fermo-core

fermo-core processes and integrates large-scale metabolomics datasets to identify and prioritize molecular features linked to phenotypic variation.

Key Features:

• Automated Data Processing: Automates processing of metabolomics data and leverages liquid chromatography-tandem mass spectrometry (LC-MS/MS) outputs that detect hundreds of molecules per sample.
• Data Annotation and Integration: Organizes and annotates orthogonal data and integrates standardized phenotypic metadata alongside molecular measurements.
• Modular Framework: Implements a modular framework that enables adaptation of analysis workflows to diverse research needs.
• Interactive Visualization and Filtering: Supports interactive visualization and advanced data filtering for refinement of datasets and molecular feature selection.
• Reproducible Prioritization: Facilitates reproducible prioritization of molecular features and samples associated with phenotypes of interest.
• Support for Exploratory and Targeted Analysis: Supports both exploratory, data-driven analyses and targeted analyses focused on specific hypotheses.

Scientific Applications:

• Benchmarking and Prioritization: Enables benchmarking studies aimed at prioritizing bioactive compounds from complex biological matrices.
• Drug Discovery: Supports identification and prioritization of candidate bioactive molecules relevant to drug discovery.
• Biomarker Identification: Aids in discovering and prioritizing molecular features as potential biomarkers linked to phenotypic traits.
• Metabolic Pathway Analysis: Supports investigations into metabolic pathways by linking small molecules to phenotypic variation.

Methodology:

Integrates computational tools to organize, annotate, and integrate orthogonal data, automates processing of LC-MS/MS outputs, and implements reproducible prioritization to select biologically relevant subsets from large datasets.