nf-core/metaboigniter
Pre-processing of mass spectrometry-based metabolomics data with quantification and identification based on MS1 and MS2 data.
Version history
This latest version brings some minor updates and enhancements:
- Default change: Default negative adducts have been changed to H-1:-:0.8 H-3O-1:-:0.2
- Missing output data after peak picking have been resolved
- The issue with long file names in the alignment has been fixed
- Charge state handling in negative mode has been improved
- C13 detection is now only applied if the identification flag is set
- SIRIUS has been updated to 5.8.6
- MS2QUERY has been updated to 1.2.3
- New parameter
ignore_msms_mapping_charge_pyopenmshas been added to control the MS2 mapping if the charges are inconsistent between the consensus elements and the spectra
Thank you @axelwalter and @maxulysse for helping with this release.
This marks the release of Version 2.0.0 of our pipeline with the introduction of DSL 2 in nf-core/metaboigniter. This latest version brings an array of major updates and enhancements:
- Enhanced Input Handling: CSV file input format
- Quantification and Identification Features: We have added mapAlignerPoseClustering, featureLinkerUnlabeledkd, MetaboliteAdductDecharger, and MS2Query.
- Resolved Bugs: We’ve addressed and fixed issues related to SIRIUS APIs
- Performance: Parallel linking and identification
Deprecated Features: We have deprecated several features from version 1.0.1. These include XCMS, IPO, MetFrag, CFMID, and Internal library search.
A huge thanks to @axelwalter and @maxulysse for helping with this release.
Minor patch release that includes:
- Fixed running the pipeline on AWS.
- Unused parameters were removed
- Template has been updated to nf-core/tools v1.14
The initial release of nf-core/metaboigniter pipeline.
This release includes:
- IPO parameter tuning
- mass trace detection using XCMS or OpenMS
- Retention time alignment and grouping
- Adduct and isotope detection
- Noise filtering using QC stability, blank filtering, and dilution series
- Metabolite identification using FINGER:ID, MetFrag, CFM-ID, and Internal library
- Normalization and transformation