metabolomics-data-format-handling
Summary
Loading and preparing raw LC-MS metabolomics data in standard open formats (mzML, NetCDF) for downstream feature detection and quantification. This skill ensures data integrity and compatibility across the MetaboAnalystR 4.0 processing pipeline.
When to use
You have raw LC-MS data in mzML or equivalent binary format from a public repository (MetaboLights, MassIVE) or instrument vendor output, and need to ingest it into MetaboAnalystR 4.0 for unified LC-MS1 feature detection and MS/MS spectra processing. Use this skill as the entry point before executing the auto-optimized feature detection module.
When NOT to use
- Input data is already a processed feature table (CSV, TSV with rows=features, columns=samples); skip directly to statistical analysis or functional interpretation modules.
- Data is in proprietary vendor binary format and no mzML/NetCDF conversion tool is available; conversion must be completed externally first (e.g., using ProteoWizard msConvert).
- Input is already quality-controlled and feature-detected by an external pipeline; load the feature table instead of raw scans.
Inputs
- raw LC-MS data file in mzML format
- raw LC-MS data file in NetCDF format
- vendor-proprietary LC-MS raw data (requires prior conversion to mzML/NetCDF)
Outputs
- loaded LC-MS data object (MetaboAnalystR internal format)
- parsed scan metadata (retention time, m/z, intensity arrays)
- ready-to-process data structure for feature detection module
How to apply
Load raw LC-MS data (mzML or equivalent format) into MetaboAnalystR 4.0 using the package's data import functions. Verify that the input file contains both LC-MS1 scans (for quantification) and, optionally, MS/MS spectra (for compound annotation via DDA or DIA acquisition modes). The loaded data will be parsed into an internal object representation that the unified LC-MS workflow can consume. MetaboAnalystR 4.0 will handle vendor-specific format conversion and normalization transparently. No manual format conversion or external tools are required if input is already in mzML; for other formats, ensure conversion to mzML before import.
Related tools
Evaluation signals
- Data object successfully instantiated in R environment without parsing errors or warnings
- Scan count and sample count are consistent with experiment design (≥1 sample, ≥10 scans per sample typical for LC-MS)
- m/z values span expected mass range (50–1500 m/z common for metabolomics; no negative or zero m/z)
- Intensity arrays are numeric, non-negative, and contain variability across scans (not all zeros or constants)
- Retention time values are monotonically increasing or form realistic scan order (no future-dated or negative times)
Limitations
- Metadata (instrument type, acquisition parameters, sample annotations) embedded in mzML headers may be incomplete or non-standard; manual curation of experimental design files is often required.
- Large files (>10 GB raw mzML) may cause memory pressure during import; consider splitting into separate batches or using the MetaboAnalyst web server API for large-scale processing.
- Vendor-specific m/z calibration or lock-mass corrections are not automatically applied; users must verify calibration accuracy before peak detection.
- Data from unconventional acquisition modes (e.g., targeted monitoring, ion mobility spectrometry) may not be fully supported by the unified LC-MS workflow; DDA and DIA are the primary supported acquisition modes.
Evidence
- [other] Load raw LC-MS data (mzML or equivalent format) from a public repository (MetaboLights or MassIVE) into MetaboAnalystR 4.0.: "Load raw LC-MS data (mzML or equivalent format) from a public repository (MetaboLights or MassIVE) into MetaboAnalystR 4.0."
- [readme] MetaboAnalystR 4.0 contains the R functions and libraries underlying the popular MetaboAnalyst web server, including metabolomic data analysis, visualization, and functional interpretation.: "MetaboAnalystR 4.0 contains the R functions and libraries underlying the popular MetaboAnalyst web server, including metabolomic data analysis, visualization, and functional interpretation."
- [readme] an auto-optimized feature detection and quantification module for LC-MS1 spectra processing: "an auto-optimized feature detection and quantification module for LC-MS1 spectra processing"
- [readme] a streamlined MS/MS spectra deconvolution and compound annotation module for both data-dependent acquisition (DDA) or data-independent acquisition (DIA): "a streamlined MS/MS spectra deconvolution and compound annotation module for both data-dependent acquisition (DDA) or data-independent acquisition (DIA)"
1---2name: metabolomics-data-format-handling-33description: Use when you have raw LC-MS data in mzML or equivalent binary format from a public repository (MetaboLights, MassIVE) or instrument vendor output, and need to ingest it into MetaboAnalystR 4.0 for unified LC-MS1 feature detection and MS/MS spectra processing.4license: CC-BY-4.05---67# metabolomics-data-format-handling89## Summary1011Loading and preparing raw LC-MS metabolomics data in standard open formats (mzML, NetCDF) for downstream feature detection and quantification. This skill ensures data integrity and compatibility across the MetaboAnalystR 4.0 processing pipeline.1213## When to use1415You have raw LC-MS data in mzML or equivalent binary format from a public repository (MetaboLights, MassIVE) or instrument vendor output, and need to ingest it into MetaboAnalystR 4.0 for unified LC-MS1 feature detection and MS/MS spectra processing. Use this skill as the entry point before executing the auto-optimized feature detection module.1617## When NOT to use1819- Input data is already a processed feature table (CSV, TSV with rows=features, columns=samples); skip directly to statistical analysis or functional interpretation modules.20- Data is in proprietary vendor binary format and no mzML/NetCDF conversion tool is available; conversion must be completed externally first (e.g., using ProteoWizard msConvert).21- Input is already quality-controlled and feature-detected by an external pipeline; load the feature table instead of raw scans.2223## Inputs2425- raw LC-MS data file in mzML format26- raw LC-MS data file in NetCDF format27- vendor-proprietary LC-MS raw data (requires prior conversion to mzML/NetCDF)2829## Outputs3031- loaded LC-MS data object (MetaboAnalystR internal format)32- parsed scan metadata (retention time, m/z, intensity arrays)33- ready-to-process data structure for feature detection module3435## How to apply3637Load raw LC-MS data (mzML or equivalent format) into MetaboAnalystR 4.0 using the package's data import functions. Verify that the input file contains both LC-MS1 scans (for quantification) and, optionally, MS/MS spectra (for compound annotation via DDA or DIA acquisition modes). The loaded data will be parsed into an internal object representation that the unified LC-MS workflow can consume. MetaboAnalystR 4.0 will handle vendor-specific format conversion and normalization transparently. No manual format conversion or external tools are required if input is already in mzML; for other formats, ensure conversion to mzML before import.3839## Related tools4041- **MetaboAnalystR** (Data import and unified LC-MS workflow execution environment for loading and processing mzML/NetCDF files) — https://github.com/xia-lab/MetaboAnalystR4243## Evaluation signals4445- Data object successfully instantiated in R environment without parsing errors or warnings46- Scan count and sample count are consistent with experiment design (≥1 sample, ≥10 scans per sample typical for LC-MS)47- m/z values span expected mass range (50–1500 m/z common for metabolomics; no negative or zero m/z)48- Intensity arrays are numeric, non-negative, and contain variability across scans (not all zeros or constants)49- Retention time values are monotonically increasing or form realistic scan order (no future-dated or negative times)5051## Limitations5253- Metadata (instrument type, acquisition parameters, sample annotations) embedded in mzML headers may be incomplete or non-standard; manual curation of experimental design files is often required.54- Large files (>10 GB raw mzML) may cause memory pressure during import; consider splitting into separate batches or using the MetaboAnalyst web server API for large-scale processing.55- Vendor-specific m/z calibration or lock-mass corrections are not automatically applied; users must verify calibration accuracy before peak detection.56- Data from unconventional acquisition modes (e.g., targeted monitoring, ion mobility spectrometry) may not be fully supported by the unified LC-MS workflow; DDA and DIA are the primary supported acquisition modes.5758## Evidence5960- [other] Load raw LC-MS data (mzML or equivalent format) from a public repository (MetaboLights or MassIVE) into MetaboAnalystR 4.0.: "Load raw LC-MS data (mzML or equivalent format) from a public repository (MetaboLights or MassIVE) into MetaboAnalystR 4.0."61- [readme] MetaboAnalystR 4.0 contains the R functions and libraries underlying the popular MetaboAnalyst web server, including metabolomic data analysis, visualization, and functional interpretation.: "MetaboAnalystR 4.0 contains the R functions and libraries underlying the popular MetaboAnalyst web server, including metabolomic data analysis, visualization, and functional interpretation."62- [readme] an auto-optimized feature detection and quantification module for LC-MS1 spectra processing: "an auto-optimized feature detection and quantification module for LC-MS1 spectra processing"63- [readme] a streamlined MS/MS spectra deconvolution and compound annotation module for both data-dependent acquisition (DDA) or data-independent acquisition (DIA): "a streamlined MS/MS spectra deconvolution and compound annotation module for both data-dependent acquisition (DDA) or data-independent acquisition (DIA)"
HolobiomicsLab 