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MSPypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics

SUMMARY: Mass spectrometry-based proteomics is increasingly employed in biology and medicine. To generate reliable information from large datasets and ensure comparability of results, it is crucial to implement and standardize the quality control of the raw data, the data processing steps and the st...

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Autores principales: Heming, Simon, Hansen, Pauline, Vlasov, Artyom, Schwörer, Florian, Schaumann, Stephen, Frolovaitė, Paulina, Lehmann, Wolf-Dieter, Timmer, Jens, Schilling, Marcel, Helm, Barbara, Klingmüller, Ursula
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710650/
https://www.ncbi.nlm.nih.gov/pubmed/36699356
http://dx.doi.org/10.1093/bioadv/vbac004
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author Heming, Simon
Hansen, Pauline
Vlasov, Artyom
Schwörer, Florian
Schaumann, Stephen
Frolovaitė, Paulina
Lehmann, Wolf-Dieter
Timmer, Jens
Schilling, Marcel
Helm, Barbara
Klingmüller, Ursula
author_facet Heming, Simon
Hansen, Pauline
Vlasov, Artyom
Schwörer, Florian
Schaumann, Stephen
Frolovaitė, Paulina
Lehmann, Wolf-Dieter
Timmer, Jens
Schilling, Marcel
Helm, Barbara
Klingmüller, Ursula
author_sort Heming, Simon
collection PubMed
description SUMMARY: Mass spectrometry-based proteomics is increasingly employed in biology and medicine. To generate reliable information from large datasets and ensure comparability of results, it is crucial to implement and standardize the quality control of the raw data, the data processing steps and the statistical analyses. MSPypeline provides a platform for importing MaxQuant output tables, generating quality control reports, data preprocessing including normalization and performing exploratory analyses by statistical inference plots. These standardized steps assess data quality, provide customizable figures and enable the identification of differentially expressed proteins to reach biologically relevant conclusions. AVAILABILITY AND IMPLEMENTATION: The source code is available under the MIT license at https://github.com/siheming/mspypeline with documentation at https://mspypeline.readthedocs.io. Benchmark mass spectrometry data are available on ProteomeXchange (PXD025792). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.
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spelling pubmed-97106502023-01-24 MSPypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics Heming, Simon Hansen, Pauline Vlasov, Artyom Schwörer, Florian Schaumann, Stephen Frolovaitė, Paulina Lehmann, Wolf-Dieter Timmer, Jens Schilling, Marcel Helm, Barbara Klingmüller, Ursula Bioinform Adv Application Note SUMMARY: Mass spectrometry-based proteomics is increasingly employed in biology and medicine. To generate reliable information from large datasets and ensure comparability of results, it is crucial to implement and standardize the quality control of the raw data, the data processing steps and the statistical analyses. MSPypeline provides a platform for importing MaxQuant output tables, generating quality control reports, data preprocessing including normalization and performing exploratory analyses by statistical inference plots. These standardized steps assess data quality, provide customizable figures and enable the identification of differentially expressed proteins to reach biologically relevant conclusions. AVAILABILITY AND IMPLEMENTATION: The source code is available under the MIT license at https://github.com/siheming/mspypeline with documentation at https://mspypeline.readthedocs.io. Benchmark mass spectrometry data are available on ProteomeXchange (PXD025792). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online. Oxford University Press 2022-01-17 /pmc/articles/PMC9710650/ /pubmed/36699356 http://dx.doi.org/10.1093/bioadv/vbac004 Text en © The Author(s) 2022. 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 Application Note
Heming, Simon
Hansen, Pauline
Vlasov, Artyom
Schwörer, Florian
Schaumann, Stephen
Frolovaitė, Paulina
Lehmann, Wolf-Dieter
Timmer, Jens
Schilling, Marcel
Helm, Barbara
Klingmüller, Ursula
MSPypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics
title MSPypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics
title_full MSPypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics
title_fullStr MSPypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics
title_full_unstemmed MSPypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics
title_short MSPypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics
title_sort mspypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics
topic Application Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710650/
https://www.ncbi.nlm.nih.gov/pubmed/36699356
http://dx.doi.org/10.1093/bioadv/vbac004
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