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Updated: May 10, 2025

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

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Published on: February 27, 2020

Ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata.

Ping Zheng¹, Enrique Audain², Henry Webel³

¹Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China.

Journal of Proteomics

|April 23, 2025

Summary

This summary is machine-generated.

We developed ibaqpy, a Python package for efficient protein quantification using intensity-based absolute quantification (iBAQ). This tool automates normalization and batch correction for large-scale proteomics experiments, enhancing data reproducibility and accessibility.

Keywords:

Big data Bioinformatics Data integration Proteomics Quantification

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Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Intensity-based absolute quantification (iBAQ) is crucial for determining protein abundance in proteomics.
Large-scale experiments (DIA, TMT, LFQ) present scalability and reproducibility challenges for iBAQ computation.
Existing methods often lack metadata integration, hindering accurate analysis of complex experimental designs.

Purpose of the Study:

To introduce ibaqpy, a Python package for efficient and scalable iBAQ value computation.
To enable automated normalization and batch correction using experimental metadata.
To support reproducible and FAIR-compliant quantitative proteomics.

Main Methods:

Developed the ibaqpy Python package.
Leveraged the Sample and Data Relationship Format (SDRF) for metadata integration.
Automated normalization and batch correction based on experimental design (replicates, fractions, conditions).

Main Results:

Successfully reanalyzed 17 public proteomics datasets (4921 samples, 5766 MS runs) from ProteomeXchange.
Quantified 11,014 proteins, demonstrating ibaqpy's scalability for large datasets.
ibaqpy automated reproducible quantification, reducing manual effort.

Conclusions:

ibaqpy provides an efficient, scalable, and reproducible solution for iBAQ computation in large-scale proteomics.
Metadata integration via SDRF enhances quantification accuracy and accounts for experimental complexities.
The package promotes FAIR data principles, improving data reuse and integration in proteomics research.