TurboPutative

TurboPutative automates processing and classification of LC-MS untargeted metabolomics data to reduce and prioritize putative metabolite annotations for downstream biological interpretation.

Key Features:

• Modular Architecture: Composed of four modules—Tagger, REname, RowMerger, and TPMetrics—each addressing specific data-processing tasks in metabolomics.
• Tagger Module: Classifies compounds to provide preliminary categorization of metabolites based on chemical properties.
• REname Module: Improves putative annotation handling by recognizing isomers and equivalent compounds and removing redundant annotations.
• RowMerger Module: Consolidates similar entries to reduce dataset size and simplify manual comparison among annotations.
• TPMetrics Module: Integrates multiple datasets with feature intensity and other information and computes scores based on adduct probability and feature correlations to aid identification and assessment.

Scientific Applications:

• Annotation Reduction and Prioritization: Reduces putative annotations by 80%–90%, facilitating prioritization of metabolites for downstream pathway analysis.
• Untargeted Metabolomics Identification: Accelerates identification and characterization of metabolites in LC-MS untargeted metabolomics datasets to support biological interpretation.

Methodology:

Computational methods explicitly include automated classification (Tagger), annotation normalization and redundancy removal (REname), row consolidation (RowMerger), dataset integration and scoring using adduct probability and feature correlations (TPMetrics); the workflow is automated, customizable, and exposes RESTful API services.