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

## TL;DR

Alpaca is a Python-based tool that simplifies and improves reproducibility in analyzing proteomics data for systems biology.

## Contribution

Alpaca introduces a user-friendly, modular pipeline for absolute proteome quantification with both downloadable and web-based access.

## Key findings

- Alpaca simplifies the extraction and calculation of protein abundances from unprocessed proteomics data.
- The tool supports label-free quantification methods and improves reproducibility across research settings.
- Its modular design allows integration with other software tools.

## 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.