MapQuant

MapQuant quantifies whole-cell protein abundances from liquid chromatography–mass spectrometry (LC/MS) data to enable proteome-wide quantification and molecular characterization.

Key Features:

• Image-Based Analysis: Conceptualizes LC/MS data as images to apply image processing methods for data interpretation.
• Noise Filtering and Segmentation: Employs noise filtering and watershed segmentation to isolate relevant signals from background noise.
• Peak Detection and Fitting: Performs peak finding and fitting to identify and quantify organic species within MS datasets.
• Peak Clustering and Charge-State Determination: Clusters peaks and determines charge states to improve molecular characterization accuracy.
• Carbon-Content Estimation: Estimates carbon content of detected species to provide insight into molecular composition.
• Linear Abundance Reporting: Reports abundance values that respond linearly with sample amounts across a dynamic range exceeding 1000-fold for low- and high-resolution instruments.
• Robustness Against Background Noise: Maintains accuracy in the presence of background noise from medium-complexity peptide mixtures or complex trypsinized proteomes, with coefficients of variance comparable to other methods.
• High-Resolution Mass Spectrometry Capabilities: Defines accurate mass and retention-time features for isotopic clusters on high-resolution mass spectrometers and assigns sequence identities to observed isotopic clusters even without tandem MS (MS/MS).

Scientific Applications:

• Whole-Cell Proteome Quantification: Enables comprehensive quantification of proteins within whole cells using LC/MS data.
• Protein Expression Profiling: Supports analysis of protein expression levels across conditions.
• Post-Translational Modification Analysis: Facilitates detection and quantification relevant to post-translational modifications.
• Sequence Identification without MS/MS: Assigns sequence identities to isotopic clusters to enable sequence identification in the absence of MS/MS data.
• Analysis of Complex Proteomes: Applies to medium-complexity peptide mixtures and complex trypsinized proteomes for quantitative studies.
• Investigation of Cellular Functions and Disease Mechanisms: Supports studies of cellular functions and disease mechanisms via reliable abundance measurements and molecular characterization.

Methodology:

MapQuant treats LC/MS experiments as images and applies image-processing techniques explicitly including noise filtering, watershed segmentation, peak finding and fitting, peak clustering, charge-state determination, carbon-content estimation, and mass and retention-time feature definition for isotopic clusters to assign sequence identities without MS/MS.