LC–MS peak assignment based on unanimous selection by six machine learning algorithms

Abstract Recent mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak si...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hiroaki Ito, Takashi Matsui, Ryo Konno, Makoto Itakura, Yoshio Kodera
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c4b961b0216b4cf98d42bebbb6cc47ec
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c4b961b0216b4cf98d42bebbb6cc47ec
record_format	dspace
spelling	oai:doaj.org-article:c4b961b0216b4cf98d42bebbb6cc47ec2021-12-05T12:13:29ZLC–MS peak assignment based on unanimous selection by six machine learning algorithms10.1038/s41598-021-02899-42045-2322https://doaj.org/article/c4b961b0216b4cf98d42bebbb6cc47ec2021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02899-4https://doaj.org/toc/2045-2322Abstract Recent mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance results in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.Hiroaki ItoTakashi MatsuiRyo KonnoMakoto ItakuraYoshio KoderaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hiroaki Ito Takashi Matsui Ryo Konno Makoto Itakura Yoshio Kodera LC–MS peak assignment based on unanimous selection by six machine learning algorithms
description	Abstract Recent mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance results in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.
format	article
author	Hiroaki Ito Takashi Matsui Ryo Konno Makoto Itakura Yoshio Kodera
author_facet	Hiroaki Ito Takashi Matsui Ryo Konno Makoto Itakura Yoshio Kodera
author_sort	Hiroaki Ito
title	LC–MS peak assignment based on unanimous selection by six machine learning algorithms
title_short	LC–MS peak assignment based on unanimous selection by six machine learning algorithms
title_full	LC–MS peak assignment based on unanimous selection by six machine learning algorithms
title_fullStr	LC–MS peak assignment based on unanimous selection by six machine learning algorithms
title_full_unstemmed	LC–MS peak assignment based on unanimous selection by six machine learning algorithms
title_sort	lc–ms peak assignment based on unanimous selection by six machine learning algorithms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c4b961b0216b4cf98d42bebbb6cc47ec
work_keys_str_mv	AT hiroakiito lcmspeakassignmentbasedonunanimousselectionbysixmachinelearningalgorithms AT takashimatsui lcmspeakassignmentbasedonunanimousselectionbysixmachinelearningalgorithms AT ryokonno lcmspeakassignmentbasedonunanimousselectionbysixmachinelearningalgorithms AT makotoitakura lcmspeakassignmentbasedonunanimousselectionbysixmachinelearningalgorithms AT yoshiokodera lcmspeakassignmentbasedonunanimousselectionbysixmachinelearningalgorithms
_version_	1718372158544019456

LC–MS peak assignment based on unanimous selection by six machine learning algorithms

Ejemplares similares