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Use when you have vendor mass spectrometry raw files (e.g., .raw, .d, .
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Basé sur la classification professionnelle SOC
Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.
| name | aird-format-conversion-and-validation |
| description | Use when you have vendor mass spectrometry raw files (e.g., .raw, .d, . |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3357","edam_topics":["http://edamontology.org/topic_0121","http://edamontology.org/topic_3520","http://edamontology.org/topic_0769"],"tools":["AirdPro V5","AirdPro V6","ProteoWizard","AirdPro","Wine",".NET Framework 4.8","Docker Desktop","AirdSDK","Redis"],"techniques":["LC-MS"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1186/s12859-021-04490-0","title":"aird"}] |
| evidence_spans | ["AirdPro V5 is now available at 2023.7","AirdPro V6 is now available at 2024.4","pwiz_bindings_cli.dll from the ProteoWizard project"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_aird","doi":"10.1186/s12859-021-04490-0","title":"aird"}],"dedup_kept_from":"coll_aird"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1186/s12859-021-04490-0","all_source_dois":["10.1186/s12859-021-04490-0"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Convert vendor mass spectrometry raw files to the Aird format using AirdPro CLI or GUI, and validate the output by verifying file integrity, format compliance, and conversion completion. Aird is a computation-oriented format that enables higher compression ratios and faster decoding compared to standard formats like mzML.
You have vendor mass spectrometry raw files (e.g., .raw, .d, .wiff2 formats) from instruments supported by ProteoWizard MSConvert, and you need to convert them to Aird format for downstream analysis that benefits from improved compression, faster XIC computation, or distributed batch processing via Redis. This is especially relevant for large-scale DDA, DIA/SWATH, PRM, or MRM acquisition datasets where storage and computational efficiency are priorities.
Launch the AirdPro CLI or GUI container (V5 or V6) with Docker, mounting input vendor raw files and an output directory. Specify the acquisition method (DDA, DIA_SWATH, PRM, MRM, or COMMON), precision threshold (default 0.0001 m/z), and compression strategy. For first-run execution, allocate ≥30 minutes for Wine initialization and .NET Framework 4.8 component download in the container. Execute the conversion and monitor for completion. Validate the output by confirming: (1) paired .aird data file and .json index file exist in the output directory with matching base filenames; (2) file sizes are non-zero and consistent with source complexity; (3) file format signatures match Aird specification (index suffix .json, data suffix .aird); (4) no conversion exceptions or retrial loops occurred. Expect 20–30% performance degradation when running through Wine on Linux/Mac.
docker run --rm -v /path/to/vendor/files:/input -v /path/to/output:/output airdpro:cli /app/run-cli.sh /input/sample.raw /output --type=DDA --mzPrecision=0.0001