Quant

Quant performs quantitative analysis of iTRAQ-labeled mass spectrometry data to produce peptide- and protein-level abundance estimates for quantitative proteomics.

Key Features:

• Error estimation and statistical methods: Implements robust error estimation and advanced statistical methods for precise abundance analysis.
• Validation with established tools: Validated against ProQuant and Mascot 2.2, demonstrating consistency with these established methodologies.
• Sum-of-intensities quantification: Uses the sum of intensities rather than peak area integration for peptide quantification.
• Integration with tandem MS tools: Combines iTRAQ quantitative data with peptide and protein identifications from MS/MS identification tools.
• Visualization and quality measures: Produces visualization outputs and computes quality measures to assess reliability of quantification results.
• Lognormal distribution fit: Fits a lognormal distribution to mass spectrometry–based relative peptide quantification data.

Scientific Applications:

• Quantitative proteomics experiments: Peptide- and protein-level quantitation in iTRAQ-based MS experiments.
• Biomarker discovery: Comparative protein expression analysis to identify candidate biomarkers.
• Disease mechanism elucidation: Analysis of differential protein abundance to investigate disease-related pathways.
• Drug development: Comparative proteomic profiling to support target validation and treatment response studies.

Methodology:

Implemented in MATLAB; applies error estimation and statistical methods, quantifies peptides using sum of intensities, integrates quantitative iTRAQ data with MS/MS identifications, computes visualization and quality measures, fits lognormal distributions to peptide quantification data, and was validated against ProQuant and Mascot 2.2.