Cargando…

MS1Connect: a mass spectrometry run similarity measure

MOTIVATION: Interpretation of newly acquired mass spectrometry data can be improved by identifying, from an online repository, previous mass spectrometry runs that resemble the new data. However, this retrieval task requires computing the similarity between an arbitrary pair of mass spectrometry run...

Descripción completa

Detalles Bibliográficos
Autores principales: Lin, Andy, Deatherage Kaiser, Brooke L, Hutchison, Janine R, Bilmes, Jeffrey A, Noble, William Stafford
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913042/
https://www.ncbi.nlm.nih.gov/pubmed/36702456
http://dx.doi.org/10.1093/bioinformatics/btad058
_version_ 1784885332218478592
author Lin, Andy
Deatherage Kaiser, Brooke L
Hutchison, Janine R
Bilmes, Jeffrey A
Noble, William Stafford
author_facet Lin, Andy
Deatherage Kaiser, Brooke L
Hutchison, Janine R
Bilmes, Jeffrey A
Noble, William Stafford
author_sort Lin, Andy
collection PubMed
description MOTIVATION: Interpretation of newly acquired mass spectrometry data can be improved by identifying, from an online repository, previous mass spectrometry runs that resemble the new data. However, this retrieval task requires computing the similarity between an arbitrary pair of mass spectrometry runs. This is particularly challenging for runs acquired using different experimental protocols. RESULTS: We propose a method, MS1Connect, that calculates the similarity between a pair of runs by examining only the intact peptide (MS1) scans, and we show evidence that the MS1Connect score is accurate. Specifically, we show that MS1Connect outperforms several baseline methods on the task of predicting the species from which a given proteomics sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities computed from fragment (MS2) scans, even though these data are not used by MS1Connect. AVAILABILITY AND IMPLEMENTATION: The MS1Connect software is available at https://github.com/bmx8177/MS1Connect. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
format Online
Article
Text
id pubmed-9913042
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-99130422023-02-13 MS1Connect: a mass spectrometry run similarity measure Lin, Andy Deatherage Kaiser, Brooke L Hutchison, Janine R Bilmes, Jeffrey A Noble, William Stafford Bioinformatics Original Paper MOTIVATION: Interpretation of newly acquired mass spectrometry data can be improved by identifying, from an online repository, previous mass spectrometry runs that resemble the new data. However, this retrieval task requires computing the similarity between an arbitrary pair of mass spectrometry runs. This is particularly challenging for runs acquired using different experimental protocols. RESULTS: We propose a method, MS1Connect, that calculates the similarity between a pair of runs by examining only the intact peptide (MS1) scans, and we show evidence that the MS1Connect score is accurate. Specifically, we show that MS1Connect outperforms several baseline methods on the task of predicting the species from which a given proteomics sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities computed from fragment (MS2) scans, even though these data are not used by MS1Connect. AVAILABILITY AND IMPLEMENTATION: The MS1Connect software is available at https://github.com/bmx8177/MS1Connect. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2023-01-25 /pmc/articles/PMC9913042/ /pubmed/36702456 http://dx.doi.org/10.1093/bioinformatics/btad058 Text en © The Author(s) 2023. 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 (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 Original Paper
Lin, Andy
Deatherage Kaiser, Brooke L
Hutchison, Janine R
Bilmes, Jeffrey A
Noble, William Stafford
MS1Connect: a mass spectrometry run similarity measure
title MS1Connect: a mass spectrometry run similarity measure
title_full MS1Connect: a mass spectrometry run similarity measure
title_fullStr MS1Connect: a mass spectrometry run similarity measure
title_full_unstemmed MS1Connect: a mass spectrometry run similarity measure
title_short MS1Connect: a mass spectrometry run similarity measure
title_sort ms1connect: a mass spectrometry run similarity measure
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913042/
https://www.ncbi.nlm.nih.gov/pubmed/36702456
http://dx.doi.org/10.1093/bioinformatics/btad058
work_keys_str_mv AT linandy ms1connectamassspectrometryrunsimilaritymeasure
AT deatheragekaiserbrookel ms1connectamassspectrometryrunsimilaritymeasure
AT hutchisonjaniner ms1connectamassspectrometryrunsimilaritymeasure
AT bilmesjeffreya ms1connectamassspectrometryrunsimilaritymeasure
AT noblewilliamstafford ms1connectamassspectrometryrunsimilaritymeasure