Literature DB >> 34239088

MaxDIA enables library-based and library-free data-independent acquisition proteomics.

Pavel Sinitcyn1, Hamid Hamzeiy1, Favio Salinas Soto1, Daniel Itzhak2, Frank McCarthy2, Christoph Wichmann1, Martin Steger3, Uli Ohmayer3, Ute Distler4, Stephanie Kaspar-Schoenefeld5, Nikita Prianichnikov1, Şule Yılmaz1, Jan Daniel Rudolph1,6, Stefan Tenzer4, Yasset Perez-Riverol7, Nagarjuna Nagaraj5, Sean J Humphrey8, Jürgen Cox9,10.   

Abstract

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification.
© 2021. The Author(s).

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Year:  2021        PMID: 34239088      PMCID: PMC8668435          DOI: 10.1038/s41587-021-00968-7

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   68.164


  46 in total

1.  Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.

Authors:  Joshua E Elias; Steven P Gygi
Journal:  Nat Methods       Date:  2007-03       Impact factor: 28.547

2.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

Authors:  Jürgen Cox; Matthias Mann
Journal:  Nat Biotechnol       Date:  2008-11-30       Impact factor: 54.908

3.  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

4.  MaxQuant goes Linux.

Authors:  Pavel Sinitcyn; Shivani Tiwary; Jan Rudolph; Petra Gutenbrunner; Christoph Wichmann; Şule Yılmaz; Hamid Hamzeiy; Favio Salinas; Jürgen Cox
Journal:  Nat Methods       Date:  2018-06       Impact factor: 28.547

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.  20 years of Nature Biotechnology research tools.

Authors:  Anna Azvolinsky; Laura DeFrancesco; Emily Waltz; Sarah Webb
Journal:  Nat Biotechnol       Date:  2016-03       Impact factor: 54.908

7.  Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues.

Authors:  Roland Bruderer; Oliver M Bernhardt; Tejas Gandhi; Saša M Miladinović; Lin-Yang Cheng; Simon Messner; Tobias Ehrenberger; Vito Zanotelli; Yulia Butscheid; Claudia Escher; Olga Vitek; Oliver Rinner; Lukas Reiter
Journal:  Mol Cell Proteomics       Date:  2015-02-27       Impact factor: 5.911

8.  A multicenter study benchmarks software tools for label-free proteome quantification.

Authors:  Pedro Navarro; Jörg Kuharev; Ludovic C Gillet; Oliver M Bernhardt; Brendan MacLean; Hannes L Röst; Stephen A Tate; Chih-Chiang Tsou; Lukas Reiter; Ute Distler; George Rosenberger; Yasset Perez-Riverol; Alexey I Nesvizhskii; Ruedi Aebersold; Stefan Tenzer
Journal:  Nat Biotechnol       Date:  2016-10-03       Impact factor: 54.908

9.  Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.

Authors:  George Rosenberger; Isabell Bludau; Uwe Schmitt; Moritz Heusel; Christie L Hunter; Yansheng Liu; Michael J MacCoss; Brendan X MacLean; Alexey I Nesvizhskii; Patrick G A Pedrioli; Lukas Reiter; Hannes L Röst; Stephen Tate; Ying S Ting; Ben C Collins; Ruedi Aebersold
Journal:  Nat Methods       Date:  2017-08-21       Impact factor: 28.547

10.  Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.

Authors:  Jürgen Cox; Marco Y Hein; Christian A Luber; Igor Paron; Nagarjuna Nagaraj; Matthias Mann
Journal:  Mol Cell Proteomics       Date:  2014-06-17       Impact factor: 5.911

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  9 in total

Review 1.  Prediction of peptide mass spectral libraries with machine learning.

Authors:  Jürgen Cox
Journal:  Nat Biotechnol       Date:  2022-08-25       Impact factor: 68.164

2.  Proteome alterations during clonal isolation of established human pancreatic cancer cell lines.

Authors:  P Bernhard; T Feilen; M Rogg; K Fröhlich; M Cosenza-Contreras; F Hause; C Schell; O Schilling
Journal:  Cell Mol Life Sci       Date:  2022-10-22       Impact factor: 9.207

Review 3.  Shotgun Proteomics as a Powerful Tool for the Study of the Proteomes of Plants, Their Pathogens, and Plant-Pathogen Interactions.

Authors:  Sadegh Balotf; Richard Wilson; Robert S Tegg; David S Nichols; Calum R Wilson
Journal:  Proteomes       Date:  2022-01-19

Review 4.  Considerations for constructing a protein sequence database for metaproteomics.

Authors:  J Alfredo Blakeley-Ruiz; Manuel Kleiner
Journal:  Comput Struct Biotechnol J       Date:  2022-01-21       Impact factor: 7.271

Review 5.  Review of Liquid Chromatography-Mass Spectrometry-Based Proteomic Analyses of Body Fluids to Diagnose Infectious Diseases.

Authors:  Hayoung Lee; Seung Il Kim
Journal:  Int J Mol Sci       Date:  2022-02-16       Impact factor: 5.923

6.  Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity.

Authors:  Klemens Fröhlich; Eva Brombacher; Matthias Fahrner; Daniel Vogele; Lucas Kook; Niko Pinter; Peter Bronsert; Sylvia Timme-Bronsert; Alexander Schmidt; Katja Bärenfaller; Clemens Kreutz; Oliver Schilling
Journal:  Nat Commun       Date:  2022-05-12       Impact factor: 17.694

Review 7.  Biomarkers in Neurodegenerative Diseases: Proteomics Spotlight on ALS and Parkinson's Disease.

Authors:  Rekha Raghunathan; Kathleen Turajane; Li Chin Wong
Journal:  Int J Mol Sci       Date:  2022-08-18       Impact factor: 6.208

8.  The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences.

Authors:  Yasset Perez-Riverol; Jingwen Bai; Chakradhar Bandla; David García-Seisdedos; Suresh Hewapathirana; Selvakumar Kamatchinathan; Deepti J Kundu; Ananth Prakash; Anika Frericks-Zipper; Martin Eisenacher; Mathias Walzer; Shengbo Wang; Alvis Brazma; Juan Antonio Vizcaíno
Journal:  Nucleic Acids Res       Date:  2022-01-07       Impact factor: 16.971

9.  ABPP-HT*-Deep Meets Fast for Activity-Based Profiling of Deubiquitylating Enzymes Using Advanced DIA Mass Spectrometry Methods.

Authors:  Hannah B L Jones; Raphael Heilig; Simon Davis; Roman Fischer; Benedikt M Kessler; Adán Pinto-Fernández
Journal:  Int J Mol Sci       Date:  2022-03-17       Impact factor: 5.923

  9 in total

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