mzqLibrary

mzqLibrary implements the Proteomics Standards Initiative (PSI) mzQuantML XML standard in Java to process, map, normalize, convert, and visualize quantitative LC-MS proteomics data for peptide-, protein-, and protein-group-level analysis.

Key Features:

• mzQuantML support: Implements the PSI mzQuantML XML format to capture two-dimensional LC-MS features and quantified peptides, proteins, and protein groups across multiple samples.
• Java implementation: Provides an open-source Java-based library of routines for post-processing and analysis of quantitative proteomics data.
• Data mapping and quantification: Maps peptide identifications onto quantified features and infers protein- or protein-group-level quantification from peptide-level data.
• Normalization and statistical analysis: Includes routines for normalization and basic statistical analyses to assess differential expression between samples.
• File format conversion: Offers converters to import formats from OpenMS, Progenesis LC-MS, and MaxQuant and to export to mzTab, HTML, and CSV.
• Visualization and R integration: Provides the mzqViewer for visualizing feature-, peptide-, and protein-level tables and integrates with the R statistical library for plotting.

Scientific Applications:

• Quantitative proteomics processing: Mapping peptide identifications to quantified LC-MS features and deriving peptide-, protein-, and protein-group-level quantities.
• Differential expression analysis: Normalizing quantitative datasets and performing basic statistical tests to detect changes between samples.
• Data standardization and exchange: Capturing and archiving quantitative proteomic results in mzQuantML to enable reproducibility and data exchange.
• Workflow interoperability: Interfacing with OpenMS, Progenesis LC-MS, MaxQuant, and exporting to mzTab/CSV/HTML to integrate with diverse proteomic workflows.

Methodology:

Java-based routines implement the mzQuantML XML format to represent LC-MS two-dimensional features and quantified peptides/proteins/protein groups across samples, provide format converters (OpenMS, Progenesis LC-MS, MaxQuant ↔ mzTab/HTML/CSV), and integrate with the R statistical library for visualization.