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HolobiomicsLab

@holobiomicslab source repo

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1. ▌
   Resnet Architecture Modification For Dimensionality Cont 2 · holobiomicslab

   Use when your task requires a pretrained convolutional encoder (ResNet18) to produce fixed-size representation vectors of a specific dimensionality (e.g., 512 dimensions) rather than the default output size.
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2. ▌
   Mass Track Clustering 2 · holobiomicslab

   Use when after constructing initial data bins from mzTree (indexed by int(mz × 1000)), determine whether a single bin contains one or multiple mass tracks. Apply clustering when the m/z range of points in a bin exceeds 2 × ppm tolerance (e.
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3. ▌
   Peak Shape Assessment 2 · holobiomicslab

   Use when after peak detection in a nontargeted LC-MS workflow when you have a feature table with detected peaks and need to filter low-quality features or understand why certain features have inconsistent intensity or poor annotation confidence.
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4. ▌
   Spectrum Data Loading 2 · holobiomicslab

   Use when when you have a USI (e.g., mzspec:MTBLS1124:QC07.mzML) pointing to a public mzML or related spectrum file in MetaboLights, MassIVE, or GNPS repositories, and need to load the spectrum data for interactive visualization, quality control assessment, or downstream analysis.
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5. ▌
   Xic Marker Annotation 2 · holobiomicslab

   Use when when you have a resolved spectrum file (mzML, mzXML) and need to visualize where MS2 precursor scans occur on an XIC display.
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6. ▌
   JSON Schema Validation 2 · holobiomicslab

   Use when after extracting tabular data into intermediate JSON representation (via tagging), or after manual JSON editing, and before converting to a target format (e.g., mwTab for Metabolomics Workbench).
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7. ▌
   File Format Validation 2 · holobiomicslab

   Use when after writing parsed spectra to a new MSP file using mssearchr's MSP writer, or when integrating MSP files from external sources into an R analysis pipeline.
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8. ▌
   Mzml Format Validation 2 · holobiomicslab

   Use when after downloading an mzML file from a remote repository (e.g., MetaboLights, MassIVE, GNPS) via USI resolution, before attempting to parse it into a spectrum container or visualization dashboard.
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9. ▌
   Peak M Z Recalibration 2 · holobiomicslab

   Use when after peak detection when you have a table of detected peaks with m/z values and need to improve mass accuracy for downstream annotation.
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10. ▌
    Build System Monitoring 2 · holobiomicslab

    Use when when you need to capture a snapshot of a research software project's health metrics from multiple CI/CD and repository services (Travis CI, Landscape.
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11. ▌
    Ccs Bias Quantification 2 · holobiomicslab

    Use when you have IM-MS lipidomics data acquired on samples spiked with fully labeled U13C lipid standards (e.g., U13C yeast extract), and you need to assess whether systematic CCS deviation exists between your instrument's measured values and the DT CCS N2 reference library for U13C labeled lipids.
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12. ▌
    Data Summary Statistics 2 · holobiomicslab

    Use when you have a curated relational dataset (structure-organism pairs) and need to quantify how structures distribute across a categorical variable (e.g., organism prevalence).
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13. ▌
    Lcms Feature Extraction 2 · holobiomicslab

    Use when when you have raw LC-MS chromatographic data (mzML or vendor format) and need to identify and characterize all detectable peaks across the full retention time range for untargeted metabolomics or discovery workflows.
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14. ▌
    Lipid Class Enumeration 2 · holobiomicslab

    Use when you have a lipid identification or library-generation task that requires you to define a target chemical space bounded by lipid classes (e.g., phosphatidylcholine, triglyceride) and fatty acid composition ranges (e.g., C14–C22 with 0–6 degrees of unsaturation).
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15. ▌
    M Z Rt Feature Matching 2 · holobiomicslab

    Use when you have extracted peaks from multiple LC/HRMS batches (n > 1) with their m/z and RT values, and you need to identify and align peaks representing the same compound across batches to build a consensus feature matrix.
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16. ▌
    Massql Query Generation 2 · holobiomicslab

    Use when you have trained a shallow decision tree on ChemEcho feature vectors (representing unique peak or neutral loss formulas from tandem MS spectra) and need to deploy the learned splitting logic as a queryable, inspectable artifact.
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17. ▌
    Metabolite Set Analysis 2 · holobiomicslab

    Use when you have a metabolite intensity matrix (rows=metabolites or peaks, columns=samples) paired with metabolite-to-pathway or metabolite-to-feature-group annotations, and you want to score activity levels across pathways or metabolite groupings in a way that tolerates missing peaks and.
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18. ▌
    Mse Reconstruction Loss 2 · holobiomicslab

    Use when training embeddings from multi-modal spectral data (peak information + metadata) where you need to ensure both contrastive discriminability AND reconstruction fidelity. Specifically use it in transformer-based architectures that produce embeddings from heterogeneous inputs (e.
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19. ▌
    Spectral Data Retrieval 2 · holobiomicslab

    Use when you have a USI string (e.g., mzspec:GNPS:TASK-d93bdbb5cdda40e48975e6e18a45c3ce-f.mwang87/data/... or mzspec:MSV000084951:AH22) and need to extract the corresponding mass spectrum peak list (m/z and intensity pairs) for downstream analysis, visualization, or cross-repository comparison.
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20. ▌
    Spectral Token Design 2 · holobiomicslab

    Use when when you need to prepare mass spectra and molecular structures for joint modeling in a BART or transformer-based sequence model, and you lack a unified representation scheme that allows both modalities to be encoded and decoded without collision or information loss.
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21. ▌
    Python Environment Setup 2 · holobiomicslab

    Use when you have cloned a scientific repository containing Python code (scripts, Jupyter notebooks, or module imports) and need to execute it locally or on new hardware.
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22. ▌
    Badge Endpoint Retrieval 2 · holobiomicslab

    Use when when you need to verify the current operational status of a software project across multiple dimensions (CI/CD, code quality, test coverage, containerization, archival) and those status indicators are exposed as badge endpoints in the project's README.
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23. ▌
    Gnps Mgf Format Handling 2 · holobiomicslab

    Use when you have mass spectrometry MS/MS spectral data in GNPS-style MGF format and need to feed it into the Mass2SMILES deep learning model for structure and functional group prediction.
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24. ▌
    Gnps Repository Querying 2 · holobiomicslab

    Use when when you have a USI string (e.g., mzspec:GNPS:TASK-d93bdbb5cdda40e48975e6e18a45c3ce-...
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25. ▌
    Lcms Data Format Parsing 2 · holobiomicslab

    Use when when you have raw LC/MS data in mzML format and need to execute the LAGF non-targeted screening pipeline. Use this skill as the first step before applying the LAGF algorithm workflow to extract and annotate features from mass spectrometry data.
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26. ▌
    Ms2 Precursor Extraction 2 · holobiomicslab

    Use when when you have a resolved spectrum file in mzML or mzXML format and need to identify where MS2 (tandem mass spectrometry) scans occur within an LC-MS run, particularly to annotate XIC displays with MS2 precursor positions or validate that MS2 events align with expected chromatographic.
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27. ▌
    Peak Formula Enumeration 2 · holobiomicslab

    Use when when you have tandem mass spectra (mz/intensity pairs with precursor m/z) and need to train interpretable machine learning models—particularly decision trees or regression models—where each feature must correspond to a concrete chemical entity (peak or neutral loss) rather than a latent.
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28. ▌
    Peak Table Harmonization 2 · holobiomicslab

    Use when when you have extracted peak tables from multiple independent LC/HRMS analysis batches (each with retention time and m/z values) and need to align peaks across batches to create a unified feature matrix for downstream statistical analysis.
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29. ▌
    Spectral Noise Filtering 2 · holobiomicslab

    Use when immediately after extracting ion chromatograms (EICs) by binning mass spectral data across the full m/z range from raw LC/HRMS files (mzML, mzXML, or netCDF format).
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30. ▌
    Imzml Metadata Parsing 2 · holobiomicslab

    Use when you have received paired .imzML (XML metadata) and .ibd (binary data) files from an Imaging Mass Spectrometry instrument and need to discover the imaging geometry, m/z calibration, and scan coordinate system before extracting mass images or computing total ion chromatograms (TIC).
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31. ▌
    Retention Time Prediction 2 · holobiomicslab

    Use when you have a set of small-molecule structures (SMILES, InChI, or SDF format) and need to predict their chromatographic retention times for a specific method, either to support metabolite annotation, validate experimental assignments, or project RTs across different chromatographic platforms.
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32. ▌
    Candidate Formula Ranking 2 · holobiomicslab

    Use when you have an observed m/z value from mass spectrometry imaging and need to assign a chemical formula with high confidence.
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33. ▌
    Chemical Shift Annotation 2 · holobiomicslab

    Use when when you have preprocessed 1H NMR spectral data with unidentified peaks and need to determine metabolite identity by exploiting the correlation structure of NMR signals.
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34. ▌
    File Integrity Validation 2 · holobiomicslab

    Use when you have a raw NV (NMRViewJ) binary file and need to confirm it is well-formed before parsing or processing.
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35. ▌
    Infoence Loss Composition 2 · holobiomicslab

    Use when when training embeddings from MS/MS spectra data where you need both discriminative power (to distinguish similar spectra) and reconstruction accuracy (to preserve peak and metadata information).
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36. ▌
    Input Type Classification 2 · holobiomicslab

    Use when a web application receives mass spectrometry data through heterogeneous identifier formats and must automatically determine which loader (Task ID, USI, or FBMN) should process the input.
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37. ▌
    Khipu Grid Offset Mapping 2 · holobiomicslab

    Use when you have a set of ions detected in LC-MS data that are suspected to derive from the same neutral compound via different isotope and adduct patterns. Use this skill after ions have been matched to isotope and adduct patterns and assigned to grid positions (isotope row and adduct column).
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38. ▌
    Metabolic Network Mapping 2 · holobiomicslab

    Use when you have an untargeted metabolomics feature table (with m/z, retention time, and statistical significance values) and want to predict which metabolic pathways and functional modules are active in your sample, but you lack confident metabolite identifications or wish to bypass the.
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39. ▌
    Mrm Transition Extraction 2 · holobiomicslab

    Use when you have raw LC-MS/MS data in MRM acquisition mode and need to systematically identify and catalog all precursor m/z and corresponding product m/z values for each transition monitored during data collection.
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40. ▌
    Organism Taxonomy Mapping 2 · holobiomicslab

    Use when you have a cleaned organism table (with validated, deduplicated organism names from sources like NCBI, manual curation, or previous cleaning steps) and need to annotate each organism with Open Tree of Life identifiers, taxonomic lineage, and standardized taxonomic annotations.
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41. ▌
    Otl Identifier Assignment 2 · holobiomicslab

    Use when after organism name cleaning and taxonomy verification (4_cleaningTaxonomy.R) have been completed and you have a cleaned organism table with standardized names.
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42. ▌
    Siamese Network Inference 2 · holobiomicslab

    Use when you have a collection of cleaned MS/MS spectra (in formats like mzML, mgf, msp, mzxml, or json) and need to predict molecular structural similarities between spectrum pairs without pre-computing RDKit fingerprints.
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43. ▌
    Smiles Parsing Validation 2 · holobiomicslab

    Use when when you have a dataset of molecular structures encoded as SMILES strings that will be processed downstream (e.
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44. ▌
    Spectral Library Matching 2 · holobiomicslab

    Use when you have one or more MS/MS spectra in .mgf format (or USI identifiers) and need to: (1) identify unknowns by searching against domain-curated reference data; (2) assign Level 2 metabolomics annotations via GNPS library matches; (3) aggregate matches across organism lineages (e.
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45. ▌
    Tree To Query Translation 2 · holobiomicslab

    Use when you have a shallow decision tree trained on ChemEcho feature vectors (peak or neutral loss formulas) and need to deploy it as an executable query against tandem mass spectrometry data.
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46. ▌
    Batch Record Processing 2 · holobiomicslab

    Use when when you have a repository containing hundreds or thousands of structured records (e.g., MassBank records in standardized format) that must be validated for correctness before release or merge.
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47. ▌
    Docker Image Deployment 2 · holobiomicslab

    Use when when you have a containerized scientific tool available on Docker Hub (e.
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48. ▌
    Mz Binning And Indexing 2 · holobiomicslab

    Use when immediately after parsing mzML files into (m/z, scan_number, intensity) tuples when you need to build mass tracks from raw MS1 spectra. Use it when working with high-resolution instruments (e.
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49. ▌
    Cdf File Format Parsing 2 · holobiomicslab

    Use when when you have multiple CDF files from mass spectrometry imaging experiments (e.g., root tissue MALDI-MS data) that need to be ingested into Matlab for linear imaging analysis. Trigger: presence of .
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50. ▌
    Lipid Database Querying 2 · holobiomicslab

    Use when you have acquired full-scan mass spectrometry imaging data (e.g., from a mouse bladder or tissue section) with detected m/z features and want to assign chemical identities to those features by querying a structured lipid database.
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51. ▌
    Ms2 Spectrum Consolidation 2 · holobiomicslab

    Use when after peak picking, sample alignment, and isotopologue/adduct grouping are complete, and you have DDA-MS2 scans associated with grouped feature ions.
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52. ▌
    Binary File Format Parsing 2 · holobiomicslab

    Use when you encounter a proprietary or undocumented binary file (e.
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53. ▌
    Breath Biomarker Discovery 2 · holobiomicslab

    Use when you have GC–MS data from human breath samples and need to identify marker metabolites for disease diagnosis, phenotyping, or biomarker discovery without a predefined target list. Your data is noisy or conventional peak picking has produced high false-positive rates.
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54. ▌
    Candidate Ranking By Score 2 · holobiomicslab

    Use when you have a query mass spectrum and a set of candidate molecular structures, and you need to prioritize candidates by their likelihood of matching the query. Typical triggers include: (1) you have computed or extracted spectral features (e.
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55. ▌
    Ccs Library Format Parsing 2 · holobiomicslab

    Use when when you have received or cloned a CCS reference library (such as the DTCCSN2 library for U13C labeled lipids) bundled with lipidomics software and need to verify its integrity, understand its lipid class composition, or extract metadata before using it for CCS bias calculation or.
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56. ▌
    Compound Identifier Lookup 2 · holobiomicslab

    Use when you have an experimental MS/MS spectrum (m/z and intensity pairs in mzML/mzXML format from DDA or targeted acquisition on Thermo, Waters, or Bruker instruments) and need to identify the unknown compound by comparing it against a reference database.
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57. ▌
    Docker Container Execution 2 · holobiomicslab

    Use when you have GNPS-style MGF spectral files as input and need to run Mass2SMILES MS/MS-to-structure inference without installing TensorFlow, CUDA, or Python dependencies locally.
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58. ▌
    Dual Branch Feature Fusion 2 · holobiomicslab

    Use when when you have molecular input data available in two or more distinct formats (e.g., RDKit-extracted fingerprints AND torch_geometric Graph objects representing molecular topology) and your prediction target (e.
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59. ▌
    Feature Annotation Mapping 2 · holobiomicslab

    Use when you have a trained decision tree model on ChemEcho sparse feature vectors and need to convert a specific decision path (root to leaf) into a deployable query.
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60. ▌
    Gradient Flow Verification 2 · holobiomicslab

    Use when after implementing a composite loss function that combines multiple loss terms (e.g., InfoNCE contrastive loss and MSE reconstruction loss) in a PyTorch module, and before running full-scale training on MS/MS spectra data.
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61. ▌
    Jupyter Notebook Execution 2 · holobiomicslab

    Use when when you have three coordinated mass spectrometry data tables (quantification, metadata, spectral) ready for integrated preprocessing and statistical analysis, and you need to generate a standardized JSON artifact for downstream interactive exploration rather than static tabular outputs.
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62. ▌
    Large Scale Data Retrieval 2 · holobiomicslab

    Use when you have a query mass spectrum (or a metabolite reference spectrum from public data) and need to search it against a large-scale spectral repository (≥billions of spectra, e.g., GNPS library) where execution time and resource efficiency are critical.
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63. ▌
    Lipid Nomenclature Mapping 2 · holobiomicslab

    Use when you need to generate a comprehensive, non-redundant inventory of lipid species that span a defined lipid class (e.g., phosphatidylcholine, triacylglycerol) and a range of fatty acid compositions (e.g., C14:0 to C22:6).
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64. ▌
    Lipid Spectral Data Export 2 · holobiomicslab

    Use when after generating a complete lipid spectral library with adduct-specific fragmentation patterns and retention time metadata, when you need to deploy the library for targeted or data-dependent acquisition on specific mass spectrometry instruments—either Excalibur-controlled orbitrap.
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65. ▌
    Metabolite To Gene Mapping 2 · holobiomicslab

    Use when you have metabolomic data (e.g., from LC-MS or GC-MS comparing patient to controls) showing differential abundant metabolites (DAMs), candidate genes from exome sequencing or variant calling, and access to a protein–protein or gene–gene interaction network (e.g., STRING).
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66. ▌
    Model Artifact Persistence 2 · holobiomicslab

    Use when after a deep neural network model has completed training on LC-MS spectral peak classification data and you need to preserve the learned weights and architecture for downstream inference, validation on held-out test sets, or sharing with collaborators.
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67. ▌
    Nmr Spin System Simulation 2 · holobiomicslab

    Use when when you have known metabolite concentrations and their spin-system coupling constants (J-values) and need to generate synthetic ¹H NMR spectra for method validation, metabolite library construction, or as a training dataset.
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68. ▌
    Pytorch Module Composition 2 · holobiomicslab

    Use when when you need to extract both local spatial patterns and global long-range dependencies from sequential or spectral data (e.g., 1H NMR spectra), and neither CNNs nor Transformers alone are sufficient.
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69. ▌
    Pytorch Module Development 2 · holobiomicslab

    Use when when constructing a composite loss function for contrastive learning on structured data (e.
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70. ▌
    REST API Endpoint Querying 2 · holobiomicslab

    Use when when you need to verify that a deployed model service (such as TensorFlow Serving) is running and exposing the correct input and output layer names before sending classification requests.
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71. ▌
    Sample Metadata Extraction 2 · holobiomicslab

    Use when when you have an Excel file uploaded by a user following the InjectionDesign template schema and need to convert it into a modifiable, structured sample list that preserves up to three classification dimensions and QC type assignments for LC/GC-MS multi-omics experiments.
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72. ▌
    Spectral Alignment Scoring 2 · holobiomicslab

    Use when you have paired MS/MS spectra (known compound and its structural analog) with assigned precursor m/z, charge, and SMILES; you want to quantify which parts of the molecular structure could have undergone modification by scoring peak alignment quality.
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73. ▌
    Spectral Query Formulation 2 · holobiomicslab

    Use when you have a query mass spectrum (or representative metabolite spectrum from public data) and need to identify it by searching against large spectral reference databases (millions to billions of spectra).
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74. ▌
    Spectrum Alignment Scoring 2 · holobiomicslab

    Use when after generating probability predictions for potential modification sites (via ModiFinder.
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75. ▌
    Lipid Library Curation 2 · holobiomicslab

    Use when you have obtained MobiLipid or a similar IM-MS lipidomics package that bundles a CCS reference library for labeled lipids, and you need to verify library integrity, validate that all expected lipid species are present with plausible numeric CCS values, and prepare a canonical curated.
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76. ▌
    Mass Accuracy Validation 2 · holobiomicslab

    Use when after implementing or modifying an mzML parser module that converts mzML files into MS-DIAL's internal data model, and before integrating the parser into the production analysis pipeline.
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77. ▌
    Smiles String Validation 2 · holobiomicslab

    Use when you have MSBERT-preprocessed spectral datasets (GNPS, MoNA, or MTBLS1572 format) with SMILES annotations before training a spectral embedding or compound identification model.
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78. ▌
    Poisson Noise Generation 2 · holobiomicslab

    Use when when preparing augmented variants of ion images (single-channel 2D arrays or multi-channel spectral images) for contrastive learning in mass spectrometry imaging tasks.
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79. ▌
    Spectral Image Filtering 2 · holobiomicslab

    Use when when generating augmented variants of single-channel or multi-channel ion images for contrastive learning in mass spectrometry imaging analysis.
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80. ▌
    Cross Method Rt Calibration 2 · holobiomicslab

    Use when when you have predicted retention times from a DNN model trained on one chromatographic method (CM) and need to rank or filter metabolite candidates on a different chromatographic method, but have only a small set of calibration molecules (≥10) with known RTs on both source and target.
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81. ▌
    Git Repository Tag Checkout 2 · holobiomicslab

    Use when when you need to reproduce or validate a specific historical release artifact (e.g., a Semantic Release v1.0.
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82. ▌
    Im Ms Drift Time Correction 2 · holobiomicslab

    Use when when you have IM-MS lipidomics data acquired on samples spiked with U13C-labeled internal standards (fully labeled yeast extract) and need to quantify systematic CCS bias and apply lipid class-specific bias correction to all measured CCS values, particularly when multiple lipids per lipid.
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83. ▌
    Lipid Feature Normalization 2 · holobiomicslab

    Use when after raw lipidomic and metabolomic data files have been generated by the Multi-ABLE method and loaded into the R environment, but before performing multivariate statistical analysis to identify differential lipids and metabolites.
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84. ▌
    Mass Accuracy Ppm Filtering 2 · holobiomicslab

    Use when when you have a set of candidate molecular formulae for a measured m/z value and need to rank them by how closely their theoretical m/z matches the observed value.
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85. ▌
    Metabolite Feature Flagging 2 · holobiomicslab

    Use when after drift correction and before imputation, when you have a MetaboSet object with LC-MS peak abundances and need to remove features with insufficient detection consistency across QC samples.
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86. ▌
    Metadata Field Verification 2 · holobiomicslab

    Use when you have located a workflow definition file (YAML or JSON) from a versioned release and need to confirm that all mandatory workflow metadata fields (name, version, inputs, outputs, steps) are declared, properly formatted, and cross-references are resolved before validation or execution.
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87. ▌
    Model Comparison Evaluation 2 · holobiomicslab

    Use when when you have multiple candidate spectrum prediction models (e.g., FFN vs. GNN encoders, NEIMS vs. MassFormer vs. ICEBERG) and need to determine which performs better on a shared task like tandem mass spectrum prediction.
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88. ▌
    Ms Ms Spectrum Purification 2 · holobiomicslab

    Use when processing LC-MS/MS data acquired in DDA mode that contains chimeric (co-fragmented) MS/MS spectra—i.e., when a single MS/MS scan contains fragments from multiple precursor ions due to co-isolation.
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89. ▌
    Nextflow Workflow Execution 2 · holobiomicslab

    Use when you have .mzML or .abf LC-HRMS metabolomics raw data files and need to perform peak detection, feature identification, and chromatogram alignment reproducibly across different compute environments (local workstations, HPC clusters).
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90. ▌
    Reference Library Alignment 2 · holobiomicslab

    Use when when you have IM-MS lipidomics data with measured CCS values from samples spiked with U13C labeled internal standards, and you need to assess systematic CCS bias or enable CCS correction by comparing measured lipids against known library entries with validated CCS values.
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91. ▌
    Release Artifact Generation 2 · holobiomicslab

    Use when when a software project has reached a stable milestone (v-tagged commit) and you need to produce official distribution artifacts with verified version metadata, checksums, and release documentation that can be validated against a published GitHub release record.
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92. ▌
    Retention Time Scan Mapping 2 · holobiomicslab

    Use when when you have loaded an LC-MS spectrum file (mzML, mzXML, or equivalent) into the GNPS LCMS Visualization Dashboard and need to annotate extracted ion chromatograms with the precise retention time or scan ID positions where MS2 events occurred.
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93. ▌
    Sample Metadata Integration 2 · holobiomicslab

    Use when when you have LC-MS peak table data in Excel format (e.g., from MS-DIAL peak picking) with separate compartments for sample information, feature properties, and intensity values, and you need to construct a structured R object that enforces consistency between sample metadata (e.
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94. ▌
    Snr Based Signal Validation 2 · holobiomicslab

    Use when after composite map peak detection has generated a full unfiltered peak list with SNR values computed for each candidate peak.
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95. ▌
    Spectral Feature Extraction 2 · holobiomicslab

    Use when you have raw mass-spectrometry data (precursor m/z, ionization mode, and fragment m/z–intensity pairs) and need to feed it into a CNN-based metabolite annotation pipeline.
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96. ▌
    Spectral Similarity Scoring 2 · holobiomicslab

    Use when when you have extracted low-resolution mass spectra from individual chromatographic peaks in GC-MS data and need to match them against a spectral library (e.g., PNNLMetV20191015.MSL) to identify the unknown compound.
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97. ▌
    Spectrum Metadata Filtering 2 · holobiomicslab

    Use when importing mass spectra from multiple open mass spectra libraries (OMSLs) or databases with heterogeneous metadata quality. Use when you observe spectra annotated with negative adducts (e.
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98. ▌
    Spectrum Similarity Scoring 2 · holobiomicslab

    Use when you have an unknown MS/MS spectrum (query spectrum with m/z and intensity pairs) and a reference spectral library (local or public: GNPS, MASSBANK, DrugBANK), and you need to identify the -matching compounds by ranking library entries by spectral similarity.
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99. ▌
    Tree Based Model Validation 2 · holobiomicslab

    Use when after training a decision tree classifier on ChemEcho sparse feature vectors (representing tandem mass spectra fragmentation patterns), especially when the goal is to deploy predictions as executable queries or to validate that learned splits correspond to chemically meaningful.
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100. ▌
     Hpc Job Parallelization 2 · holobiomicslab

     Use when you have a filtered set of conformers (100s–1000s) from ASE-ANI that each require independent quantum calculations via QUICK, and you have access to HPC resources with multiple cores or nodes. Parallelization is necessary when serial execution would exceed practical time budgets (e.
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