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DIAmeter: matching peptides to data-independent acquisition mass spectrometry data
MOTIVATION: Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of pre...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8686675/ https://www.ncbi.nlm.nih.gov/pubmed/34252924 http://dx.doi.org/10.1093/bioinformatics/btab284 |
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author | Lu, Yang Young Bilmes, Jeff Rodriguez-Mias, Ricard A Villén, Judit Noble, William Stafford |
author_facet | Lu, Yang Young Bilmes, Jeff Rodriguez-Mias, Ricard A Villén, Judit Noble, William Stafford |
author_sort | Lu, Yang Young |
collection | PubMed |
description | MOTIVATION: Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a ‘spectral library’), but this approach is expensive because the libraries do not typically generalize well across laboratories. RESULTS: Here, we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Although some existing library-free DIA analysis methods (i) support data generated using both wide and narrow isolation windows, (ii) detect peptides containing post-translational modifications, (iii) analyze data from a variety of instrument platforms and (iv) are capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan, DIAmeter is the only method that offers all four capabilities in a single tool. AVAILABILITY AND IMPLEMENTATION: The open source, Apache licensed source code is available as part of the Crux mass spectrometry analysis toolkit (http://crux.ms). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-8686675 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-86866752021-12-21 DIAmeter: matching peptides to data-independent acquisition mass spectrometry data Lu, Yang Young Bilmes, Jeff Rodriguez-Mias, Ricard A Villén, Judit Noble, William Stafford Bioinformatics General Computational Biology MOTIVATION: Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a ‘spectral library’), but this approach is expensive because the libraries do not typically generalize well across laboratories. RESULTS: Here, we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Although some existing library-free DIA analysis methods (i) support data generated using both wide and narrow isolation windows, (ii) detect peptides containing post-translational modifications, (iii) analyze data from a variety of instrument platforms and (iv) are capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan, DIAmeter is the only method that offers all four capabilities in a single tool. AVAILABILITY AND IMPLEMENTATION: The open source, Apache licensed source code is available as part of the Crux mass spectrometry analysis toolkit (http://crux.ms). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-07-12 /pmc/articles/PMC8686675/ /pubmed/34252924 http://dx.doi.org/10.1093/bioinformatics/btab284 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | General Computational Biology Lu, Yang Young Bilmes, Jeff Rodriguez-Mias, Ricard A Villén, Judit Noble, William Stafford DIAmeter: matching peptides to data-independent acquisition mass spectrometry data |
title | DIAmeter: matching peptides to data-independent acquisition mass spectrometry data |
title_full | DIAmeter: matching peptides to data-independent acquisition mass spectrometry data |
title_fullStr | DIAmeter: matching peptides to data-independent acquisition mass spectrometry data |
title_full_unstemmed | DIAmeter: matching peptides to data-independent acquisition mass spectrometry data |
title_short | DIAmeter: matching peptides to data-independent acquisition mass spectrometry data |
title_sort | diameter: matching peptides to data-independent acquisition mass spectrometry data |
topic | General Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8686675/ https://www.ncbi.nlm.nih.gov/pubmed/34252924 http://dx.doi.org/10.1093/bioinformatics/btab284 |
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