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SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions

Historically, the database search algorithms have been the de facto standard for inferring peptides from mass spectrometry (MS) data. Database search algorithms deduce peptides by transforming theoretical peptides into theoretical spectra and matching them to the experimental spectra. Heuristic simi...

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Autores principales: Tariq, Muhammad Usman, Saeed, Fahad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555789/
https://www.ncbi.nlm.nih.gov/pubmed/34714871
http://dx.doi.org/10.1371/journal.pone.0259349
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author Tariq, Muhammad Usman
Saeed, Fahad
author_facet Tariq, Muhammad Usman
Saeed, Fahad
author_sort Tariq, Muhammad Usman
collection PubMed
description Historically, the database search algorithms have been the de facto standard for inferring peptides from mass spectrometry (MS) data. Database search algorithms deduce peptides by transforming theoretical peptides into theoretical spectra and matching them to the experimental spectra. Heuristic similarity-scoring functions are used to match an experimental spectrum to a theoretical spectrum. However, the heuristic nature of the scoring functions and the simple transformation of the peptides into theoretical spectra, along with noisy mass spectra for the less abundant peptides, can introduce a cascade of inaccuracies. In this paper, we design and implement a Deep Cross-Modal Similarity Network called SpeCollate, which overcomes these inaccuracies by learning the similarity function between experimental spectra and peptides directly from the labeled MS data. SpeCollate transforms spectra and peptides into a shared Euclidean subspace by learning fixed size embeddings for both. Our proposed deep-learning network trains on sextuplets of positive and negative examples coupled with our custom-designed SNAP-loss function. Online hardest negative mining is used to select the appropriate negative examples for optimal training performance. We use 4.8 million sextuplets obtained from the NIST and MassIVE peptide libraries to train the network and demonstrate that for closed search, SpeCollate is able to perform better than Crux and MSFragger in terms of the number of peptide-spectrum matches (PSMs) and unique peptides identified under 1% FDR for real-world data. SpeCollate also identifies a large number of peptides not reported by either Crux or MSFragger. To the best of our knowledge, our proposed SpeCollate is the first deep-learning network that can determine the cross-modal similarity between peptides and mass-spectra for MS-based proteomics. We believe SpeCollate is significant progress towards developing machine-learning solutions for MS-based omics data analysis. SpeCollate is available at https://deepspecs.github.io/.
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spelling pubmed-85557892021-10-30 SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions Tariq, Muhammad Usman Saeed, Fahad PLoS One Research Article Historically, the database search algorithms have been the de facto standard for inferring peptides from mass spectrometry (MS) data. Database search algorithms deduce peptides by transforming theoretical peptides into theoretical spectra and matching them to the experimental spectra. Heuristic similarity-scoring functions are used to match an experimental spectrum to a theoretical spectrum. However, the heuristic nature of the scoring functions and the simple transformation of the peptides into theoretical spectra, along with noisy mass spectra for the less abundant peptides, can introduce a cascade of inaccuracies. In this paper, we design and implement a Deep Cross-Modal Similarity Network called SpeCollate, which overcomes these inaccuracies by learning the similarity function between experimental spectra and peptides directly from the labeled MS data. SpeCollate transforms spectra and peptides into a shared Euclidean subspace by learning fixed size embeddings for both. Our proposed deep-learning network trains on sextuplets of positive and negative examples coupled with our custom-designed SNAP-loss function. Online hardest negative mining is used to select the appropriate negative examples for optimal training performance. We use 4.8 million sextuplets obtained from the NIST and MassIVE peptide libraries to train the network and demonstrate that for closed search, SpeCollate is able to perform better than Crux and MSFragger in terms of the number of peptide-spectrum matches (PSMs) and unique peptides identified under 1% FDR for real-world data. SpeCollate also identifies a large number of peptides not reported by either Crux or MSFragger. To the best of our knowledge, our proposed SpeCollate is the first deep-learning network that can determine the cross-modal similarity between peptides and mass-spectra for MS-based proteomics. We believe SpeCollate is significant progress towards developing machine-learning solutions for MS-based omics data analysis. SpeCollate is available at https://deepspecs.github.io/. Public Library of Science 2021-10-29 /pmc/articles/PMC8555789/ /pubmed/34714871 http://dx.doi.org/10.1371/journal.pone.0259349 Text en © 2021 Tariq, Saeed https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tariq, Muhammad Usman
Saeed, Fahad
SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions
title SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions
title_full SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions
title_fullStr SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions
title_full_unstemmed SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions
title_short SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions
title_sort specollate: deep cross-modal similarity network for mass spectrometry data based peptide deductions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555789/
https://www.ncbi.nlm.nih.gov/pubmed/34714871
http://dx.doi.org/10.1371/journal.pone.0259349
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