MetCraft: AI-Powered Platform for Systems Metabolomics

MetCraft is an AI-powered platform for pipeline-based analysis of metabolomics data. Multiple modules are implemented in MetCraft for various applications such as: (1) data import; (2) data processing including peak detection from LC-MS metabolomics data; (3) metabolite annotation by spectral matching and compound fingerprint prediction; and (4) integrative analysis of multi-omics data for biomarker discovery and systems metabolomics research. Users can drag modules to the pipeline canvas to build pipelines, configure them, and run them on the cloud.


MODULES Drag a module from this panel to the "PIPELINES" canvas


Data Import Use these modules to import processed/unprocessed data

Data Upload
Retrieval from Database



Data Processing Use these modules to process raw metabolomic data

Peak Detection
Adduct/Isotope Recognition
Outlier Screening
Data Filter
Missing Value Imputation
Normalization
Batch Correction

Metabolite Annotation Use these modules to annotate metabolites based on m/z values, MS/MS, or GC-MS data

Spectral Matching
Compound Fingerprint Prediction
Mass-Based Search
IF-THEN Rule
Isotopic Pattern Analysis
Network-Based Annotation


Data/Integrative Analysis Use these modules to select markers that distinguish between two pre-specified groups

Univariate Statistical Analysis
Multivariate Regression Analysis
Hierarchical Integrative Analysis
Network-Based Analysis
Machine Learning
Generative AI

PIPELINE CANVAS Drag a module from the “MODULES” panel to this canvas


Drag and drop modules here

PROGRESS See the progress on this panel

MetCraft version 0.96

MANAGEMENT UTILITIES Click a button to execute a task for a selected module or to save/load a pipeline






MODULE TASKS Click a button to execute a task for a selected module or to save/load a pipeline






PIPELINE TASKS Click a button to execute a task for a selected module or to save/load a pipeline



DEMO/USER MANUAL Click a button to load a demo pipeline or to download demo/user manual files