MeltDB

MeltDB processes and organizes high-throughput metabolomics datasets from gas chromatography (GC) and liquid chromatography–mass spectrometry (LC-MS) to support preprocessing, statistical analysis, visualization, and integration with functional genomics and transcriptomics.

Key Features:

• Instrument data support: Processes raw metabolome data generated by gas chromatography (GC) and liquid chromatography–mass spectrometry (LC-MS).
• Supported file formats: Supports netCDF, mzXML, and mzDATA file formats for metabolomics data exchange.
• Preprocessing pipelines: Provides flexible pipelines for raw-data preprocessing and importing pre-processed datasets.
• Integration of analysis packages: Integrates external open-source packages including XCMS, MassSpecWavelet, and metaB for preprocessing and analysis.
• Dataset description and storage: Enables systematic dataset description, standardized storage, and annotation of large-scale metabolomics experiments.
• Statistical and functional integration: Supports higher-level statistical analyses and contextualization of metabolomics results within functional genomics and transcriptomics frameworks.
• Visualization: Provides visualization features for interpretation of processed metabolomics datasets.

Scientific Applications:

• Metabolic profiling and pathway analysis: Dissects the influence of different carbon sources on metabolites in the gram-negative bacterium Xanthomonas campestris pv. campestris to explore metabolic pathways and regulatory mechanisms.
• Functional genomics integration: Situates metabolomics changes within genomics and transcriptomics analyses to investigate cellular regulatory processes.

Methodology:

Computational steps include handling raw GC and LC-MS metabolome data, flexible preprocessing pipelines and import of pre-processed data, support for netCDF/mzXML/mzDATA formats, integration of XCMS, MassSpecWavelet, and metaB, standardized dataset description and storage, and visualization of processed results.