Literature DB >> 24813211

A framework for installable external tools in Skyline.

Daniel Broudy1, Trevor Killeen1, Meena Choi1, Nicholas Shulman1, Deepak R Mani1, Susan E Abbatiello1, Deepak Mani1, Rushdy Ahmad1, Alexandria K Sahu1, Birgit Schilling1, Kaipo Tamura1, Yuval Boss1, Vagisha Sharma1, Bradford W Gibson1, Steven A Carr1, Olga Vitek1, Michael J MacCoss1, Brendan MacLean1.   

Abstract

UNLABELLED: Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. The Skyline document model contains extensive mass spectrometry data from targeted proteomics experiments performed using selected reaction monitoring, parallel reaction monitoring and data-independent and data-dependent acquisition methods. Researchers have developed software tools that perform statistical analysis of the experimental data contained within Skyline documents. The new external tools framework allows researchers to integrate their tools into Skyline without modifying the Skyline codebase. Installed tools provide point-and-click access to downstream statistical analysis of data processed in Skyline. The framework also specifies a uniform interface to format tools for installation into Skyline. Tool developers can now easily share their tools with proteomics researchers using Skyline.
AVAILABILITY AND IMPLEMENTATION: Skyline is available as a single-click self-updating web installation at http://skyline.maccosslab.org. This Web site also provides access to installable external tools and documentation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Mesh:

Year:  2014        PMID: 24813211      PMCID: PMC4147880          DOI: 10.1093/bioinformatics/btu148

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


  3 in total

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

Authors:  Hannes Röst; Lars Malmström; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2012-04-24       Impact factor: 5.911

2.  Skyline: an open source document editor for creating and analyzing targeted proteomics experiments.

Authors:  Brendan MacLean; Daniela M Tomazela; Nicholas Shulman; Matthew Chambers; Gregory L Finney; Barbara Frewen; Randall Kern; David L Tabb; Daniel C Liebler; Michael J MacCoss
Journal:  Bioinformatics       Date:  2010-02-09       Impact factor: 6.937

3.  MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments.

Authors:  Meena Choi; Ching-Yun Chang; Timothy Clough; Daniel Broudy; Trevor Killeen; Brendan MacLean; Olga Vitek
Journal:  Bioinformatics       Date:  2014-05-02       Impact factor: 6.937

  3 in total
  14 in total

1.  Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics.

Authors:  Kendra J Adams; Brian Pratt; Neelanjan Bose; Laura G Dubois; Lisa St John-Williams; Kevin M Perrott; Karina Ky; Pankaj Kapahi; Vagisha Sharma; Michael J MacCoss; M Arthur Moseley; Carol A Colton; Brendan X MacLean; Birgit Schilling; J Will Thompson
Journal:  J Proteome Res       Date:  2020-03-26       Impact factor: 4.466

2.  Domain-specific Quantification of Prion Protein in Cerebrospinal Fluid by Targeted Mass Spectrometry.

Authors:  Eric Vallabh Minikel; Eric Kuhn; Alexandra R Cocco; Sonia M Vallabh; Christina R Hartigan; Andrew G Reidenbach; Jiri G Safar; Gregory J Raymond; Michael D McCarthy; Rhonda O'Keefe; Franc Llorens; Inga Zerr; Sabina Capellari; Piero Parchi; Stuart L Schreiber; Steven A Carr
Journal:  Mol Cell Proteomics       Date:  2019-09-26       Impact factor: 5.911

3.  ProteoSign: an end-user online differential proteomics statistical analysis platform.

Authors:  Georgios Efstathiou; Andreas N Antonakis; Georgios A Pavlopoulos; Theodosios Theodosiou; Peter Divanach; David C Trudgian; Benjamin Thomas; Nikolas Papanikolaou; Michalis Aivaliotis; Oreste Acuto; Ioannis Iliopoulos
Journal:  Nucleic Acids Res       Date:  2017-07-03       Impact factor: 16.971

Review 4.  The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics.

Authors:  Lindsay K Pino; Brian C Searle; James G Bollinger; Brook Nunn; Brendan MacLean; Michael J MacCoss
Journal:  Mass Spectrom Rev       Date:  2017-07-09       Impact factor: 10.946

5.  An Automated Pipeline to Monitor System Performance in Liquid Chromatography-Tandem Mass Spectrometry Proteomic Experiments.

Authors:  Michael S Bereman; Joshua Beri; Vagisha Sharma; Cory Nathe; Josh Eckels; Brendan MacLean; Michael J MacCoss
Journal:  J Proteome Res       Date:  2016-10-04       Impact factor: 4.466

6.  Data-independent-acquisition mass spectrometry for identification of targeted-peptide site-specific modifications.

Authors:  Caleb J Porter; Michael S Bereman
Journal:  Anal Bioanal Chem       Date:  2015-06-24       Impact factor: 4.142

Review 7.  Algorithms and design strategies towards automated glycoproteomics analysis.

Authors:  Han Hu; Kshitij Khatri; Joseph Zaia
Journal:  Mass Spectrom Rev       Date:  2016-01-04       Impact factor: 10.946

Review 8.  Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques.

Authors:  Jesse G Meyer; Birgit Schilling
Journal:  Expert Rev Proteomics       Date:  2017-05       Impact factor: 3.940

9.  Implementation of statistical process control for proteomic experiments via LC MS/MS.

Authors:  Michael S Bereman; Richard Johnson; James Bollinger; Yuval Boss; Nick Shulman; Brendan MacLean; Andrew N Hoofnagle; Michael J MacCoss
Journal:  J Am Soc Mass Spectrom       Date:  2014-02-05       Impact factor: 3.109

10.  Protein acetylation dynamics in response to carbon overflow in Escherichia coli.

Authors:  Birgit Schilling; David Christensen; Robert Davis; Alexandria K Sahu; Linda I Hu; Arti Walker-Peddakotla; Dylan J Sorensen; Bozena Zemaitaitis; Bradford W Gibson; Alan J Wolfe
Journal:  Mol Microbiol       Date:  2015-09-10       Impact factor: 3.501

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