Literature DB >> 23303751

Automatic recalibration and processing of tandem mass spectra using formula annotation.

Michael A Stravs1, Emma L Schymanski, Heinz P Singer, Juliane Hollender.   

Abstract

High accuracy, high resolution tandem mass spectrometry (MS/MS) is becoming more common in analytical applications, yet databases of these spectra remain limited. Databases require good quality spectra with sufficient compound information, but processing, calibration, noise reduction and retrieval of compound information are time-consuming tasks that prevent many contributions. We present a comprehensive workflow for the automatic processing of MS/MS using formula annotation for recalibration and cleanup to generate high quality spectra of standard compounds for upload to MassBank (www.massbank.jp). Compound information is retrieved via Internet services. Reference standards of 70 pesticides were measured at various collision energies on an LTQ-Orbitrap XL to develop and evaluate the workflow. A total of 944 resulting spectra are now available on MassBank. Evidence of nitrogen adduct formation during MS/MS fragmentation processes was found, highlighting the benefits high accuracy MS/MS offers for spectral interpretation. A database of recalibrated, cleaned-up spectra resulted in the most correct spectra ranked in first place, regardless of whether the search spectra were recalibrated or not, whereas the average rank of the correct molecular formula was improved from 2.55 (uncalibrated) to 1.53 when using recalibrated MS/MS data. The workflow is available as an R package RMassBank capable of generating MassBank records from raw MS and MS/MS data and can be adjusted to process data acquired with different settings and instruments. This workflow is a vital step towards addressing the need for more high quality, high accuracy MS/MS spectra in spectral databases and provides important information for spectral interpretation.
Copyright © 2012 John Wiley & Sons, Ltd.

Entities:  

Year:  2013        PMID: 23303751     DOI: 10.1002/jms.3131

Source DB:  PubMed          Journal:  J Mass Spectrom        ISSN: 1076-5174            Impact factor:   1.982


  23 in total

1.  Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics.

Authors:  Arpana Vaniya; Oliver Fiehn
Journal:  Trends Analyt Chem       Date:  2015-06-01       Impact factor: 12.296

2.  Winners of CASMI2013: Automated Tools and Challenge Data.

Authors:  Takaaki Nishioka; Takeshi Kasama; Tomoya Kinumi; Hidefumi Makabe; Fumio Matsuda; Daisuke Miura; Masahiro Miyashita; Takemichi Nakamura; Ken Tanaka; Atsushi Yamamoto
Journal:  Mass Spectrom (Tokyo)       Date:  2014-09-02

3.  Similarity of High-Resolution Tandem Mass Spectrometry Spectra of Structurally Related Micropollutants and Transformation Products.

Authors:  Jennifer E Schollée; Emma L Schymanski; Michael A Stravs; Rebekka Gulde; Nikolaos S Thomaidis; Juliane Hollender
Journal:  J Am Soc Mass Spectrom       Date:  2017-09-26       Impact factor: 3.109

4.  In Silico Collision Cross Section Calculations to Aid Metabolite Annotation.

Authors:  Susanta Das; Kiyoto Aramis Tanemura; Laleh Dinpazhoh; Mithony Keng; Christina Schumm; Lydia Leahy; Carter K Asef; Markace Rainey; Arthur S Edison; Facundo M Fernández; Kenneth M Merz
Journal:  J Am Soc Mass Spectrom       Date:  2022-04-04       Impact factor: 3.262

Review 5.  Identification of small molecules using accurate mass MS/MS search.

Authors:  Tobias Kind; Hiroshi Tsugawa; Tomas Cajka; Yan Ma; Zijuan Lai; Sajjan S Mehta; Gert Wohlgemuth; Dinesh Kumar Barupal; Megan R Showalter; Masanori Arita; Oliver Fiehn
Journal:  Mass Spectrom Rev       Date:  2017-04-24       Impact factor: 10.946

6.  Topic modeling for untargeted substructure exploration in metabolomics.

Authors:  Justin Johan Jozias van der Hooft; Joe Wandy; Michael P Barrett; Karl E V Burgess; Simon Rogers
Journal:  Proc Natl Acad Sci U S A       Date:  2016-11-16       Impact factor: 11.205

7.  Reproducible molecular networking of untargeted mass spectrometry data using GNPS.

Authors:  Allegra T Aron; Emily C Gentry; Kerry L McPhail; Louis-Félix Nothias; Mélissa Nothias-Esposito; Amina Bouslimani; Daniel Petras; Julia M Gauglitz; Nicole Sikora; Fernando Vargas; Justin J J van der Hooft; Madeleine Ernst; Kyo Bin Kang; Christine M Aceves; Andrés Mauricio Caraballo-Rodríguez; Irina Koester; Kelly C Weldon; Samuel Bertrand; Catherine Roullier; Kunyang Sun; Richard M Tehan; Cristopher A Boya P; Martin H Christian; Marcelino Gutiérrez; Aldo Moreno Ulloa; Javier Andres Tejeda Mora; Randy Mojica-Flores; Johant Lakey-Beitia; Victor Vásquez-Chaves; Yilue Zhang; Angela I Calderón; Nicole Tayler; Robert A Keyzers; Fidele Tugizimana; Nombuso Ndlovu; Alexander A Aksenov; Alan K Jarmusch; Robin Schmid; Andrew W Truman; Nuno Bandeira; Mingxun Wang; Pieter C Dorrestein
Journal:  Nat Protoc       Date:  2020-05-13       Impact factor: 17.021

8.  Molecular Formula Identification with SIRIUS.

Authors:  Kai Dührkop; Kerstin Scheubert; Sebastian Böcker
Journal:  Metabolites       Date:  2013-06-13

9.  SPLASH, a hashed identifier for mass spectra.

Authors:  Gert Wohlgemuth; Sajjan S Mehta; Ramon F Mejia; Steffen Neumann; Diego Pedrosa; Tomáš Pluskal; Emma L Schymanski; Egon L Willighagen; Michael Wilson; David S Wishart; Masanori Arita; Pieter C Dorrestein; Nuno Bandeira; Mingxun Wang; Tobias Schulze; Reza M Salek; Christoph Steinbeck; Venkata Chandrasekhar Nainala; Robert Mistrik; Takaaki Nishioka; Oliver Fiehn
Journal:  Nat Biotechnol       Date:  2016-11-08       Impact factor: 54.908

Review 10.  Data standards can boost metabolomics research, and if there is a will, there is a way.

Authors:  Philippe Rocca-Serra; Reza M Salek; Masanori Arita; Elon Correa; Saravanan Dayalan; Alejandra Gonzalez-Beltran; Tim Ebbels; Royston Goodacre; Janna Hastings; Kenneth Haug; Albert Koulman; Macha Nikolski; Matej Oresic; Susanna-Assunta Sansone; Daniel Schober; James Smith; Christoph Steinbeck; Mark R Viant; Steffen Neumann
Journal:  Metabolomics       Date:  2015-11-17       Impact factor: 4.290

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