Literature DB >> 31981311

Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries.

Bart Van Puyvelde1, Sander Willems1, Ralf Gabriels2,3, Simon Daled1, Laura De Clerck1, Sofie Vande Casteele1, An Staes2,3,4, Francis Impens2,3,4, Dieter Deforce1, Lennart Martens2,3, Sven Degroeve2,3, Maarten Dhaenens1.   

Abstract

Data-independent acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data-dependent acquisition (DDA) libraries for deep peptide-centric detection. Here, it is shown that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter-laboratory comparison.
© 2020 The Authors. Proteomics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  bioinformatics; data-independent acquisition; label-free quantification; peptide-centric

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Substances：

Year:  2020        PMID： 31981311     DOI： 10.1002/pmic.201900306

Source DB:  PubMed          Journal:  Proteomics        ISSN： 1615-9853            Impact factor:   3.984


  9 in total

1.  Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data.

Authors:  Lilian R Heil; William E Fondrie; Christopher D McGann; Alexander J Federation; William S Noble; Michael J MacCoss; Uri Keich
Journal:  J Proteome Res       Date:  2022-05-12       Impact factor: 5.370

2.  Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas.

Authors:  Mathias Walzer; David García-Seisdedos; Ananth Prakash; Paul Brack; Peter Crowther; Robert L Graham; Nancy George; Suhaib Mohammed; Pablo Moreno; Irene Papatheodorou; Simon J Hubbard; Juan Antonio Vizcaíno
Journal:  Sci Data       Date:  2022-06-14       Impact factor: 8.501

3.  Use of Hybrid Data-Dependent and -Independent Acquisition Spectral Libraries Empowers Dual-Proteome Profiling.

Authors:  Patrick Willems; Ursula Fels; An Staes; Kris Gevaert; Petra Van Damme
Journal:  J Proteome Res       Date:  2021-01-19       Impact factor: 4.466

4.  Histone Sample Preparation for Bottom-Up Mass Spectrometry: A Roadmap to Informed Decisions.

Authors:  Simon Daled; Sander Willems; Bart Van Puyvelde; Laura Corveleyn; Sigrid Verhelst; Laura De Clerck; Dieter Deforce; Maarten Dhaenens
Journal:  Proteomes       Date:  2021-04-21

5.  A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics.

Authors:  Bart Van Puyvelde; Simon Daled; Sander Willems; Ralf Gabriels; Anne Gonzalez de Peredo; Karima Chaoui; Emmanuelle Mouton-Barbosa; David Bouyssié; Kurt Boonen; Christopher J Hughes; Lee A Gethings; Yasset Perez-Riverol; Nic Bloomfield; Stephen Tate; Odile Schiltz; Lennart Martens; Dieter Deforce; Maarten Dhaenens
Journal:  Sci Data       Date:  2022-03-30       Impact factor: 6.444

Review 6.  Deep learning neural network tools for proteomics.

Authors:  Jesse G Meyer
Journal:  Cell Rep Methods       Date:  2021-05-17

7.  CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation.

Authors:  Damien B Wilburn; Alicia L Richards; Danielle L Swaney; Brian C Searle
Journal:  J Proteome Res       Date:  2021-03-17       Impact factor: 4.466

8.  Cov-MS: A Community-Based Template Assay for Mass-Spectrometry-Based Protein Detection in SARS-CoV-2 Patients.

Authors:  Bart Van Puyvelde; Katleen Van Uytfanghe; Olivier Tytgat; Laurence Van Oudenhove; Ralf Gabriels; Robbin Bouwmeester; Simon Daled; Tim Van Den Bossche; Pathmanaban Ramasamy; Sigrid Verhelst; Laura De Clerck; Laura Corveleyn; Sander Willems; Nathan Debunne; Evelien Wynendaele; Bart De Spiegeleer; Peter Judak; Kris Roels; Laurie De Wilde; Peter Van Eenoo; Tim Reyns; Marc Cherlet; Emmie Dumont; Griet Debyser; Ruben t'Kindt; Koen Sandra; Surya Gupta; Nicolas Drouin; Amy Harms; Thomas Hankemeier; Donald J L Jones; Pankaj Gupta; Dan Lane; Catherine S Lane; Said El Ouadi; Jean-Baptiste Vincendet; Nick Morrice; Stuart Oehrle; Nikunj Tanna; Steve Silvester; Sally Hannam; Florian C Sigloch; Andrea Bhangu-Uhlmann; Jan Claereboudt; N Leigh Anderson; Morteza Razavi; Sven Degroeve; Lize Cuypers; Christophe Stove; Katrien Lagrou; Geert A Martens; Dieter Deforce; Lennart Martens; Johannes P C Vissers; Maarten Dhaenens
Journal:  JACS Au       Date:  2021-05-03

9.  [Research progress and application of retention time prediction method based on deep learning].

Authors:  Zhuokun DU; Wei Shao; Weijie Qin
Journal:  Se Pu       Date:  2021-03
  9 in total

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