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HolobiomicsLab

@holobiomicslab source repo

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1. ▌
   Colocalization Coefficient Computation · holobiomicslab

   Use when you have two co-registered LA-ICP-MS element channel images and need to quantify whether their spatial distributions are statistically correlated or independent. Use it specifically when investigating whether two elements co-occur spatially (e.
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2. ▌
   Compound Class Prediction From Spectra · holobiomicslab

   Use when you have an unknown mass spectrometry spectrum (acquired experimentally or computationally) and need to assign it to a known drug class or identify candidate structures.
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3. ▌
   Computational Performance Benchmarking · holobiomicslab

   Use when you have multiple candidate normalization methods for metabolomics data and need to choose one based on both statistical robustness and computational cost.
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4. ▌
   Conda Environment Setup And Activation · holobiomicslab

   Use when preparing a fresh system or user account to run MSIGen for mass spectrometry imaging data processing, or when you need to isolate MSIGen installation from other Python projects to avoid dependency conflicts.
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5. ▌
   Conditional Execution Dispatch Routing · holobiomicslab

   Use when when you have a Streamlit workflow application that must support both offline (local machine, Windows installer) and online (Docker, cloud) deployment modes, and you need to decide at runtime whether to queue jobs remotely via Redis or spawn local processes.
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6. ▌
   Converter Response Parsing And Merging · holobiomicslab

   Use when after executing asynchronous conversion jobs across multiple external APIs during spectrum metadata enrichment, when you have received response dictionaries from one or more converters and need to integrate them into spectrum records while maintaining data integrity and handling partial or.
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7. ▌
   Correlation Network Construction Omics · holobiomicslab

   Use when when you have a feature abundance table (rows=features, columns=samples) with >10,000 features from microbiome, metabolomics, transcriptomics, or multi-omics data and need to identify correlated or co-occurring features for network-based analysis, module detection, or cross-omics.
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8. ▌
   Cross Platform Build Environment Setup · holobiomicslab

   Use when when you have a Qt5 C++ project (such as Maven GUI or Maven Core) that must be built on multiple target operating systems, and you need to install and verify platform-specific build dependencies (MSYS2 + mingw64 on Windows, Homebrew + Qt5 on macOS, PPA + Qt5.
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9. ▌
   Cross Validation Result Interpretation · holobiomicslab

   Use when after running k-fold repeated cross-validation on a development set (via runCrossValidation with parameters k, repNum, and a subset of algorithms) and you need to select which trained classifier and hyperparameter combination to deploy.
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10. ▌
    Data Quality Control Report Generation · holobiomicslab

    Use when after applying biomolecule filtering criteria (minimum non-missing values and coefficient of variation thresholds) to an omics expression dataset, generate a report to document filtering impact and justify data retention decisions to stakeholders or for reproducibility.
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11. ▌
    Deep Learning Model Weight Persistence · holobiomicslab

    Use when you are training a deep learning model using k-fold cross-validation on an HPC cluster with SLURM job arrays, where each fold runs as a separate independent job with its own GPU allocation.
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12. ▌
    Differential Lipid Expression Analysis · holobiomicslab

    Use when when you have a LipidomicsExperiment object with logged and normalized Area measurements, sample grouping metadata (e.g., SampleType, Stage, Race), and a research question about whether specific lipid molecules or classes differ significantly between two or more sample groups.
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13. ▌
    Embedding Space Rationality Validation · holobiomicslab

    Use when after training a deep learning model (e.g. MSBERT) on mass spectra and obtaining embedding vectors, apply this skill to verify that the learned representations cluster chemically similar compounds together and that structural relationships are preserved in the embedding space.
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14. ▌
    Error Handling For Unsupported Formats · holobiomicslab

    Use when building a file format dispatcher or initialization routine that must accept user-provided file paths and map them to format-specific processing modules.
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15. ▌
    Feature Abundance Correlation Grouping · holobiomicslab

    Use when after performing retention-time-based feature grouping (e.g., 10–20 second windows), when you observe large feature groups that may conflate multiple independent compounds with coincidentally similar retention times.
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16. ▌
    Feature Interpretation Neural Networks · holobiomicslab

    Use when you have trained multiple neural network models (e.
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17. ▌
    Foundation Model Prediction Generation · holobiomicslab

    Use when you have a pre-trained foundation model checkpoint (e.g., NaFM.ckpt) and new molecules represented as SMILES strings or a CSV file, and you need to generate predictions (e.g., bioactivity scores, taxonomy class, screening rankings) or embeddings for downstream analysis.
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18. ▌
    Gene Expression Constraint Integration · holobiomicslab

    Use when when you have a generic constraint-based metabolic model, RNA-seq or transcriptomics data (FPKM or similar), and you want to reduce the underdetermination of metabolic flux predictions by encoding which reactions are expected to be active or inactive based on their constituent enzymes'.
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19. ▌
    Gene Identifier Format Standardization · holobiomicslab

    Use when when you have differential expression results from genes, miRNAs, proteins, or lipids with heterogeneous identifier formats (raw gene names, Ensembl accessions, RefSeq IDs, or gene symbols) and need to perform pathway enrichment analysis.
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20. ▌
    Gene Ontology Enrichment Visualization · holobiomicslab

    Use when you have completed a GO enrichment analysis (e.g., via hypergeometric test or similar) and need to visualize the results as a dotplot. Input is a CSV table with columns for GO term identifiers, p-values or adjusted p-values, gene ratios (or counts), and gene count;
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21. ▌
    Hit Score Consolidation Across Domains · holobiomicslab

    Use when you have executed batch spectral searches against two or more domain-specific MASSTs (via the Fast Search API or individual MASST tools) and need to combine the resulting hit lists, match scores, and taxonomic/metadata annotations into a single ranked table for downstream analysis.
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22. ▌
    Image Channel Masking And Segmentation · holobiomicslab

    Use when when you have multi-channel LA-ICP-MS images and need to isolate specific elemental regions (e.
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23. ▌
    Interactive Plot Generation And Export · holobiomicslab

    Use when after normalizing a featuredata matrix (samples × metabolites), when you need to visually assess the success of normalization across batches or sample groups.
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24. ▌
    Internal Standard Intensity Extraction · holobiomicslab

    Use when you have lipidomics data from LipidSearch or LIQUID output that includes internal lipid standards spiked into samples at known concentrations, and your goal is to convert relative intensities to absolute concentration values.
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25. ▌
    Metabolite Annotation Ensemble Ranking · holobiomicslab

    Use when you have ESI/LC-MS test spectra requiring candidate metabolite ranking, pre-trained MLP (NEIMS) and GNN baseline models are available or can be trained, you seek quantified improvement over single-model average rank performance (baseline MLP shows ~339 average rank), and your evaluation.
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26. ▌
    Metabolite Annotation Mlp Gnn Ensemble · holobiomicslab

    Use when you have ESI/LC-MS test spectra requiring metabolite annotation and need to compare ensemble-based neural network predictions (ESP) against a baseline MLP model to quantify performance gains.
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27. ▌
    Metabolite Rank Performance Evaluation · holobiomicslab

    Use when after training an ensemble model (MLP, GNN, or ESP) on spectral data, use this skill to measure performance on test spectra where ground-truth metabolite identities are known. Essential for comparing model variants (e.
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28. ▌
    Metabolomics Workflow Step Enumeration · holobiomicslab

    Use when when initializing a SmartPeak session and you have a workflow.csv file co-located with sequence.csv in the session directory, and you need to determine the precise order and validity of peak detection, calibration, and QC operations before execution.
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29. ▌
    Molecular Spectrum Prediction Baseline · holobiomicslab

    Use when you have multiple mass spectrum prediction models to compare and need to establish a level playing field by implementing at least one well-characterized baseline (such as NEIMS) with both FFN and GNN encoder variants.
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30. ▌
    Molecular Weight Prediction From Ei Ms · holobiomicslab

    Use when you have EI-MS spectrum data (m/z and intensity pairs) from a sample and need a direct, model-based prediction of molecular weight.
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31. ▌
    Neutral Loss Annotation Interpretation · holobiomicslab

    Use when when building Word2Vec or embedding-based spectral similarity models where you need to capture fragmentation patterns beyond individual peak positions.
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32. ▌
    Overlapped Peak Resolution Transformer · holobiomicslab

    Use when when GC-MS chromatograms contain overlapped or co-eluting peaks in a retention time region and you need to recover the individual pure mass spectra and relative abundances of each component without manual peak picking or external standards.
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33. ▌
    Peak Filtering And Preprocessing Lc Ms · holobiomicslab

    Use when you have raw LC-MS/MS spectra from vendor instruments (mzML, mzXML, MGF, or MSP format) with variable peak quality and intensity distributions, and you plan to perform library matching, molecular networking, or spectral similarity comparison.
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34. ▌
    Pytorch Model Loading And Forward Pass · holobiomicslab

    Use when when you have a pre-trained PyTorch model checkpoint (e.g., a MWFormer transformer) and structured input data (e.g., electron ionization mass spectrum m/z and intensity pairs) that must be converted to tensor format and passed through the model to produce a direct numerical output (e.
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35. ▌
    Retention Time Prediction Optimization · holobiomicslab

    Use when when you have a retention-time dataset (e.g., SMRT or Eawag_XBridgeC18_364) in .xlsx format and need to train or adapt a graph neural network model to predict chromatographic retention times for new compounds.
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36. ▌
    Train Validation Test Split Management · holobiomicslab

    Use when when you have a complete dataset of labeled examples (e.g., 100,000 augmented spectra, chromatograms, or synthetic samples) and need to train a supervised model (such as a Transformer) while preserving a held-out test set to measure generalization performance without bias.
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37. ▌
    Unsupervised Pattern Discovery Spectra · holobiomicslab

    Use when when you have preprocessed mass spectral data (normalized peak intensities or binned m/z representations) and need to discover latent spectral patterns to enhance neural network predictors without labeled spectral classes.
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38. ▌
    Adduct Fragment Formula Interpretation · holobiomicslab

    Use when after temporal correlation has identified candidate feature pairs with matching intensity profiles across time-resolved DBDI-MS experiments.
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39. ▌
    Analyte Metadata Hierarchical Indexing · holobiomicslab

    Use when after applying a stringent Q-value quality filter (e.
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40. ▌
    Area Integration From Detection Output · holobiomicslab

    Use when you have region-of-interest (ROI) LC-MS data and a pre-trained object detection model has already predicted peak locations and confidence scores.
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41. ▌
    Baseline Noise Estimation Chromatogram · holobiomicslab

    Use when after auditing and optionally rescaling a mass track (composite mass chromatogram) when you need to subtract background signal and set dynamic prominence thresholds for peak detection.
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42. ▌
    Binary Cross Entropy Loss Optimization · holobiomicslab

    Use when when you have a pretrained spectrum encoder (TCN) and need to train a formula rescoring module that ranks candidate molecular formulas against MS/MS spectra.
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43. ▌
    Cardinal Peak Processing Snr Filtering · holobiomicslab

    Use when when you have loaded an unprocessed Cardinal object from MS imaging data (e.g., from Zenodo or native formats) containing thousands of m/z features across many spectra, and you need to produce a curated peak list with known expected peak count (e.g., 687 cleaned peaks from PIGII_206).
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44. ▌
    Chemical Similarity Metrics Evaluation · holobiomicslab

    Use when after training a sequence-to-sequence decoder that reconstructs SMILES strings from fixed-size embeddings (e.g., from MS/MS spectra), use this skill to measure reconstruction fidelity on held-out test embeddings.
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45. ▌
    Chemical Valence Constraint Validation · holobiomicslab

    Use when when you have enumerated a large pool of candidate chemical subformulae for observed fragment peaks (m/z values) within a mass tolerance window and need to eliminate chemically invalid candidates before ranking.
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46. ▌
    Chromatographic Misalignment Detection · holobiomicslab

    Use when after running XCMS-based alignment on LC-MS datasets with hundreds of samples or data acquisition periods longer than a week, when the assumption that all m/z bins in the same sample share a single warping function is likely to fail.
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47. ▌
    Cluster Based Candidate Prioritization · holobiomicslab

    Use when after mass-to-charge matching has produced a large table of candidate KEGG metabolites with multiple adduct assignments per feature. Use it when you observe that multiple candidate ions co-elute and share similar intensity profiles, suggesting they derive from the same parent metabolite;
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48. ▌
    Clustering Solution Comparison Metrics · holobiomicslab

    Use when you have applied two different clustering or dendrogram-flattening methods (e.g., constant-threshold vs. silhouette-score optimization) to the same feature set and need to assess whether the two solutions assign features to clusters consistently.
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49. ▌
    Compound List Annotation And Filtering · holobiomicslab

    Use when after marker identification or feature selection has produced a list of discriminatory m/z features, and before pathway enrichment analysis (e.g., KEGG).
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50. ▌
    Compound Retrieval Hit At K Evaluation · holobiomicslab

    Use when when you have generated spectral embeddings for a query set and a reference spectral library, computed pairwise cosine similarity scores between them, and need to quantify retrieval performance across ranked candidates (top-1, top-5, top-10).
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51. ▌
    Configuration File Validation Dynaconf · holobiomicslab

    Use when when setting up NPLinker for natural products data mining and you have a TOML configuration file (nplinker.toml) that specifies root_dir, mode (local or podp), and paths to GNPS, AntiSMASH, and MIBiG directories.
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52. ▌
    Constraint Based Flux Balance Analysis · holobiomicslab

    Use when you have a generic genome-scale metabolic model (SBML format) and cross-sectional omics data (RNA-seq, intracellular metabolomics, extracellular flux measurements from bioanalyzer or similar) from multiple biological samples (cell lines, conditions).
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53. ▌
    Container Image Selection And Mounting · holobiomicslab

    Use when when deploying a Nextflow workflow across multiple execution environments (local machines, HPC clusters) where tool versions, dependencies, or OS configurations may differ. Choose this skill specifically when you need to process LC-HRMS .mzML or .
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54. ▌
    Cross Tool Result Concordance Analysis · holobiomicslab

    Use when you have executed multiple NPDtools database search pipelines (Dereplicator, VarQuest, Dereplicator+, or MetaMiner in different modes) on identical test spectra or RiPP sequence inputs and need to understand their relative sensitivity, specificity, and complementarity.
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55. ▌
    Dataframe And Numericlist Manipulation · holobiomicslab

    Use when you are implementing a new MsBackend subclass and need to store spectra metadata (sample names, retention times, precursor m/z, etc.) separately from peak data (m/z and intensity pairs) while maintaining row-wise alignment.
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56. ▌
    Deep Learning Model Checkpoint Loading · holobiomicslab

    Use when when you have MS/MS spectra from GNPS or other libraries and need to apply a pre-trained FIDDLE model (TCN formula predictor or Siamese rescore architecture) without training from scratch. Use this skill before running inference on new samples or benchmarks.
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57. ▌
    Directory Structure Schema Enforcement · holobiomicslab

    Use when you have raw or partially organized natural products data from multiple sources (GNPS molecular networking, AntiSMASH BGC predictions, BigScape clustering, MIBiG metadata) and need to prepare them for NPLinker integration.
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58. ▌
    Distance Clustering In High Dimensions · holobiomicslab

    Use when when you have detected feature tables from two or more LC-IMS-MS/MS samples and need to establish correspondence between features across samples (i.e., which features in sample A match which features in sample B).
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59. ▌
    Drift Time To Ccs Correlation Modeling · holobiomicslab

    Use when you have tunemix or other reference standards with known m/z, drift-time, charge state, and CCS values, and you need to establish a predictive calibration model for your ion-mobility mass spectrometry instrument.
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60. ▌
    Feature Abundance Correlation Analysis · holobiomicslab

    Use when after initial retention-time-based feature grouping (e.g., using SimilarRtimeParam with a 20-second window), apply this skill when you need to split large feature groups into more homogeneous sub-groups.
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61. ▌
    Feature Abundance Threshold Comparison · holobiomicslab

    Use when after loading an MZmine3-exported feature quantification table and identifying blank sample columns, when you need to remove features with significant intensity in procedural blanks before proceeding to batch correction and statistical analysis.
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62. ▌
    Feature Alignment Error Identification · holobiomicslab

    Use when you have completed peak detection and feature alignment in metabolomic LC-MS processing and suspect systematic errors in peak integration or feature misalignment across your sample cohort.
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63. ▌
    Feature Correlation Graph Construction · holobiomicslab

    Use when after imputing missing values and before assigning Cluster_IDs in the notame preprocessing pipeline.
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64. ▌
    Feature Flagging Threshold Calibration · holobiomicslab

    Use when after drift correction in non-targeted LC-MS metabolomics workflows, when you need to decide which molecular features are sufficiently reproducible (low instrument/QC variance) and biologically informative (high QC-versus-biological signal ratio) to retain for downstream statistical.
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65. ▌
    Feature Grouping By Similarity Metrics · holobiomicslab

    Use when after LCMS feature alignment (e.g., Eclipse output) when you have a feature table with retention times and intensity profiles across multiple injections, and you need to collapse redundant features (e.
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66. ▌
    Feature Retention Criteria Application · holobiomicslab

    Use when after signal drift correction and batch effect removal (step 4) have been completed and per-feature D-Ratio values are available, but before normalization (step 7).
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67. ▌
    File Format Conversion Troubleshooting · holobiomicslab

    Use when when integrating MSConvert into an automated LC-MS QC workflow and you need to confirm that vendor acquisition files are properly converted to mzML format with intact spectral metadata. Apply this skill after each MSConvert invocation or when QC results appear incomplete or anomalous (e.
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68. ▌
    File Path Normalization And Validation · holobiomicslab

    Use when when preparing to read Thermo Fisher Scientific .raw files using rawrr functions (readFileHeader, readSpectrum, readChromatogram, readIndex), or when retrieving cached assembly paths for the wrapped RawFileReader dependency.
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69. ▌
    Fragmentation Pattern Query Validation · holobiomicslab

    Use when after converting a decision tree path into a MassQL query string, before deployment to production mass spectrometry workflows.
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70. ▌
    Gnps Molecular Networking Data Parsing · holobiomicslab

    Use when you have downloaded a GNPS archive from either GNPS1 (https://gnps.ucsd.edu) or GNPS2 (https://gnps2.org) and need to programmatically load and validate its contents (spectra.mgf, molecular_families.tsv, annotations.tsv, file_mappings.
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71. ▌
    Ion Mode Specific Ccs Model Generation · holobiomicslab

    Use when you have tunemix reference data acquired in both positive and negative ion modes and need to establish independent CCS calibration curves for each mode. The skill is required when downstream CCS assignments must achieve R² ≥ 0.
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72. ▌
    Isotope And Adduct Pattern Recognition · holobiomicslab

    Use when when processing MS1 mass tracks from a single sample and you have already constructed per-bin mass tracks with consensus m/z and intensity vectors.
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73. ▌
    Isotopic Pattern Detection And Merging · holobiomicslab

    Use when your peak table contains features suspected of being artifacts of incorrect isotopic pattern splitting during preprocessing—particularly when you observe multiple ions with very similar mass-to-charge ratios and retention times, or when visual inspection of m/z vs. retention time plots.
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74. ▌
    Isotopologue Adduct Cluster Assignment · holobiomicslab

    Use when after sample alignment and peak picking have produced an aligned feature table with m/z and retention time coordinates.
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75. ▌
    Isotopologue Grouping By Mass Accuracy · holobiomicslab

    Use when after PuInc_seeker has identified putative incorporations in a stable-isotope-labeled LC/MS dataset (e.g., CELL_Glc12 unlabeled vs. CELL_Glc13 labeled samples).
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76. ▌
    Isotopologue Quantification Extraction · holobiomicslab

    Use when you have GC-CI-MS or LC-MS data in mzML format targeting a defined set of compounds with known monoisotopic mass, retention time, and chemical formula, and you need per-isotopologue (M+0, M+1, M+2, etc.) quantification metrics (area, maxo intensity, abundance).
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77. ▌
    Jpa Module Execution And Configuration · holobiomicslab

    Use when when you have raw LC-MS data in mzXML format (or vendor formats convertible via MS-Convert) and need to extract metabolic features as the first major step of untargeted metabolomics analysis. Choose MS1 peak picking for DDA/full-scan data;
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78. ▌
    JSON Serialization And Deserialization · holobiomicslab

    Use when after extracting header metadata from a Thermo Fisher Scientific .
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79. ▌
    Laplacian Embedding Spectral Alignment · holobiomicslab

    Use when you have two or more MS/MS fragmentation spectra (with precursor m/z, fragment m/z values, and intensities) and need to identify which fragment ions match across spectra while quantifying the statistical confidence of those matches.
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80. ▌
    Lc Ms Dataset Acquisition And Curation · holobiomicslab

    Use when when beginning an untargeted LC-MS metabolomics study and need to assemble a cohort of mzML files for processing; particularly when establishing performance baselines across sample counts (10, 50, 100+ samples), validating reproducibility, or preparing data for publication.
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81. ▌
    Lc Ms Mass Matching Reference Building · holobiomicslab

    Use when at the start of an untargeted LC-MS annotation pipeline when you have a KEGG database with exact masses and need to prepare a mass-matching reference.
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82. ▌
    Lipid Fragmentation Pattern Prediction · holobiomicslab

    Use when you have defined lipid species (class, chain composition, and adducts) and need to generate precursor–fragment transition pairs for targeted lipidomics experiments.
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83. ▌
    Lipid Identification Quality Filtering · holobiomicslab

    Use when you have MS-DIAL lipid identification results (alignment exports in msp/txt format) and need to distinguish correct from incorrect lipid IDs before downstream analysis.
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84. ▌
    Mass Spectrometry Data Format Handling · holobiomicslab

    Use when you have raw MRM sample files from an LC-MS/MS instrument and need to programmatically identify and tabulate all precursor m/z and product m/z pairs for each MRM transition.
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85. ▌
    Mass Spectrometry Embedding Generation · holobiomicslab

    Use when when you have preprocessed MS/MS spectral data (normalized peak intensities and m/z values) and need to convert individual spectra into fixed-dimensional vector representations for similarity-based metabolite matching or comparative analysis.
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86. ▌
    Mass Spectrometry File Format Handling · holobiomicslab

    Use when when you have Thermo Fisher Scientific Orbitrap .raw files (e.g., from Q Exactive HF instruments) and need to extract spectral, chromatographic, or metadata directly into R for downstream statistical analysis, benchmarking, or integration with Bioconductor workflows.
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87. ▌
    Mass Spectrometry File Standardization · holobiomicslab

    Use when you have raw or semi-processed mass-spectrometry peak data from XCMS, MSnbase, or other peak-picking tools in non-standard formats (MetaboAnalyst-like, Metabolights, vendor-specific), and you need to load them into MetaboShiny for compound identification, normalization, and statistical.
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88. ▌
    Mass Spectrometry Imaging Data Loading · holobiomicslab

    Use when you have raw or preprocessed MS imaging data archived as an RDS file or from a Zenodo deposit that includes the full m/z feature set (e.g., 10,200 m/z values spanning 150–1000 m/z range) and spectrum count (e.
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89. ▌
    Mass Spectrum Averaging And Processing · holobiomicslab

    Use when you have loaded a Bruker Solarix transient file (.d format with .ser or .fid content) and need to generate a processed mass spectrum for peak picking and molecular formula annotation.
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90. ▌
    Mass Spectrum Visualization Matplotlib · holobiomicslab

    Use when you have an annotated MsmsSpectrum object (with fragment assignments via ProForma 2.0) and need to produce a high-resolution, static PNG figure showing both the observed spectrum and color-highlighted fragment ion matches for inclusion in a manuscript or supplementary materials.
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91. ▌
    Mass Tolerance Matching And Validation · holobiomicslab

    Use when you have an MS/MS spectrum with observed peaks and a peptidoform specification (e.g., ProForma 2.0 notation such as 'EM[Oxidation]EVEES[Phospho]PEK'), and you need to annotate which observed peaks correspond to known fragment ions.
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92. ▌
    Metabolite Ranking By Annotation Score · holobiomicslab

    Use when when you have generated a set of candidate metabolites for a given experimental MS/MS spectrum and need to determine which candidate is most likely to be the true metabolite.
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93. ▌
    Metabolite Structure Format Conversion · holobiomicslab

    Use when when importing candidate metabolite structures from public chemical databases (PubChem, ChEBI, etc.) for use in MAGMa-based annotation workflows, or when integrating external structure datasets that may use divergent molecular representation formats or contain non-standard chemical.
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94. ▌
    Metadata Batch Assignment Verification · holobiomicslab

    Use when after data merging and before applying batch correction algorithms (ComBat, SVA, or normalization techniques) to a merged feature table from non-targeted LC-MS/MS metabolomics data.
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95. ▌
    Metadata Extraction From Spectral Data · holobiomicslab

    Use when you have raw mass spectrometry files in one of the supported formats (mzML, mzXML, msp, metabolomics-USI, MGF, JSON) and need to parse out metadata fields (e.
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96. ▌
    Metadata Harmonization Standardization · holobiomicslab

    Use when when you have preprocessed MS/MS spectra from multiple source repositories or instruments with inconsistent metadata field naming, formats, or values (e.g., mixed adduct notations like '[M+H]+' vs '[M+H]⁺', variable instrument type strings, or non-standard collision energy units).
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97. ▌
    Molecular Database Loading And Parsing · holobiomicslab

    Use when when setting up a ViMMS chemical sampling environment and you need to restrict the chemical search space to a specific m/z range (e.g., 100–1000) and MS level (e.g., MS1 only) before generating virtual LC-MS/MS data.
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98. ▌
    Molecular Network Attribute Enrichment · holobiomicslab

    Use when you have a GNPS mass spectral molecular network (in .graphml or Cytoscape format) and wish to annotate its nodes and edges with chemical class assignments from the GNPS library and/or MS2LDA motif probabilities from an independent LDA experiment.
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99. ▌
    Ms1 Spectral Annotation Chemical Space · holobiomicslab

    Use when you have (1) a peaklist from untargeted LC/HRMS analysis with m/z and intensity values (typically output from IDSL.IPA or similar peak-picking tools), (2) a need to assign molecular formulas to detected peaks, and (3) only MS1 spectral data available (MS/MS is not required).
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100. ▌
     Multi Domain Search Result Aggregation · holobiomicslab

     Use when you have executed batch searches of MS/MS spectra against multiple domain-specific MASST tools and need to synthesize results across domains (e.
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