Overview
JPA combines MS1 chromatographic peak picking with MS/MS-precursor-based feature extraction to broaden LC-MS feature detection. The R workflow supports alignment and downstream handling of the jointly extracted feature set.
What can this tool do?
Common uses
- JPA combines MS1 chromatographic peak picking with MS/MS-precursor-based feature extraction to broaden LC-MS feature detection.
Scientific fit
Primary function
Preprocessing and feature extraction
Analysis types
Targeted metabolomics, Untargeted metabolomics
Data compatibility
Inputs
Raw instrument data, Feature or peak table, Mass spectra
Outputs
Feature or peak table
Input formats
mzXML LC-MS/MS data and optional formatted CSV peak tables
Output formats
Jointly extracted and aligned LC-MS feature tables
Scope and considerations
Designed for
- JPA combines MS1 chromatographic peak picking with MS/MS-precursor-based feature extraction to broaden LC-MS feature detection.
Not designed for
- Metabolite structure identification or recovery of compounds lacking sufficient acquisition evidence.
Important requirements
- R/XCMS dependencies and mzXML acquisition files with appropriate MS/MS precursor information; follow the documented peak-table layout when supplying existing features.
Considerations
- Additional precursor-derived features still require quality review. Peak-picking, alignment and acquisition thresholds affect the balance between added coverage and unwanted features.
Access and documentation
MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested.
License: MIT
Evidence and review
Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md.
Core publications
Resource papers, direct updates and tool-specific methods. Selection policy.
- JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics. Metabolites (2022). doi:10.3390/metabo12030212
Related tools
Entry history
Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.
- 2026-10-27 · First committed entry
Add 45 source-reviewed Enveda tool entries in third expansion batch
View change · Entry at this revision