Literature DB >> 22535207

A computational tool to detect and avoid redundancy in selected reaction monitoring.

Hannes Röst1, Lars Malmström, Ruedi Aebersold.   

Abstract

Selected reaction monitoring (SRM), also called multiple reaction monitoring, has become an invaluable tool for targeted quantitative proteomic analyses, but its application can be compromised by nonoptimal selection of transitions. In particular, complex backgrounds may cause ambiguities in SRM measurement results because peptides with interfering transitions similar to those of the target peptide may be present in the sample. Here, we developed a computer program, the SRMCollider, that calculates nonredundant theoretical SRM assays, also known as unique ion signatures (UIS), for a given proteomic background. We show theoretically that UIS of three transitions suffice to conclusively identify 90% of all yeast peptides and 85% of all human peptides. Using predicted retention times, the SRMCollider also simulates time-scheduled SRM acquisition, which reduces the number of interferences to consider and leads to fewer transitions necessary to construct an assay. By integrating experimental fragment ion intensities from large scale proteome synthesis efforts (SRMAtlas) with the information content-based UIS, we combine two orthogonal approaches to create high quality SRM assays ready to be deployed. We provide a user friendly, open source implementation of an algorithm to calculate UIS of any order that can be accessed online at http://www.srmcollider.org to find interfering transitions. Finally, our tool can also simulate the specificity of novel data-independent MS acquisition methods in Q1-Q3 space. This allows us to predict parameters for these methods that deliver a specificity comparable with that of SRM. Using SRM interference information in addition to other sources of information can increase the confidence in an SRM measurement. We expect that the consideration of information content will become a standard step in SRM assay design and analysis, facilitated by the SRMCollider.

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Year:  2012        PMID: 22535207      PMCID: PMC3412981          DOI: 10.1074/mcp.M111.013045

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  40 in total

1.  How specific is my SRM?: The issue of precursor and product ion redundancy.

Authors:  Jamie Sherman; Matthew J McKay; Keith Ashman; Mark P Molloy
Journal:  Proteomics       Date:  2009-03       Impact factor: 3.984

2.  MRMaid, the web-based tool for designing multiple reaction monitoring (MRM) transitions.

Authors:  Jennifer A Mead; Luca Bianco; Vanessa Ottone; Chris Barton; Richard G Kay; Kathryn S Lilley; Nicholas J Bond; Conrad Bessant
Journal:  Mol Cell Proteomics       Date:  2008-11-15       Impact factor: 5.911

3.  Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast.

Authors:  Lyris M F de Godoy; Jesper V Olsen; Jürgen Cox; Michael L Nielsen; Nina C Hubner; Florian Fröhlich; Tobias C Walther; Matthias Mann
Journal:  Nature       Date:  2008-09-28       Impact factor: 49.962

Review 4.  PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows.

Authors:  Eric W Deutsch; Henry Lam; Ruedi Aebersold
Journal:  EMBO Rep       Date:  2008-05       Impact factor: 8.807

5.  Expediting the development of targeted SRM assays: using data from shotgun proteomics to automate method development.

Authors:  Amol Prakash; Daniela M Tomazela; Barbara Frewen; Brendan Maclean; Gennifer Merrihew; Scott Peterman; Michael J Maccoss
Journal:  J Proteome Res       Date:  2009-06       Impact factor: 4.466

6.  Implementation of a data repository-driven approach for targeted proteomics experiments by multiple reaction monitoring.

Authors:  Geraldine M Walsh; Shujun Lin; Daniel M Evans; Arash Khosrovi-Eghbal; Ronald C Beavis; Juergen Kast
Journal:  J Proteomics       Date:  2008-12-06       Impact factor: 4.044

7.  A database of mass spectrometric assays for the yeast proteome.

Authors:  Paola Picotti; Henry Lam; David Campbell; Eric W Deutsch; Hamid Mirzaei; Jeff Ranish; Bruno Domon; Ruedi Aebersold
Journal:  Nat Methods       Date:  2008-11       Impact factor: 28.547

8.  Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics.

Authors:  Nico Pfeifer; Andreas Leinenbach; Christian G Huber; Oliver Kohlbacher
Journal:  BMC Bioinformatics       Date:  2007-11-30       Impact factor: 3.169

9.  MRMer, an interactive open source and cross-platform system for data extraction and visualization of multiple reaction monitoring experiments.

Authors:  Daniel B Martin; Ted Holzman; Damon May; Amelia Peterson; Ashley Eastham; Jimmy Eng; Martin McIntosh
Journal:  Mol Cell Proteomics       Date:  2008-07-18       Impact factor: 5.911

10.  Selected reaction monitoring for quantitative proteomics: a tutorial.

Authors:  Vinzenz Lange; Paola Picotti; Bruno Domon; Ruedi Aebersold
Journal:  Mol Syst Biol       Date:  2008-10-14       Impact factor: 11.429

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  31 in total

1.  Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.

Authors:  Ludovic C Gillet; Pedro Navarro; Stephen Tate; Hannes Röst; Nathalie Selevsek; Lukas Reiter; Ron Bonner; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2012-01-18       Impact factor: 5.911

2.  Building high-quality assay libraries for targeted analysis of SWATH MS data.

Authors:  Olga T Schubert; Ludovic C Gillet; Ben C Collins; Pedro Navarro; George Rosenberger; Witold E Wolski; Henry Lam; Dario Amodei; Parag Mallick; Brendan MacLean; Ruedi Aebersold
Journal:  Nat Protoc       Date:  2015-02-12       Impact factor: 13.491

3.  Automated Validation of Results and Removal of Fragment Ion Interferences in Targeted Analysis of Data-independent Acquisition Mass Spectrometry (MS) using SWATHProphet.

Authors:  Andrew Keller; Samuel L Bader; David Shteynberg; Leroy Hood; Robert L Moritz
Journal:  Mol Cell Proteomics       Date:  2015-02-24       Impact factor: 5.911

4.  A framework for installable external tools in Skyline.

Authors:  Daniel Broudy; Trevor Killeen; Meena Choi; Nicholas Shulman; Deepak R Mani; Susan E Abbatiello; Deepak Mani; Rushdy Ahmad; Alexandria K Sahu; Birgit Schilling; Kaipo Tamura; Yuval Boss; Vagisha Sharma; Bradford W Gibson; Steven A Carr; Olga Vitek; Michael J MacCoss; Brendan MacLean
Journal:  Bioinformatics       Date:  2014-05-09       Impact factor: 6.937

5.  OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

Authors:  Hannes L Röst; George Rosenberger; Pedro Navarro; Ludovic Gillet; Saša M Miladinović; Olga T Schubert; Witold Wolski; Ben C Collins; Johan Malmström; Lars Malmström; Ruedi Aebersold
Journal:  Nat Biotechnol       Date:  2014-03       Impact factor: 54.908

6.  Quantification of Inflammasome Adaptor Protein ASC in Biological Samples by Multiple-Reaction Monitoring Mass Spectrometry.

Authors:  Annegret Ulke-Lemée; Arthur Lau; Michelle C Nelson; Matthew T James; Daniel A Muruve; Justin A MacDonald
Journal:  Inflammation       Date:  2018-08       Impact factor: 4.092

7.  Targeted proteomics: a bridge between discovery and validation.

Authors:  Robert Harlan; Hui Zhang
Journal:  Expert Rev Proteomics       Date:  2014-10-28       Impact factor: 3.940

8.  Levels of the retinoic acid synthesizing enzyme aldehyde dehydrogenase-1A2 are lower in testicular tissue from men with infertility.

Authors:  John K Amory; Samuel Arnold; María C Lardone; Antonio Piottante; Mauricio Ebensperger; Nina Isoherranen; Charles H Muller; Thomas Walsh; Andrea Castro
Journal:  Fertil Steril       Date:  2014-02-10       Impact factor: 7.329

Review 9.  Review of software tools for design and analysis of large scale MRM proteomic datasets.

Authors:  Christopher M Colangelo; Lisa Chung; Can Bruce; Kei-Hoi Cheung
Journal:  Methods       Date:  2013-05-21       Impact factor: 3.608

10.  Detection and correction of interference in SRM analysis.

Authors:  Y Bao; S Waldemarson; G Zhang; A Wahlander; B Ueberheide; S Myung; B Reed; K Molloy; J C Padovan; J Eriksson; T A Neubert; B T Chait; D Fenyö
Journal:  Methods       Date:  2013-05-23       Impact factor: 3.608

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