VIQoR: a web service for visually supervised protein inference and protein quantification.

Area of Science:

• Proteomics
• Computational Biology
• Bioinformatics

Background:

• Quantitative proteomics relies on accurate protein concentration estimation from peptide data.
• Protein inference and abundance summarization are critical but complex steps.
• Existing tools lack integrated visualization for peptide and protein quantitative data, including post-translational modifications.

Purpose of the Study:

• To present VIQoR, a user-friendly web service for post-identification quantitative analysis in proteomics.
• To provide integrated protein inference, summarization, and interactive visualization capabilities.
• To address the need for tools that handle complex proteomics data and uncertainty.

Main Methods:

• VIQoR implements two parsimonious algorithms for protein inference.
• Protein summarization uses fast-FARMS followed by a weighted average function, optimized by the Global Correlation Indicator (GCI).
• The tool visualizes quantitative data through the novel VIQoR plot, integrating peptide and protein levels.

Main Results:

• VIQoR successfully performs protein inference and summarization on labeled and label-free quantitative proteomics data.
• The protein inference algorithms effectively handle shared peptides.
• The Global Correlation Indicator (GCI) enhances quantitative analysis accuracy in replicated datasets.

Conclusions:

• VIQoR offers a comprehensive solution for quantitative proteomics analysis, combining inference, summarization, and visualization.
• The tool's interactive visualization aids in understanding complex quantitative proteomics data.
• VIQoR improves the reliability and accuracy of protein abundance estimation in mass spectrometry-based proteomics.