Literature DB >> 21063960

OpenMS and TOPP: open source software for LC-MS data analysis.

Andreas Bertsch1, Clemens Gröpl, Knut Reinert, Oliver Kohlbacher.   

Abstract

Proteomics experiments based on state-of-the-art mass spectrometry produce vast amounts of data, which cannot be analyzed manually. Hence, software is needed which is able to analyze the data in an automated fashion. The need for robust and reusable software tools triggered the development of libraries implementing different algorithms for the various analysis steps. OpenMS is such a software library and provides a wealth of data structures and algorithms for the analysis of mass spectrometric data. For users unfamiliar with programming, TOPP ("The OpenMS Proteomics Pipeline") offers a wide range of already implemented tools sharing the same interface and designed for a specific analysis task each. TOPP thus makes the sophisticated algorithms of OpenMS accessible to nonprogrammers. The individual TOPP tools can be strung together into pipelines for analyzing mass spectrometry-based experiments starting from the raw output of the mass spectrometer. These analysis pipelines can be constructed using a graphical editor. Even complex analytical workflows can thus be analyzed with ease.

Mesh:

Substances:

Year:  2011        PMID: 21063960     DOI: 10.1007/978-1-60761-987-1_23

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  20 in total

1.  mz5: space- and time-efficient storage of mass spectrometry data sets.

Authors:  Mathias Wilhelm; Marc Kirchner; Judith A J Steen; Hanno Steen
Journal:  Mol Cell Proteomics       Date:  2011-09-29       Impact factor: 5.911

2.  LC-IMS-MS Feature Finder: detecting multidimensional liquid chromatography, ion mobility and mass spectrometry features in complex datasets.

Authors:  Kevin L Crowell; Gordon W Slysz; Erin S Baker; Brian L LaMarche; Matthew E Monroe; Yehia M Ibrahim; Samuel H Payne; Gordon A Anderson; Richard D Smith
Journal:  Bioinformatics       Date:  2013-09-05       Impact factor: 6.937

3.  Statistical approach to protein quantification.

Authors:  Sarah Gerster; Taejoon Kwon; Christina Ludwig; Mariette Matondo; Christine Vogel; Edward M Marcotte; Ruedi Aebersold; Peter Bühlmann
Journal:  Mol Cell Proteomics       Date:  2013-11-19       Impact factor: 5.911

4.  Structure and RNA-binding properties of the Not1-Not2-Not5 module of the yeast Ccr4-Not complex.

Authors:  Varun Bhaskar; Vladimir Roudko; Jérôme Basquin; Kundan Sharma; Henning Urlaub; Bertrand Séraphin; Elena Conti
Journal:  Nat Struct Mol Biol       Date:  2013-10-13       Impact factor: 15.369

5.  Novel data analysis tool for semiquantitative LC-MS-MS2 profiling of N-glycans.

Authors:  Hannu Peltoniemi; Suvi Natunen; Ilja Ritamo; Leena Valmu; Jarkko Räbinä
Journal:  Glycoconj J       Date:  2012-06-17       Impact factor: 2.916

6.  CHICKN: extraction of peptide chromatographic elution profiles from large scale mass spectrometry data by means of Wasserstein compressive hierarchical cluster analysis.

Authors:  Olga Permiakova; Romain Guibert; Alexandra Kraut; Thomas Fortin; Anne-Marie Hesse; Thomas Burger
Journal:  BMC Bioinformatics       Date:  2021-02-12       Impact factor: 3.169

7.  Quantitative measurement of phosphoproteome response to osmotic stress in arabidopsis based on Library-Assisted eXtracted Ion Chromatogram (LAXIC).

Authors:  Liang Xue; Pengcheng Wang; Lianshui Wang; Emily Renzi; Predrag Radivojac; Haixu Tang; Randy Arnold; Jian-Kang Zhu; W Andy Tao
Journal:  Mol Cell Proteomics       Date:  2013-05-08       Impact factor: 5.911

8.  Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis.

Authors:  Masahiro Sugimoto; Masato Kawakami; Martin Robert; Tomoyoshi Soga; Masaru Tomita
Journal:  Curr Bioinform       Date:  2012-03       Impact factor: 3.543

Review 9.  Middle-down approach: a choice to sequence and characterize proteins/proteomes by mass spectrometry.

Authors:  P Boomathi Pandeswari; Varatharajan Sabareesh
Journal:  RSC Adv       Date:  2019-01-02       Impact factor: 4.036

10.  xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data.

Authors:  Karan Uppal; Quinlyn A Soltow; Frederick H Strobel; W Stephen Pittard; Kim M Gernert; Tianwei Yu; Dean P Jones
Journal:  BMC Bioinformatics       Date:  2013-01-16       Impact factor: 3.169

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