WebSpecmine

WebSpecmine provides web-based analysis of metabolomics data by integrating Nuclear Magnetic Resonance (NMR), Infrared Spectroscopy, UV-visible Spectroscopy, Raman Spectroscopy, Liquid Chromatography–Mass Spectrometry (LC-MS), and Gas Chromatography–Mass Spectrometry (GC-MS) data to enable compound quantification, metabolite identification, and statistical analysis for biomedical research.

Key Features:

• Integration of spectroscopic and chromatographic techniques: Combines data from NMR, Infrared, UV-visible, Raman, LC-MS, and GC-MS for joint analysis.
• Quantification: Performs analysis of compound concentrations within complex biological samples.
• Statistical analyses: Supports univariate, unsupervised, and supervised multivariate statistical analyses.
• Metabolite identification: Provides functionality for identifying metabolites from spectral and chromatographic data.
• Pathway analysis: Enables biochemical pathway analysis linked to identified metabolites.
• Data management: Includes features for dataset storage and management for analysis workflows.

Scientific Applications:

• Comprehensive metabolomics studies: Enables integrated analyses combining multiple spectroscopic and chromatographic modalities.
• Quantitative metabolite profiling: Facilitates measurement of compound concentrations in biological samples.
• Metabolite identification and pathway mapping: Supports identification of metabolites and subsequent pathway analysis.
• Investigation of metabolic processes and disease mechanisms: Applies statistical and pathway analyses to study metabolic alterations in biomedical research.

Methodology:

Implements univariate, unsupervised, and supervised multivariate statistical analyses, metabolite identification, and pathway analysis using the R programming language and is deployed via the Shiny web framework.