MetaboAnalyst 2.0

MetaboAnalyst 2.0 performs comprehensive metabolomic data analysis and interpretation for processing NMR, chromatography, and mass spectrometry data to support biomarker identification and metabolic profiling.

Key Features:

• Data Processing and Normalization: Supports input types including NMR peak lists, binned spectra, MS peak lists, and compound/concentration data, and provides data filtering, editing, and normalization procedures.
• Statistical Analysis: Performs univariate and multivariate statistical analyses for two/multi-group comparisons, one/two-factor studies, and time-series data, including fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering, and other advanced statistical or machine learning methods.
• Quality Control: Includes a quality-control module to assess data quality prior to analysis.
• Functional Enrichment and Pathway Analysis: Implements metabolite set enrichment analysis and metabolic pathway analysis supporting 15 different model organisms.
• Graphical Presentation Tools: Generates publication-quality images with anti-aliasing in multiple resolutions, sizes, and formats (PNG, TIFF, PDF, PostScript, SVG).
• Performance Enhancements: Optimized for multi-core CPU execution to handle computationally intensive tasks.

Scientific Applications:

• Biomarker Identification: Facilitates identification of metabolite biomarkers from NMR and MS data.
• Metabolic Profiling: Supports metabolic profiling across various biological samples using concentration data and spectral inputs.
• Functional Interpretation and Pathway Mapping: Enables metabolite set enrichment and pathway mapping across 15 model organisms to interpret biological significance.

Methodology:

Computational methods explicitly include data filtering, editing, normalization; univariate and multivariate statistical analyses for two/multi-group comparisons, one/two-factor studies, and time-series data; fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering; metabolite set enrichment analysis; metabolic pathway analysis for 15 model organisms; and image rendering with anti-aliasing to PNG, TIFF, PDF, PostScript, and SVG outputs, with optimization for multi-core CPU execution.