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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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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. |
format | Online Article Text |
id | pubmed-9710650 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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