Literature DB >> 25631240

Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics.

Eric W Deutsch1, Luis Mendoza1, David Shteynberg1, Joseph Slagel1, Zhi Sun1, Robert L Moritz1.   

Abstract

Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include MS to define protein sequence, protein:protein interactions, and protein PTMs. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans-Proteomics Pipeline (TPP) is a robust open-source standardized data processing pipeline for large-scale reproducible quantitative MS proteomics. It supports all major operating systems and instrument vendors via open data formats. Here, we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of MS/MS datasets, as well as some major upcoming features.
© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Bioinformatics; Mass spectrometry

Mesh:

Substances:

Year:  2015        PMID: 25631240      PMCID: PMC4506239          DOI: 10.1002/prca.201400164

Source DB:  PubMed          Journal:  Proteomics Clin Appl        ISSN: 1862-8346            Impact factor:   3.494


  52 in total

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Journal:  Bioinformatics       Date:  2004-02-19       Impact factor: 6.937

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Review 3.  A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies.

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4.  General framework for developing and evaluating database scoring algorithms using the TANDEM search engine.

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5.  Automated identification of SUMOylation sites using mass spectrometry and SUMmOn pattern recognition software.

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Journal:  Nat Methods       Date:  2006-07       Impact factor: 28.547

6.  A cross-platform toolkit for mass spectrometry and proteomics.

Authors:  Matthew C Chambers; Brendan Maclean; Robert Burke; Dario Amodei; Daniel L Ruderman; Steffen Neumann; Laurent Gatto; Bernd Fischer; Brian Pratt; Jarrett Egertson; Katherine Hoff; Darren Kessner; Natalie Tasman; Nicholas Shulman; Barbara Frewen; Tahmina A Baker; Mi-Youn Brusniak; Christopher Paulse; David Creasy; Lisa Flashner; Kian Kani; Chris Moulding; Sean L Seymour; Lydia M Nuwaysir; Brent Lefebvre; Frank Kuhlmann; Joe Roark; Paape Rainer; Suckau Detlev; Tina Hemenway; Andreas Huhmer; James Langridge; Brian Connolly; Trey Chadick; Krisztina Holly; Josh Eckels; Eric W Deutsch; Robert L Moritz; Jonathan E Katz; David B Agus; Michael MacCoss; David L Tabb; Parag Mallick
Journal:  Nat Biotechnol       Date:  2012-10       Impact factor: 54.908

7.  The mzIdentML data standard for mass spectrometry-based proteomics results.

Authors:  Andrew R Jones; Martin Eisenacher; Gerhard Mayer; Oliver Kohlbacher; Jennifer Siepen; Simon J Hubbard; Julian N Selley; Brian C Searle; James Shofstahl; Sean L Seymour; Randall Julian; Pierre-Alain Binz; Eric W Deutsch; Henning Hermjakob; Florian Reisinger; Johannes Griss; Juan Antonio Vizcaíno; Matthew Chambers; Angel Pizarro; David Creasy
Journal:  Mol Cell Proteomics       Date:  2012-02-27       Impact factor: 5.911

8.  SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology.

Authors:  Bruz Marzolf; Eric W Deutsch; Patrick Moss; David Campbell; Michael H Johnson; Timothy Galitski
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Journal:  Mol Syst Biol       Date:  2005-08-02       Impact factor: 11.429

10.  File formats commonly used in mass spectrometry proteomics.

Authors:  Eric W Deutsch
Journal:  Mol Cell Proteomics       Date:  2012-09-06       Impact factor: 5.911

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  127 in total

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Authors:  Laura M Palma Medina; Ann-Kristin Becker; Stephan Michalik; Harita Yedavally; Elisa J M Raineri; Petra Hildebrandt; Manuela Gesell Salazar; Kristin Surmann; Henrike Pförtner; Solomon A Mekonnen; Anna Salvati; Lars Kaderali; Jan Maarten van Dijl; Uwe Völker
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Review 2.  Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry.

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3.  StPeter: Seamless Label-Free Quantification with the Trans-Proteomic Pipeline.

Authors:  Michael R Hoopmann; Jason M Winget; Luis Mendoza; Robert L Moritz
Journal:  J Proteome Res       Date:  2018-02-14       Impact factor: 4.466

4.  Flexible and Fast Mapping of Peptides to a Proteome with ProteoMapper.

Authors:  Luis Mendoza; Eric W Deutsch; Zhi Sun; David S Campbell; David D Shteynberg; Robert L Moritz
Journal:  J Proteome Res       Date:  2018-09-28       Impact factor: 4.466

5.  An integrated transcriptomics-guided genome-wide promoter analysis and next-generation proteomics approach to mine factor(s) regulating cellular differentiation.

Authors:  Kamal Mandal; Samuel L Bader; Pankaj Kumar; Dipankar Malakar; David S Campbell; Bhola Shankar Pradhan; Rajesh K Sarkar; Neerja Wadhwa; Souvik Sensharma; Vaibhav Jain; Robert L Moritz; Subeer S Majumdar
Journal:  DNA Res       Date:  2017-04-01       Impact factor: 4.458

6.  PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline.

Authors:  David D Shteynberg; Eric W Deutsch; David S Campbell; Michael R Hoopmann; Ulrike Kusebauch; Dave Lee; Luis Mendoza; Mukul K Midha; Zhi Sun; Anthony D Whetton; Robert L Moritz
Journal:  J Proteome Res       Date:  2019-07-22       Impact factor: 4.466

7.  Mass Spectrometry Analysis of Lysine Posttranslational Modifications of Tau Protein from Alzheimer's Disease Brain.

Authors:  Stefani N Thomas; Austin J Yang
Journal:  Methods Mol Biol       Date:  2017

8.  Chromosome 17 Missing Proteins: Recent Progress and Future Directions as Part of the neXt-MP50 Challenge.

Authors:  Omer Siddiqui; Hongjiu Zhang; Yuanfang Guan; Gilbert S Omenn
Journal:  J Proteome Res       Date:  2018-10-23       Impact factor: 4.466

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.  An Open Data Format for Visualization and Analysis of Cross-Linked Mass Spectrometry Results.

Authors:  Michael R Hoopmann; Luis Mendoza; Eric W Deutsch; David Shteynberg; Robert L Moritz
Journal:  J Am Soc Mass Spectrom       Date:  2016-07-28       Impact factor: 3.109

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