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HolobiomicsLab

@holobiomicslab source repo

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1. ▌
   Metabolite Annotation At Scale 2 · holobiomicslab

   Use when you have extracted a large feature set of m/z values (hundreds to tens of thousands) from a Cardinal MSImagingExperiment object or similar MS dataset and need to assign putative metabolite identities using public structural databases.
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2. ▌
   Metabolite Intensity Alignment 2 · holobiomicslab

   Use when you have raw lipidomic and metabolomic data files generated by the Multi-ABLE method (high-pressure liquid chromatography–mass spectrometry output) and need to prepare them for multivariate analysis to detect differential lipids and metabolites across biological samples (e.
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3. ▌
   Modification Site Localization 2 · holobiomicslab

   Use when you have a pair of MS/MS spectra—one from a known compound and one from a structurally related modified (unknown) compound—and need to identify which atom(s) in the structure carry the modification.
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4. ▌
   Organism Dictionary Enrichment 2 · holobiomicslab

   Use when after organism names have been cleaned and standardized (e.g., via 1_cleaningOriginal.R and 4_cleaningTaxonomy.R) but before final integration of organism, structure, and reference data. Use it when you have a cleaned organism table (interim/tables/2_cleaned/organism/cleaned.tsv.
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5. ▌
   Pathway Level Activity Scoring 2 · holobiomicslab

   Use when you have a metabolite intensity matrix (samples × metabolites) with assigned annotations (peak IDs mapped to KEGG or ChEBI compound IDs), a metabolic pathway database, and need to rank pathways by activity level within experimental groups or comparisons.
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6. ▌
   Pipeline Prerequisite Checking 2 · holobiomicslab

   Use when before launching the DaDIA metabolomics pipeline or any multi-package workflow, when you have an R environment with potentially mixed or unknown package versions and need to confirm that R ≥4.0, XCMS ≥3.11.4, metaMS ≥1.25.
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7. ▌
   Plasma Serum Sample Comparison 2 · holobiomicslab

   Use when you have NMR-based metabolomics measurements from a cohort containing both plasma and serum samples with associated processing delay metadata (pre- and post-centrifugation times), and you need to determine whether metabolic parameter stability differs between the two sample types or to.
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8. ▌
   Precursor Fragment Ion Pairing 2 · holobiomicslab

   Use when you have raw LC-MS/MS data files (mzML/mzXML format from Thermo, Waters, or Bruker instruments) and a list of target compounds defined by precursor m/z values (and optionally retention time windows).
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9. ▌
   Repository Metadata Extraction 2 · holobiomicslab

   Use when when you need to inventory a collection of related web applications or tools distributed across multiple repositories, discover their live deployment URLs, trace their associated publications, and verify accessibility and metadata completeness.
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10. ▌
    Semantic Versioning Validation 2 · holobiomicslab

    Use when when you need to confirm that a generated or retrieved release artifact from a version control system (e.g., git tag v1.0.0) produces byte-for-byte or functionally equivalent outputs to the official release published on a platform (e.g., GitHub Releases) on a specific date.
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11. ▌
    Signal Smoothing Preprocessing 2 · holobiomicslab

    Use when when working with raw LC-HRMS profile-mode data containing noisy chromatographic signals, apply this skill before peak detection. Smoothing is particularly needed when the signal-to-noise ratio is low or when gradient-based peak detection would be compromised by high-frequency noise.
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12. ▌
    Sparse Feature Vector Handling 2 · holobiomicslab

    Use when when you have tandem mass spectra (mz/intensity pairs with precursor m/z) and need to train an interpretable model (e.
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13. ▌
    Spatial Coordinate Mapping Msi 2 · holobiomicslab

    Use when after loading and preprocessing a Cardinal MSImagingExperiment object (with normalized peaks and optional spatial segmentation results), and before conducting spatial statistical tests or co-localization analyses.
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14. ▌
    Spectral Library Matching Gnps 2 · holobiomicslab

    Use when you have MS2 product-ion spectra in open formats (.mzML or .mzXML) from public mass spectrometry datasets (e.g., from MassIVE with a valid accession) and need to identify chemical compounds by comparing fragmentation patterns against the GNPS reference spectral library.
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15. ▌
    Spectrum Feature Vectorization 2 · holobiomicslab

    Use when you have raw mass spectrometry spectra (peak lists or intensity arrays) that must be fed into a pre-trained deep learning model for substance classification (e.g., PS²MS for NPS detection).
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16. ▌
    Taxonomic Metadata Integration 2 · holobiomicslab

    Use when after organism name cleaning and standardization (via 1_cleaningOriginal.R and 4_cleaningTaxonomy.R) has produced a cleaned organism table (interim/tables/2_cleaned/organism/cleaned.tsv.gz), apply this skill to map those cleaned names to Open Tree of Life (OTL) identifiers.
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17. ▌
    Theoretical Mz Grid Generation 2 · holobiomicslab

    Use when you have a feature table from untargeted LC-MS (m/z, retention time, intensity) and need to annotate which observed m/z values correspond to isotopologues and adducts of the same neutral compound.
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18. ▌
    Scientific Task Formulation 2 · holobiomicslab

    Use when you encounter a published scientific article or software paper that makes claims about data processing, analysis, or results, but the reproducibility context is unclear, artifacts are scattered, or the connection between claims and outputs is not immediately evident.
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19. ▌
    Conformer Generation Rdkit 2 · holobiomicslab

    Use when when you have ionized adduct structures (SMILES or MOL format) from an ionization-state determination step and need to create an ensemble of relaxed 3D geometries for each molecule prior to expensive conformation filtering (e.g., ASE-ANI or quantum methods).
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20. ▌
    Consensus Mass Determination 2 · holobiomicslab

    Use when after constructing individual mass tracks from mzTree data bins and before alignment across samples.
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21. ▌
    JSON Spectral Format Parsing 2 · holobiomicslab

    Use when when you have raw mass spectrometry spectral data in JSON format from open mass spectra libraries (OMSLs) or other sources and need to validate structural completeness, check for required metadata fields (SMILES, InChI, InChIKey, precursor m/z, adduct), and separate spectra by acquisition.
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22. ▌
    Spectral Metadata Validation 2 · holobiomicslab

    Use when processing heterogeneous mass spectral datasets from multiple open libraries (e.g., MassBank, UNPD, GMD) where structural identifiers, precursor m/z, and adduct information are frequently incomplete or inconsistent across records.
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23. ▌
    Linear Axis Spatial Analysis 2 · holobiomicslab

    Use when you have deposited mass spectrometry imaging datasets in NetCDF (CDF) format with accompanying MATLAB workspace files (.
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24. ▌
    Matlab Data Structure Design 2 · holobiomicslab

    Use when you have multiple CDF files containing mass spectrometry imaging data (spectra, m/z arrays, and spatial coordinates) that need to be ingested into MATLAB for the DIMPLE pipeline or similar linear-axis mass spectrometry imaging analysis.
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25. ▌
    Random Missing Value Masking 2 · holobiomicslab

    Use when preparing ion image data for contrastive learning in mass spectrometry imaging (MSI), specifically when you need to augment single ion images into pairs of variants for encoder training in COL mode (co-localized ions) or as a base component of ISO mode (isotope ions).
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26. ▌
    Spatial Filtering Ion Images 2 · holobiomicslab

    Use when preparing raw ion image data from mass spectrometry imaging for deep learning-based representation learning.
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27. ▌
    Metabolite Set Activity Scoring 2 · holobiomicslab

    Use when you have log2-transformed, standardized peak intensity matrices with metabolite annotations (peak ID → KEGG/ChEBI IDs) and need to test whether groups of peaks co-vary systematically within known pathways or spectral groupings.
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28. ▌
    Molecular Descriptor Extraction 2 · holobiomicslab

    Use when when you have validated SMILES strings or canonical molecule objects from RDKit and need to convert them into the fixed-size numerical tensor format expected by a deep learning model (e.g., PS2MS).
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29. ▌
    Spectral Data Format Conversion 2 · holobiomicslab

    Use when you have raw LC–MS data in vendor-proprietary or uncorrected formats (e.g., .raw, .d) and need to perform targeted peak detection, retention-time correction, or automated quality metrics on identified compounds. The input files must be converted to .
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30. ▌
    Spectral File Format Conversion 2 · holobiomicslab

    Use when when importing raw mass spectrometry data from instrument vendors or public repositories in one format (e.g., mzML, mzXML) and needing to export it in another format (e.
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31. ▌
    Axis Label Format Customization 2 · holobiomicslab

    Use when when designing injection plate layouts in InjectionDesign and needing to display sample positions with clear, domain-appropriate labels on the y-axis (e.g., row identifiers, well coordinates, or sample indices).
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32. ▌
    Biotransformation Rule Encoding 2 · holobiomicslab

    Use when you have untargeted metabolomics data with unknown metabolite structures and need to generate plausible candidate products by systematically applying known enzymatic or chemical transformation rules.
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33. ▌
    Bruker Nmr Spectral Data Import 2 · holobiomicslab

    Use when you have raw Bruker NMR spectral files (from a Bruker instrument) in a directory and need to prepare them for automated metabolite identification and quantification using ASICS.
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34. ▌
    Candidate Metabolite Prediction 2 · holobiomicslab

    Use when you have an unknown compound's mass spectrum (m/z peaks and intensities) in positive or negative ion mode and need to identify candidate metabolites from a structure database.
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35. ▌
    Chromatographic Method Transfer 2 · holobiomicslab

    Use when you have retention time predictions from a source chromatographic method and need to predict retention times for a target chromatographic method, but have limited calibration data (10–100 molecules) measured on both methods.
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36. ▌
    Confidence Score Interpretation 2 · holobiomicslab

    Use when after executing forward inference on preprocessed mass spectrometry spectra with a deep learning model (e.g., PS²MS), when you have per-spectrum predictions with associated confidence scores or per-class probabilities.
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37. ▌
    Contrastive Loss Implementation 2 · holobiomicslab

    Use when training embeddings from MS/MS spectra and you need to simultaneously enforce: (1) discrimination between spectra with different structural properties via contrastive learning on peak information and metadata embeddings, and (2) accurate reconstruction of embeddings from peak features via.
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38. ▌
    J Coupling Multiplet Generation 2 · holobiomicslab

    Use when you have parsed metabolite identities with known spin-system coupling constants (J-values) and chemical shifts, and need to generate the theoretical multiplet patterns that will form the basis of a simulated 1D or 2D NMR spectrum.
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39. ▌
    Lipid Class Coverage Assessment 2 · holobiomicslab

    Use when when you have acquired a CCS reference library (such as DTCCSN2 for U13C labeled lipids) and need to verify that it contains the expected lipid classes, CCS values are physically plausible for ion mobility data, and coverage matches the library's advertised documentation before using it.
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40. ▌
    Lipid Class Stratified Analysis 2 · holobiomicslab

    Use when you have IM-MS lipidomics data with measured CCS values, samples spiked with U13C-labeled lipid internal standards (e.
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41. ▌
    Mass Spectral Feature Alignment 2 · holobiomicslab

    Use when when you have separate LC-MS peak tables for unlabeled (C12) and labeled (C13) isotope tracer experiments and need to identify which features correspond to the same metabolite across the two labeling conditions.
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42. ▌
    Mass Spectrum Database Matching 2 · holobiomicslab

    Use when you have centroided LC-MS/MS spectral data (in MGF, mzXML, mzML, or mzData format) and want to identify known or predicted natural product structures present in your sample.
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43. ▌
    Mass Spectrum Semantic Encoding 2 · holobiomicslab

    Use when when you have an unknown compound's mass spectrum (m/z peaks and intensities in .mgf or equivalent format) and need to identify structurally related metabolites from a reference database by computing similarity in learned semantic space rather than direct spectral matching.
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44. ▌
    Metabolite Structure Prediction 2 · holobiomicslab

    Use when you have a parent compound (or set of compounds) in SMILES, MOL, or SDF format and need to predict plausible metabolite structures and pathways in a specific biological context (mammalian Phase I/II metabolism, human gut microbiota, or soil/aquatic microbial degradation).
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45. ▌
    Metadata Annotation Integration 2 · holobiomicslab

    Use when you have chemical annotations (GNPS spectral library matches) assigned to MS/MS samples and a validated ReDU sample-information template (TSV) with categorical metadata (e.g., sample type, extraction method, ionization source).
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46. ▌
    Molecular Descriptor Extraction 3 · holobiomicslab

    Use when you have a collection of chemical structures in SMILES format and need to create paired structure–spectrum training data for a generative model, but do not have experimental MS/MS spectra available.
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47. ▌
    Ms Modification Site Evaluation 2 · holobiomicslab

    Use when after ModiFinder has generated modification site probability scores for an unknown compound by comparing its MS/MS spectrum to a known analog, and you have access to the true structure of the unknown compound (oracle mode) or a reference modification site annotation.
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48. ▌
    Ms Ms Spectral Library Matching 2 · holobiomicslab

    Use when you have experimental MS/MS spectra from nontargeted metabolomics data and need to assign molecular identities or identify structurally related analogs.
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49. ▌
    Ms2 Spectrum Format Preparation 2 · holobiomicslab

    Use when you have raw or unstructured MS2 spectral data (from untargeted tandem mass spectrometry experiments) and plan to run MS2MP inference for KEGG pathway prediction.
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50. ▌
    Nmr Spectral Feature Extraction 2 · holobiomicslab

    Use when when you have raw or preprocessed 1H NMR spectral tensors from flavor or chemical mixtures and need to generate high-level feature representations that capture both fine-grained local patterns (e.g., peak multiplet structure, coupling constants) and global spectral context (e.
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51. ▌
    Peak Quality Metric Computation 2 · holobiomicslab

    Use when you have completed XCMS preprocessing (getEIC() and fillPeaks()) on untargeted LC-MS metabolomics data and need to assign per-peak quality scores prior to manual curation, classifier training, or downstream statistical analysis.
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52. ▌
    Peak Removal Robustness Testing 2 · holobiomicslab

    Use when when validating a metabolomics pathway analysis method (particularly decomposition-based approaches like PLAGE) against data quality degradation, or when comparing robustness across methods (PLAGE vs. ORA vs. GSEA).
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53. ▌
    Qc Type Color Marker Assignment 2 · holobiomicslab

    Use when configuring a new injection-plate design template in InjectionDesign if you have multiple QC types to position on a plate and need to visually distinguish them in the final worksheet.
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54. ▌
    Random Forest Regression Tuning 2 · holobiomicslab

    Use when your MetaboSet object contains missing values (marked as NA) in the expression matrix after quality flagging, but you need complete data for multivariate analysis.
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55. ▌
    Rank Order Correlation Analysis 2 · holobiomicslab

    Use when when you have run a pathway ranking method (such as PALS/PLAGE) on clean metabolomics data and wish to assess how sensitive the resulting pathway activity rankings are to realistic data quality issues—specifically Gaussian noise and random peak dropout—which are prevalent in untargeted.
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56. ▌
    Retention Time Window Filtering 2 · holobiomicslab

    Use when you have mzML/mzXML chromatogram files from Thermo, Waters, or Bruker instruments and need to extract MS1 and MS2 scans matching both a target m/z value AND a known or suspected retention time range.
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57. ▌
    Separation Metric Normalization 2 · holobiomicslab

    Use when you have extracted retention times from top MS1 features across an LC-MS run and need a single, comparable metric to evaluate how effectively a gradient spreads compounds across the chromatographic window.
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58. ▌
    Spectral Data Normalization Tic 2 · holobiomicslab

    Use when apply TIC normalization when you have raw, unprocessed mass spectrometry data (Cardinal objects or imaging matrices with 10,000+ m/z features and 1,000+ spectra) where signal intensity varies across spatial locations or samples due to instrumental drift, uneven sample preparation, or.
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59. ▌
    Spectral Retrieval Ranking Task 2 · holobiomicslab

    Use when when you have pre-computed dense embeddings for query spectra (unknown compounds) and reference spectra (spectral library), and you need to rank library entries by similarity to each query for compound identification or structural similarity retrieval.
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60. ▌
    Spectral Scan Metadata Matching 2 · holobiomicslab

    Use when you have extracted MS1 and MS2 scans in mzML/mzXML format from raw chromatogram files and a structured metadata file (containing retention time, m/z, compound name, molecular weight, and annotation fields), and you need to pair each scan set with its corresponding chemical record to build.
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61. ▌
    Structure Organism Pair Binning 2 · holobiomicslab

    Use when when you have loaded a structure-organism pairs table from a natural products database (e.g., LOTUS) and need to answer questions about the distribution of chemical diversity—specifically, how many unique 2D structures appear in exactly 1 organism versus many organisms.
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62. ▌
    Theoretical Spectrum Generation 2 · holobiomicslab

    Use when you have a defined set of lipid species (identified by class and fatty acid composition) and need to create a high-throughput spectral library for mass spectrometry-based lipid identification.
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63. ▌
    Tracer Metabolite Pair Matching 2 · holobiomicslab

    Use when you have acquired LC-MS peak tables from both unlabeled (12C) and isotope-labeled (13C) samples from a stable isotope tracing experiment, paired with sample metadata distinguishing case and control groups, and you need to systematically identify which unlabeled features correspond to.
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64. ▌
    Usi Spectrum Identifier Parsing 2 · holobiomicslab

    Use when when you have a USI string (e.g., mzspec:MTBLS1124:QC07.
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65. ▌
    Vendor Instrument Data Handling 2 · holobiomicslab

    Use when you have acquired raw mass spectrometry data in vendor-proprietary formats (ThermoFisher, Agilent, or equivalent) and need to analyze it using MSThunder for unknown organic pollutant identification.
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66. ▌
    Distance Geometry Embedding 2 · holobiomicslab

    Use when when you have ionized adduct structures (in SMILES or MOL format) from an upstream ionization-state determination step and need to produce multiple low-energy 3D conformations for collision cross section prediction, metabolite annotation, or structure-property modeling.
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67. ▌
    Imms Data Format Conversion 2 · holobiomicslab

    Use when when you have raw Agilent MassHunter (.d) or UIMF IM-MS data files from drift tube (DT) or structure for lossless ion manipulations (SLIM) instruments and need to ingest them into a preprocessing pipeline that requires standardized in-memory or intermediate representations for.
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68. ▌
    Ionization State Prediction 2 · holobiomicslab

    Use when when you have SMILES strings representing neutral organic molecules and need to enumerate the likely protonated (e.g., [M+H]+) and deprotonated (e.
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69. ▌
    Chromatographic Peak Labeling 2 · holobiomicslab

    Use when you have a user-defined reference list of isolated, high-confidence chromatographic peaks (ground-truth) matched across multiple LC-HRMS samples, and you need to produce a labelled training dataset large enough to train a CNN model that discriminates true peaks from background noise and.
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70. ▌
    Cross Dataset Entry Filtering 2 · holobiomicslab

    Use when you have received MSBERT-preprocessed spectral data from GNPS, MoNA, or MTBLS1572 and need to ensure data integrity before training a spectral embedding model.
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71. ▌
    JSON Spectral Data Processing 2 · holobiomicslab

    Use when you have raw or semi-curated mass spectrometry spectral data in JSON, CSV, MSP, or MGF format from multiple open mass spectra libraries (OMSLs) and need to standardize field names, validate chemical identifiers (SMILES, InChI, InChIKey), remove duplicates, filter by quality criteria.
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72. ▌
    Mass Spectrometry Calibration 2 · holobiomicslab

    Use when after peak detection when you have a detected peaks table with m/z values and need to correct systematic mass drift or inaccuracy before peak alignment across multiple batches.
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73. ▌
    Metabolite Similarity Scoring 2 · holobiomicslab

    Use when you have an unknown compound's mass spectrum (m/z peaks and intensities) in .
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74. ▌
    Peak Border Center Estimation 2 · holobiomicslab

    Use when you have LC-HRMS chromatograms in retention time × m/z matrix format and need to automatically localize chromatographic peak positions and extents prior to matching against a reference peak library or generating CNN training instances.
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75. ▌
    R Function Workflow Execution 2 · holobiomicslab

    Use when you have raw lipidomic and metabolomic data files generated by the Multi-ABLE barocycler-based concurrent multiomics method and need to perform integrative preprocessing (spectral normalization and alignment across samples) followed by multivariate analysis to identify differential lipids.
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76. ▌
    Spectral Database Compilation 2 · holobiomicslab

    Use when you have generated predicted fragment spectra for a set of compounds using CFM-ID or similar in-silico prediction tools and need to organize these results into a persistent, queryable database that mirrors the schema of an existing online spectral reference (such as the predicted-fragments.
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77. ▌
    Imzml File Loading And Import 2 · holobiomicslab

    Use when when you have one or more imzML files containing mass spectrometry imaging data and need to import them into LipidQMap for ion image extraction, isotopic correction, and quantitative analysis.
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78. ▌
    Latent Feature Interpretation 2 · holobiomicslab

    Use when you have imaging mass spectrometry (IMS) data preprocessed into an h5py-backed feature matrix, and a trained graph-attention autoencoder has already extracted latent low-dimensional peak features.
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79. ▌
    Lipid Category Classification 2 · holobiomicslab

    Use when a spatial metabolomics dataset contains semicolon-delimited isomer name annotations (e.g., 'all_IsomerNames' column in SpaMTP Seurat objects) and you need to collapse multiple lipid nomenclature variants into their parent lipid categories and classes.
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80. ▌
    Per Root Metabolite Profiling 2 · holobiomicslab

    Use when when you have deposited mass spectrometry imaging datasets for plant roots in CDF format paired with pre-computed Matlab workspaces, and you need to reproduce per-root linear-axis metabolite profiling outputs to validate reported genotype-level results (e.
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81. ▌
    Fragmentation Pattern Annotation 2 · holobiomicslab

    Use when you have custom lipid species (not covered by the 500,000+ built-in LipidMatch entries) that you need to match against experimental MS/MS data, or you are extending LipidMatch's coverage for specialized lipid classes.
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82. ▌
    Mass Spectral Network Annotation 2 · holobiomicslab

    Use when you have a GNPS-generated molecular network (graphml or JSON format) and corresponding MS2LDA experiment results or chemical class assignments, and you want to annotate network nodes with substructural motifs or chemical classes to facilitate structural interpretation and identify.
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83. ▌
    Annotation Complexity Comparison 2 · holobiomicslab

    Use when you have MS imaging or LC-MS data with pre-annotated m/z values that include multiple isomer or metabolite names per m/z (stored as semicolon-delimited or multi-record strings), and you want to measure whether a refinement step (e.
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84. ▌
    Ccs Prediction Model Application 2 · holobiomicslab

    Use when you have structural input data (SMILES or molecular geometry files) for N-Me derived unsaturated sterol lipids and need to generate a predicted CCS dataset indexed by lipid identifier and structural isomer class.
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85. ▌
    Chemical Space Structure Ranking 2 · holobiomicslab

    Use when you have an unknown compound's mass spectrum (m/z peaks and intensities in .
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86. ▌
    Chemical Structure Deduplication 2 · holobiomicslab

    Use when after applying biotransformation rules to generate candidate product structures from input molecules, when the same transformed structure can be produced via multiple transformation pathways or rule applications, and you need a single canonical representation while tracking which rules and.
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87. ▌
    Chemical Structure Featurization 2 · holobiomicslab

    Use when when you have molecular structures (SMILES, SDF, or molecular graphs) and need to train a spectrum prediction or molecular property model, and you want to learn task-specific representations rather than use fixed descriptors.
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88. ▌
    Feature Detection Rate Filtering 2 · holobiomicslab

    Use when after constructing a MetaboSet object with LC-MS peak abundances, sample metadata (pData with QC labels), and feature metadata (fData), and after marking missing values as NA.
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89. ▌
    Feature Group Isotope Annotation 2 · holobiomicslab

    Use when you have a feature table from nontargeted LC-MS peak detection (containing m/z, retention time, and intensity values) and need to disambiguate whether detected features represent the same molecular entity under different ionization/modification states or are true independent signals.
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90. ▌
    Fragmentation Pattern Annotation 3 · holobiomicslab

    Use when when you have an experimental MS/MS spectrum (query spectrum as m/z–intensity pairs) and need to identify the compound by comparing its fragmentation pattern to a spectral library.
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91. ▌
    Ion Image Embedding Optimization 2 · holobiomicslab

    Use when you have 512-dimensional representation vectors output from ResNet18 encoders processing paired augmented ion images, and you need to prevent trivial solutions (representation collapse) during contrastive learning—specifically when optimizing for maximized similarity between augmentations.
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92. ▌
    Mass Spectral Feature Annotation 2 · holobiomicslab

    Use when you have m/z values from spatially-resolved mass spectrometry imaging (e.g., MALDI-MSI, DESI-MSI) and need to assign molecular formulae to thousands of features with higher precision than traditional LC-MS approaches.
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93. ▌
    Mass Spectrum Peak Normalization 2 · holobiomicslab

    Use when when comparing two or more MSMS spectra using intensity-weighted similarity measures (cosine similarity, modified cosine, or neutral loss similarity), and the spectra have been acquired under different instrumental conditions, ionization efficiencies, or detector gains that produce.
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94. ▌
    Mass Track Consensus Computation 2 · holobiomicslab

    Use when after mass tracks have been aligned across all samples (either via pairwise alignment for ≤10 samples or nearest-neighbor clustering for larger cohorts), and you need to generate a single representative m/z per aligned bin for downstream feature extraction and annotation.
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95. ▌
    Metabolite Dataset Preprocessing 2 · holobiomicslab

    Use when you have raw NMR metabolomics measurements paired with pre-analytical metadata (e.g., processing delay times, sample type designations [plasma vs. serum], cohort identifiers) and need to investigate how delays affect measured metabolic parameters.
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96. ▌
    Metabolite Identifier Annotation 2 · holobiomicslab

    Use when you have observed compounds (from LC-MS/MS, GC-MS, NMR, or other analytical techniques) with unknown identity and you want to assign candidate metabolite structures by comparing them to computationally predicted metabolism pathways.
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97. ▌
    Metabolomics Matrix Manipulation 2 · holobiomicslab

    Use when you have a raw metabolomics abundance table (e.g., LC/MS or GC/MS peak intensities or concentrations) with non-normal distributions and missing values, and you need to prepare it for Gibbs sampler or other model-based imputation.
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98. ▌
    Missing Data Simulation In Omics 2 · holobiomicslab

    Use when when comparing the robustness of multiple pathway ranking methods (e.g., PLAGE, ORA, GSEA) on metabolomics or other omics data, and you need to establish which method is least sensitive to peak dropout, instrumental noise, or annotation uncertainty.
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99. ▌
    Model Inference And Tokenization 2 · holobiomicslab

    Use when you have MS/MS spectra in .msp format and need to retrieve similar compounds or compute spectral similarities for compound identification. The input spectra should already be cleaned (malformed or invalid SMILES removed), and you have access to a pre-trained SpecEmbedding model checkpoint.
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100. ▌
     Msms Spectral Database Retrieval 2 · holobiomicslab

     Use when when you have a target compound (modified or unmodified) and need to obtain its experimental MS/MS spectrum and metadata to serve as a known reference for ModiFinder analysis, or when benchmarking evaluation methods like average_distance scoring.
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