Overview
Asari uses mass alignment and a composite representation of chromatographic signals to extract features across samples. Its default workflow focuses on LC-MS feature processing.
What can this tool do?
Common uses
- Build a feature table from centroided LC-MS files.
- Prepare data for downstream quality control and statistics.
Scientific fit
Primary function
Preprocessing and feature extraction
Data compatibility
Inputs
Mass spectra
Outputs
Feature or peak table
Input formats
mzML
Output formats
CSV or TSV, JSON
Scope and considerations
Designed for
- Build a feature table from centroided LC-MS files.
Not designed for
- Assigning confirmed chemical structures from feature detection alone.
Important requirements
- Centroided mzML data and a compatible Python environment.
Considerations
- Check mass tolerances, chromatography and acquisition mode against the workflow; default processing and specialized workflows differ.
Access and documentation
Source and installation instructions are public; consult the repository license for the version used.
Evidence and review
Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed.
Core publications
Resource papers, direct updates and tool-specific methods. Selection policy.
- Li S, Siddiqa A, Thapa M, et al. Trackable and scalable LC-MS metabolomics data processing using asari. Nat Commun 14, 4113 (2023). doi:10.1038/s41467-023-39889-1
Related tools
Entry history
Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.
- 2026-09-08 · Entry update
Audit core publications across existing tools
View change · Entry at this revision - 2026-09-07 · First committed entry
Add 50 documented metabolomics resources
View change · Entry at this revision