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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| Published in: | Proteomics (Weinheim) Vol. 25; no. 9-10; pp. e202400417 - n/a |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. 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. |
| Author | Ferrero‐Bordera, Borja Maaß, Sandra Becher, Dörte |
| AuthorAffiliation | 1 Department of Microbial Proteomics Institute of Microbiology Center of Functional Genomics of Microbes University of Greifswald Greifswald Germany 2 Institute of Medical Psychology Medical Faculty Ludwig‐Maximilians‐University Munich Munich Germany |
| AuthorAffiliation_xml | – name: 2 Institute of Medical Psychology Medical Faculty Ludwig‐Maximilians‐University Munich Munich Germany – name: 1 Department of Microbial Proteomics Institute of Microbiology Center of Functional Genomics of Microbes University of Greifswald Greifswald Germany |
| Author_xml | – sequence: 1 givenname: Borja orcidid: 0000-0002-6535-8607 surname: Ferrero‐Bordera fullname: Ferrero‐Bordera, Borja organization: Ludwig‐Maximilians‐University Munich – sequence: 2 givenname: Dörte orcidid: 0000-0002-9630-5735 surname: Becher fullname: Becher, Dörte organization: University of Greifswald – sequence: 3 givenname: Sandra orcidid: 0000-0002-6573-1088 surname: Maaß fullname: Maaß, Sandra email: sandra.maass@uni-greifswald.de organization: University of Greifswald |
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| Cites_doi | 10.1093/bioadv/vbab041 10.1038/nprot.2017.040 10.1126/science.1069492 10.1093/BIOINFORMATICS/BTU200 10.1128/spectrum.02616‐23 10.1074/mcp.M113.037309 10.1186/s12864‐022‐09058‐7 10.1038/nature 10.1038/nmeth.3901 10.1002/pmic.201400441 10.1021/acs.analchem.9b02869 10.1038/nbt.1511 10.15252/msb.20209536 10.1016/j.jprot.2016.01.015 |
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| Copyright | 2025 The Author(s). published by Wiley‐VCH GmbH. 2025 The Author(s). PROTEOMICS published by Wiley‐VCH GmbH. 2025. This work is published under Creative Commons Attribution License~https://creativecommons.org/licenses/by/3.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | open source proteomics data mining protein abundances Python absolute proteome quantification proteomics analysis |
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| References | 2015; 15 2019; 91 2016; 537 2002; 295 2021; 17 2017; 12 2022; 23 2008; 26 2014; 13 2024; 12 2022; 2 2016; 136 2014; 30 2016; 13 e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_13_1 e_1_2_7_12_1 e_1_2_7_11_1 e_1_2_7_10_1 |
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The accurate construction of computational models in systems biology heavily relies on the availability of quantitative proteomics data, specifically,... The accurate construction of computational models in systems biology heavily relies on the availability of quantitative proteomics data, specifically, absolute... |
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| SubjectTerms | 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 |
| Title | Alpaca. A Simplified and Reproducible Python‐Based Pipeline for Absolute Proteome Quantification Data Mining |
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