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PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis

[Image: see text] The increased speed and sensitivity in mass spectrometry-based proteomics has encouraged its use in biomedical research in recent years. Large-scale detection of proteins in cells, tissues, and whole organisms yields highly complex quantitative data, the analysis of which poses sig...

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Autores principales: Ressa, Anna, Fitzpatrick, Martin, van den Toorn, Henk, Heck, Albert J. R., Altelaar, Maarten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2018
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364269/
https://www.ncbi.nlm.nih.gov/pubmed/30525654
http://dx.doi.org/10.1021/acs.jproteome.8b00576
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author Ressa, Anna
Fitzpatrick, Martin
van den Toorn, Henk
Heck, Albert J. R.
Altelaar, Maarten
author_facet Ressa, Anna
Fitzpatrick, Martin
van den Toorn, Henk
Heck, Albert J. R.
Altelaar, Maarten
author_sort Ressa, Anna
collection PubMed
description [Image: see text] The increased speed and sensitivity in mass spectrometry-based proteomics has encouraged its use in biomedical research in recent years. Large-scale detection of proteins in cells, tissues, and whole organisms yields highly complex quantitative data, the analysis of which poses significant challenges. Standardized proteomic workflows are necessary to ensure automated, sharable, and reproducible proteomics analysis. Likewise, standardized data processing workflows are also essential for the overall reproducibility of results. To this purpose, we developed PaDuA, a Python package optimized for the processing and analysis of (phospho)proteomics data. PaDuA provides a collection of tools that can be used to build scripted workflows within Jupyter Notebooks to facilitate bioinformatics analysis by both end-users and developers.
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spelling pubmed-63642692019-02-07 PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis Ressa, Anna Fitzpatrick, Martin van den Toorn, Henk Heck, Albert J. R. Altelaar, Maarten J Proteome Res [Image: see text] The increased speed and sensitivity in mass spectrometry-based proteomics has encouraged its use in biomedical research in recent years. Large-scale detection of proteins in cells, tissues, and whole organisms yields highly complex quantitative data, the analysis of which poses significant challenges. Standardized proteomic workflows are necessary to ensure automated, sharable, and reproducible proteomics analysis. Likewise, standardized data processing workflows are also essential for the overall reproducibility of results. To this purpose, we developed PaDuA, a Python package optimized for the processing and analysis of (phospho)proteomics data. PaDuA provides a collection of tools that can be used to build scripted workflows within Jupyter Notebooks to facilitate bioinformatics analysis by both end-users and developers. American Chemical Society 2018-12-10 2019-02-01 /pmc/articles/PMC6364269/ /pubmed/30525654 http://dx.doi.org/10.1021/acs.jproteome.8b00576 Text en Copyright © 2018 American Chemical Society This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License (http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html) , which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes.
spellingShingle Ressa, Anna
Fitzpatrick, Martin
van den Toorn, Henk
Heck, Albert J. R.
Altelaar, Maarten
PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis
title PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis
title_full PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis
title_fullStr PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis
title_full_unstemmed PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis
title_short PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis
title_sort padua: a python library for high-throughput (phospho)proteomics data analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364269/
https://www.ncbi.nlm.nih.gov/pubmed/30525654
http://dx.doi.org/10.1021/acs.jproteome.8b00576
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