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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Vydané v:Proteomics (Weinheim) Ročník 25; číslo 9-10; s. e202400417 - n/a
Hlavní autori: Ferrero‐Bordera, Borja, Becher, Dörte, Maaß, Sandra
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Germany Wiley Subscription Services, Inc 01.05.2025
John Wiley and Sons Inc
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ISSN:1615-9853, 1615-9861, 1615-9861
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Shrnutí: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.
Bibliografia: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).
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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