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SMITER—A Python Library for the Simulation of LC-MS/MS Experiments
SMITER (Synthetic mzML writer) is a Python-based command-line tool designed to simulate liquid-chromatography-coupled tandem mass spectrometry LC-MS/MS runs. It enables the simulation of any biomolecule amenable to mass spectrometry (MS) since all calculations are based on chemical formulas. SMITER...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8000309/ https://www.ncbi.nlm.nih.gov/pubmed/33799543 http://dx.doi.org/10.3390/genes12030396 |
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author | Kösters, Manuel Leufken, Johannes Leidel, Sebastian A. |
author_facet | Kösters, Manuel Leufken, Johannes Leidel, Sebastian A. |
author_sort | Kösters, Manuel |
collection | PubMed |
description | SMITER (Synthetic mzML writer) is a Python-based command-line tool designed to simulate liquid-chromatography-coupled tandem mass spectrometry LC-MS/MS runs. It enables the simulation of any biomolecule amenable to mass spectrometry (MS) since all calculations are based on chemical formulas. SMITER features a modular design, allowing for an easy implementation of different noise and fragmentation models. By default, SMITER uses an established noise model and offers several methods for peptide fragmentation, and two models for nucleoside fragmentation and one for lipid fragmentation. Due to the rich Python ecosystem, other modules, e.g., for retention time (RT) prediction, can easily be implemented for the tailored simulation of any molecule of choice. This facilitates the generation of defined gold-standard LC-MS/MS datasets for any type of experiment. Such gold standards, where the ground truth is known, are required in computational mass spectrometry to test new algorithms and to improve parameters of existing ones. Similarly, gold-standard datasets can be used to evaluate analytical challenges, e.g., by predicting co-elution and co-fragmentation of molecules. As these challenges hinder the detection or quantification of co-eluents, a comprehensive simulation can identify and thus, prevent such difficulties before performing actual MS experiments. SMITER allows the creation of such datasets easily, fast, and efficiently. |
format | Online Article Text |
id | pubmed-8000309 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80003092021-03-28 SMITER—A Python Library for the Simulation of LC-MS/MS Experiments Kösters, Manuel Leufken, Johannes Leidel, Sebastian A. Genes (Basel) Article SMITER (Synthetic mzML writer) is a Python-based command-line tool designed to simulate liquid-chromatography-coupled tandem mass spectrometry LC-MS/MS runs. It enables the simulation of any biomolecule amenable to mass spectrometry (MS) since all calculations are based on chemical formulas. SMITER features a modular design, allowing for an easy implementation of different noise and fragmentation models. By default, SMITER uses an established noise model and offers several methods for peptide fragmentation, and two models for nucleoside fragmentation and one for lipid fragmentation. Due to the rich Python ecosystem, other modules, e.g., for retention time (RT) prediction, can easily be implemented for the tailored simulation of any molecule of choice. This facilitates the generation of defined gold-standard LC-MS/MS datasets for any type of experiment. Such gold standards, where the ground truth is known, are required in computational mass spectrometry to test new algorithms and to improve parameters of existing ones. Similarly, gold-standard datasets can be used to evaluate analytical challenges, e.g., by predicting co-elution and co-fragmentation of molecules. As these challenges hinder the detection or quantification of co-eluents, a comprehensive simulation can identify and thus, prevent such difficulties before performing actual MS experiments. SMITER allows the creation of such datasets easily, fast, and efficiently. MDPI 2021-03-11 /pmc/articles/PMC8000309/ /pubmed/33799543 http://dx.doi.org/10.3390/genes12030396 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Kösters, Manuel Leufken, Johannes Leidel, Sebastian A. SMITER—A Python Library for the Simulation of LC-MS/MS Experiments |
title | SMITER—A Python Library for the Simulation of LC-MS/MS Experiments |
title_full | SMITER—A Python Library for the Simulation of LC-MS/MS Experiments |
title_fullStr | SMITER—A Python Library for the Simulation of LC-MS/MS Experiments |
title_full_unstemmed | SMITER—A Python Library for the Simulation of LC-MS/MS Experiments |
title_short | SMITER—A Python Library for the Simulation of LC-MS/MS Experiments |
title_sort | smiter—a python library for the simulation of lc-ms/ms experiments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8000309/ https://www.ncbi.nlm.nih.gov/pubmed/33799543 http://dx.doi.org/10.3390/genes12030396 |
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