few years. Volume and complexity of the data have both been growing at a similar rate. Accordingly, data management and analysis are one of the major challenges in proteomics. Flexible algorithms are required to handle changing experimental setups and to assist in developing and validating new methods. In order to facilitate these studies, it would be desirable to have a flexible 'toolbox' of versatile and user-friendly applications allowing for rapid construction of computational workflows in proteomics.
RESULTS: We describe a set of tools for proteomics data analysis-TOPP, The OpenMS Proteomics Pipeline. TOPP provides a set of computational tools which can be easily combined into analysis pipelines even by non-experts and can be used in proteomics workflows. These applications range from useful utilities (file format conversion, peak picking) over wrapper applications for known applications (e.g. Mascot) to completely new algorithmic techniques for data reduction and data analysis. We anticipate that TOPP will greatly facilitate rapid prototyping of proteomics data evaluation pipelines. As such, we describe the basic concepts and the current abilities of TOPP and illustrate these concepts in the context of two example applications: the identification of peptides from a raw dataset through database search and the complex analysis of a standard addition experiment for the absolute quantitation of biomarkers. The latter example demonstrates TOPP's ability to construct flexible analysis pipelines in support of complex experimental setups. AVAILABILITY: The TOPP components are available as open-source software under the lesser GNU public license (LGPL). Source code is available from the project website at www.OpenMS.de

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Year:  2007        PMID: 17237091     DOI: 10.1093/bioinformatics/btl299

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  78 in total

1.  msCompare: a framework for quantitative analysis of label-free LC-MS data for comparative candidate biomarker studies.

Authors:  Berend Hoekman; Rainer Breitling; Frank Suits; Rainer Bischoff; Peter Horvatovich
Journal:  Mol Cell Proteomics       Date:  2012-02-07       Impact factor: 5.911

2.  DeMix-Q: Quantification-Centered Data Processing Workflow.

Authors:  Bo Zhang; Lukas Käll; Roman A Zubarev
Journal:  Mol Cell Proteomics       Date:  2016-01-04       Impact factor: 5.911

3.  LC-MS Based Detection of Differential Protein Expression.

Authors:  Leepika Tuli; Habtom W Ressom
Journal:  J Proteomics Bioinform       Date:  2009-10-02

4.  Protein quantification across hundreds of experimental conditions.

Authors:  Zia Khan; Joshua S Bloom; Benjamin A Garcia; Mona Singh; Leonid Kruglyak
Journal:  Proc Natl Acad Sci U S A       Date:  2009-08-26       Impact factor: 11.205

5.  A novel alignment method and multiple filters for exclusion of unqualified peptides to enhance label-free quantification using peptide intensity in LC-MS/MS.

Authors:  Xianyin Lai; Lianshui Wang; Haixu Tang; Frank A Witzmann
Journal:  J Proteome Res       Date:  2011-09-21       Impact factor: 4.466

6.  From raw data to biological discoveries: a computational analysis pipeline for mass spectrometry-based proteomics.

Authors:  Mathieu Lavallée-Adam; Sung Kyu Robin Park; Salvador Martínez-Bartolomé; Lin He; John R Yates
Journal:  J Am Soc Mass Spectrom       Date:  2015-05-22       Impact factor: 3.109

7.  Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics.

Authors:  Tamar Geiger; Jacek R Wisniewski; Juergen Cox; Sara Zanivan; Marcus Kruger; Yasushi Ishihama; Matthias Mann
Journal:  Nat Protoc       Date:  2011-02       Impact factor: 13.491

8.  mzResults: an interactive viewer for interrogation and distribution of proteomics results.

Authors:  James T Webber; Manor Askenazi; Jarrod A Marto
Journal:  Mol Cell Proteomics       Date:  2011-01-25       Impact factor: 5.911

9.  Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0.

Authors:  Paulo C Carvalho; Diogo B Lima; Felipe V Leprevost; Marlon D M Santos; Juliana S G Fischer; Priscila F Aquino; James J Moresco; John R Yates; Valmir C Barbosa
Journal:  Nat Protoc       Date:  2015-12-10       Impact factor: 13.491

10.  Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments.

Authors:  Ole Schulz-Trieglaff; Egidijus Machtejevas; Knut Reinert; Hartmut Schlüter; Joachim Thiemann; Klaus Unger
Journal:  BioData Min       Date:  2009-04-07       Impact factor: 2.522

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