.png){kind=icon}
Return
Alpaca. A Simplified and Reproducible Python-Based Pipeline for Absolute Proteome Quantification Data Mining
.png){kind=icon}
OA
AI
DOI：10.1002/pmic.202400417.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
absolute proteome quantification
data mining
open source
protein abundances
proteomics
proteomics analysis
Python
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
7.6K
Citations:
1.1W
Organization
Cited Papers
No cited papers available