Alpaca. A Simplified and Reproducible Python‐Based Pipeline for Absolute Proteome Quantification Data Mining

ABSTRACT The accurate construction of computational models in systems biology heavily relies on the availability of quantitative proteomics data, specifically, absolute protein abundances. However, the complex nature of proteomics data analysis necessitates specialised expertise, making the integrat...

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Veröffentlicht in:Proteomics (Weinheim) Jg. 25; H. 9-10; S. e202400417 - n/a
Hauptverfasser: Ferrero‐Bordera, Borja, Becher, Dörte, Maaß, Sandra
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Germany Wiley Subscription Services, Inc 01.05.2025
John Wiley and Sons Inc
Schlagworte:
absolute proteome quantification
Animals
Availability
Biology
Data analysis
Data mining
Data Mining - methods
Data processing
Graphical user interface
Mathematical models
Modular structures
open source
protein abundances
Proteins
Proteome - analysis
Proteomes
Proteomics
Proteomics - methods
proteomics analysis
Python
Reproducibility
Reproducibility of Results
Robustness
Software
Technical Brief
User-Computer Interface
open source
proteomics
data mining
protein abundances
Python
absolute proteome quantification
proteomics analysis
ISSN:1615-9853, 1615-9861, 1615-9861
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:ABSTRACT The accurate construction of computational models in systems biology heavily relies on the availability of quantitative proteomics data, specifically, absolute protein abundances. However, the complex nature of proteomics data analysis necessitates specialised expertise, making the integration of this data into models challenging. Therefore, the development of software tools that ease the analysis of proteomics data and bridge between disciplines is crucial for advancing the field of systems biology. We developed an open access Python‐based software tool available either as downloadable library or as web‐based graphical user interface (GUI). The pipeline simplifies the extraction and calculation of protein abundances from unprocessed proteomics data, accommodating a range of experimental approaches based on label‐free quantification. Our tool was conceived as a versatile and robust pipeline designed to ease and simplify data analysis, thereby improving reproducibility between researchers and institutions. Moreover, the robust modular structure of Alpaca allows its integration with other software tools.
Bibliographie:This study was funded by the People Programme (Marie Skłodowska‐Curie Actions) of the European Union's Horizon 2020 Programme under REA grant agreement no. 813979 (SECRETERS).
Funding
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Funding: This study was funded by the People Programme (Marie Skłodowska‐Curie Actions) of the European Union's Horizon 2020 Programme under REA grant agreement no. 813979 (SECRETERS).
ISSN:1615-9853
1615-9861
1615-9861
DOI:10.1002/pmic.202400417