PiProteline: An R Package for Integrated Proteomics Data Analysis, from Label-Free Quantitation to Systems Biology.
- 1Department of Biomedical Science, Institute for Biomedical Technologies, National Research Council, F.lli Cervi 93, Segrate, 20054 Milan, Italy.
- 2Institute of Experimental Endocrinology and Oncology "G. Salvatore", National Research Council, Pietro Castellino 111, 80131 Naples, Italy.
Abstract
The demand for user-friendly applications to support biologists in analyzing high-throughput proteomics data remains a pressing challenge. Given the complexity and the multiple intermediate steps involved, this process is time-consuming and often requires specialized computational skills. To simplify and accelerate the exploration of proteomics data, we present PiProteline, an R package designed to operate on high-dimensional data matrices assembled from the output of any search engine commonly used in bottom-up proteomics experiments. In addition to data preprocessing and descriptive statistics, PiProteline enables label-free quantitation, functional enrichment, and systems biology analyses. It supports both unweighted and weighted protein-protein interaction (PPI) network topological analyses for the identification of critical nodes, such as hubs and bottlenecks. By integrating multiple computational approaches, PiProteline accelerates the selection of potentially relevant protein signatures, providing insights into the molecular mechanisms characterizing the investigated systems. This information facilitates the formulation of new hypotheses and the design of targeted experiments, ultimately reducing costs and advancing translational medicine. PiProteline is available as an open-source R package via GitHub, as a Shiny application, and via Zenodo.
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BibTeX
@article{lomagno2026piproteline,
title = {{PiProteline: An R Package for Integrated Proteomics Data Analysis, from Label-Free Quantitation to Systems Biology.}},
author = {Andrea Lomagno and Saleh Hamed and Ishak Yusuf and Pietro Luigi Mauri and Dario Di Silvestre},
journal = {Biology},
year = {2026},
month = oct,
doi = {10.3390/biology15191723},
url = {https://europepmc.org/article/MED/42883396},
note = {CC BY},
}
RIS
TY - JOUR
TI - PiProteline: An R Package for Integrated Proteomics Data Analysis, from Label-Free Quantitation to Systems Biology.
AU - Andrea Lomagno
AU - Saleh Hamed
AU - Ishak Yusuf
AU - Pietro Luigi Mauri
AU - Dario Di Silvestre
PY - 2026
DA - 2026/10/01
JO - Biology
DO - 10.3390/biology15191723
UR - https://europepmc.org/article/MED/42883396
AB - The demand for user-friendly applications to support biologists in analyzing high-throughput proteomics data remains a pressing challenge. Given the complexity and the multiple intermediate steps involved, this process is time-consuming and often requires specialized computational skills. To simplify and accelerate the exploration of proteomics data, we present PiProteline, an R package designed to operate on high-dimensional data matrices assembled from the output of any search engine commonly used in bottom-up proteomics experiments. In addition to data preprocessing and descriptive statistics, PiProteline enables label-free quantitation, functional enrichment, and systems biology analyses. It supports both unweighted and weighted protein-protein interaction (PPI) network topological analyses for the identification of critical nodes, such as hubs and bottlenecks. By integrating multiple computational approaches, PiProteline accelerates the selection of potentially relevant protein signatures, providing insights into the molecular mechanisms characterizing the investigated systems. This information facilitates the formulation of new hypotheses and the design of targeted experiments, ultimately reducing costs and advancing translational medicine. PiProteline is available as an open-source R package via GitHub, as a Shiny application, and via Zenodo.
SN - 2079-7737
ER -