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HolobiomicsLab

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1. ▌
   Ion Mobility Spectrometry Peak Extraction · holobiomicslab

   Use when after peaks have been detected in aligned GCIMS samples using findPeaks with CWT parameters and peaks have been clustered across samples, and you need to integrate peak signals into a matrix format where each entry represents the intensity of a peak cluster in a specific sample for.
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2. ▌
   Ionisation Method Hardware Correspondence · holobiomicslab

   Use when when evaluating whether a mass spectrometry analysis platform (such as mzmine) has comprehensive module support across multiple ionisation and separation techniques (LC, GC, IMS, MALDI MS imaging), or when planning a multi-technique MS study and needing to confirm that all intended.
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3. ▌
   Mass Spectrometry M Z Accuracy Assessment · holobiomicslab

   Use when when you have detected peaks in a direct injection FTICR-MS mzML file (or similar high-resolution MS format) and need to assess whether m/z measurements are accurate and consistent across the m/z range.
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4. ▌
   Microbiome Metabolome Prediction Modeling · holobiomicslab

   Use when you have paired microbiome (16S rRNA, metagenomic) and metabolomic (LC-MS, GC-MS) abundance tables from the same biosamples, and you want to predict which metabolites are recoverable from microbial composition alone and identify groups of microbes and metabolites with correlated.
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5. ▌
   Molecular Structure Generation Evaluation · holobiomicslab

   Use when you have access to pre-trained MSGO model weights (PFAS or lipid variants) and a set of 300+ real mass spectra (LC–QTOF or similar), and need to verify whether the model can generate correct molecular structures for unknown chemicals.
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6. ▌
   Parallel Computing Workflow Orchestration · holobiomicslab

   Use when when compiling EI or MS/MS spectral libraries from multiple gigabyte-scale sources (e.
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7. ▌
   Retention Index Calibration And Alignment · holobiomicslab

   Use when processing raw GC-MS data in NetCDF format where peaks have been detected but lack standardized retention indices.
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8. ▌
   Software Feature Inventory And Comparison · holobiomicslab

   Use when you need to assess whether a newly developed FT-ICR MS pipeline (e.
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9. ▌
   Attention Mechanism For Spectral Decoding · holobiomicslab

   Use when when you have pretrained encoder-produced embeddings from MS/MS spectra and need to decode them into canonical SMILES strings representing molecular structures.
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10. ▌
    Automated Feature Extraction From Spectra · holobiomicslab

    Use when when you have raw or processed direct-infusion MS (DI-MS) or ASAP-MS spectra as mz/intensity pairs and need to rapidly identify salient peaks for species authentication, sample scoring, or comparative profiling without manual inspection.
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11. ▌
    Automated Metabolite Property Computation · holobiomicslab

    Use when you have ion mobility-mass spectrometry data (raw drift times, m/z values, and feature intensities) from DTIMS-MS or SLIM-based IMS-MS platforms and need to compute collision cross section values using a calibration standard (e.g., Agilent tune-mix).
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12. ▌
    Batch Construction Parameter Optimization · holobiomicslab

    Use when preparing labeled LC-MS peak data for neural network training and you need to decide whether class imbalance in your dataset should be preserved or corrected in batch construction. Use it particularly when your annotated peak dataset has unequal class distributions (e.
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13. ▌
    Batch Effect Detection And Quantification · holobiomicslab

    Use when after merging feature tables from multiple LC-MS/MS analytical runs or sample cohorts processed in separate batches, and before applying batch correction.
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14. ▌
    Bayesian Annotation Probability Inference · holobiomicslab

    Use when you have LC/MS feature data (m/z, retention time, intensity) and need to assign metabolite annotations with confidence scores rather than binary peak-to-compound matches.
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15. ▌
    Bgc Spectrum Ranking By Kernel Similarity · holobiomicslab

    Use when you have: (1) a trained IOKR model mapping from spectrum kernels to molecular fingerprints, (2) MS2 spectra from your sample, (3) a set of candidate BGCs with known or predicted structures (e.
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16. ▌
    Candidate Structure Ranking From Spectrum · holobiomicslab

    Use when you have an experimental tandem mass spectrum (m/z and intensity pairs) and a known or suspected chemical formula, and you need to narrow down the identity of an unknown compound from a large candidate pool (e.g., all PubChem entries matching that formula).
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17. ▌
    Charge State Determination And Assignment · holobiomicslab

    Use when when analyzing high-resolution mass spectrometry data from natural-abundance or labeled peptides where multiple charge states (+2, +3, +4, etc.) may be present within the same m/z isolation window (e.g., 880–890 m/z).
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18. ▌
    Chemical Classification Scheme Validation · holobiomicslab

    Use when you have structural annotations from in silico tools (SIRIUS, CANOPUS) and spectral library matches from GNPS, but need to (1) reconcile conflicting or incomplete ClassyFire ontology assignments, or (2) substitute NPClassifier taxonomy when ClassyFire ontology data is unavailable from GNPS.
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19. ▌
    Chemical Formula Extraction And Filtering · holobiomicslab

    Use when when you have a loaded metabolite database (e.g., hmdb_compounds.p pickle file) and need to constrain the chemical space to a specific instrumental m/z range (e.
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20. ▌
    Chemical Structure Annotation Oracle Mode · holobiomicslab

    Use when you have a known compound structure with validated MS/MS spectrum and a structural analog (modified version) with its own MS/MS spectrum, and you need to assess whether a modification site prediction method correctly identifies which atoms were altered.
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21. ▌
    Chemical Structure Annotation Preparation · holobiomicslab

    Use when you have acquired composite fragmentation spectra from DIA experiments (MS^E, AIF, or SWATH-MS) that have been deconvoluted by IDSL.CSA, and you need to export the resolved spectra in a format suitable for chemical structure identification against reference libraries or spectral databases.
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22. ▌
    Chemical Structure Descriptor Computation · holobiomicslab

    Use when when you have a set of molecular structures (N-Me derived unsaturated sterol lipids or structurally similar organic molecules with C=C bonds) represented as SMILES or molecular geometry files, and you need to train or apply a machine-learning model to predict an instrument-dependent.
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23. ▌
    Chimeric Spectrum Detection And Filtering · holobiomicslab

    Use when you have acquired LC-MS/MS data in Data-Dependent Acquisition (DDA) mode for untargeted metabolomics and suspect contamination from chimeric (co-fragmented) MS/MS spectra.
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24. ▌
    Chromatogram Extraction And Visualization · holobiomicslab

    Use when you have sqMass files containing pre-extracted transition group chromatograms from DIA-MS experiments and need to interactively visualize individual peptide precursor chromatograms, inspect peak quality via Q-values (typically 1% FDR cutoff), and overlay peak boundaries or apply on-the-fly.
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25. ▌
    Comparative Machine Learning Benchmarking · holobiomicslab

    Use when you have developed a new machine learning model for predicting metabolomic profiles from microbiome data and need to quantify its performance improvement over existing methods.
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26. ▌
    Compound Annotation Confidence Assessment · holobiomicslab

    Use when you have MS/MS spectra matched to a reference library via both identity search (exact or high-similarity matches) and fuzzy/analog search (structurally related compounds with similar fragmentation), and you need to prioritize which annotations to trust for downstream reporting, validation.
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27. ▌
    Data Visualization From Tabulated Results · holobiomicslab

    Use when after executing a MassQL query that returns a tabulated results DataFrame (e.g., MS1 or MS2 scan metadata, peak intensities, retention times), and you need to produce visual summaries suitable for publication, presentation, or exploratory analysis.
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28. ▌
    Decision Tree Training And Interpretation · holobiomicslab

    Use when you have tandem mass spectra data and need to predict a discrete molecular property (e.g., presence/absence of a sulfo group) while maintaining full interpretability of the decision logic.
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29. ▌
    Extracellular Flux Constraint Integration · holobiomicslab

    Use when you have constraint-based metabolic models of multiple cell lines, experimental measurements of extracellular metabolite concentrations at two timepoints (e.
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30. ▌
    Feature Intensity Recovery Across Samples · holobiomicslab

    Use when after sample alignment in untargeted LC-MS workflows, when the aligned feature table contains missing (NA or zero) intensity entries for features that are detected in some samples but fall below the instrument detection limit or are absent in others.
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31. ▌
    Feature List Harmonization Across Methods · holobiomicslab

    Use when you have feature lists in CSV format originating from different acquisition methods (e.
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32. ▌
    Feature To Metabolite Network Propagation · holobiomicslab

    Use when you have an untargeted metabolomics feature table (m/z, retention time, p-value from statistical test) but lack comprehensive metabolite identifications or MS/MS annotations.
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33. ▌
    Fragment Frequency Threshold Optimization · holobiomicslab

    Use when you have replicate MS/MS spectra with labeled fragment recurrence frequencies and need to select an optimal frequency threshold (beyond the default 0.1) that maximizes spectral quality metrics while minimizing false positive noise.
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34. ▌
    Fragment Ion Mass Matching With Tolerance · holobiomicslab

    Use when denoising MS/MS spectra and you have: (1) a precursor ion with measured m/z and known molecular formula (from SMILES or direct formula input), (2) an adduct type ([M+H]+, [M+Na]+, etc.), (3) a list of fragment ions with observed m/z values, and (4) a need to distinguish chemically valid.
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35. ▌
    Fragment Neutral Loss Annotation Matching · holobiomicslab

    Use when after generateComponents with algorithm='tp' has tentatively paired parent features with TP candidates based on retention time and spectrum similarity, and when formula annotations are available for both parents and candidates.
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36. ▌
    Fragment Recurrence Frequency Calculation · holobiomicslab

    Use when after extracting and grouping fragments from top x% TIC-filtered replicate spectra for a given feature, to quantify which fragments consistently appear across replicates. Use this when you have multiple MS/MS spectra for the same precursor (e.
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37. ▌
    Functional Module Inference From Networks · holobiomicslab

    Use when you have an untargeted metabolomics feature table (m/z and retention time columns) and a statistical test result (p-value) per feature, but lack confident metabolite identifications.
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38. ▌
    Gaussian Process Regression Model Fitting · holobiomicslab

    Use when after you have accumulated experimental MS data from ≥2 LC gradient trials, extracted separation efficiency metrics (retention time spacing) from each trial, and encoded each gradient as a feature vector.
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39. ▌
    Genome Cluster Family Scoring Computation · holobiomicslab

    Use when you have pre-processed AntiSMASH BGC annotations (optionally clustered via BigScape into GCFs), GNPS molecular networking spectra and molecular families, and you seek to computationally link biosynthetic gene clusters to observed metabolites without manual curation.
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40. ▌
    Hierarchical Spectrum Object Construction · holobiomicslab

    Use when immediately after parsing and validating raw LC-MS/MS data files (mzML, mzXML, or vendor formats) when you need to prepare spectral data for fragmentation tree computation, isotope pattern analysis, or molecular formula ranking within the SIRIUS framework.
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41. ▌
    In Memory Optimization For Large Datasets · holobiomicslab

    Use when you are repeatedly querying or iterating over multidimensional MS data stored in MZA HDF5 format (retention time, drift time, m/z dimensions) and profiling shows that repeated disk I/O for the same metadata or scan ranges dominates runtime.
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42. ▌
    Ion Clustering By Retention Time And Mass · holobiomicslab

    Use when your peak table contains suspected mispicked ions—ions with similar m/z and retention time that likely represent the same metabolite split across multiple features due to preprocessing errors.
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43. ▌
    Knowledge Graph Generation And Validation · holobiomicslab

    Use when after completing all per-sample annotation steps (molecular networking, ISDB/spectral matching, SIRIUS/CSI:FingerID, and compounds metadata enhancement with Wikidata IDs and NPClassifier ontology).
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44. ▌
    Lcms Feature Detection And Quantification · holobiomicslab

    Use when you have raw LC-MS data (mzML or equivalent format) from a metabolomics experiment and need to extract a reproducible, quantified feature table with intensity measurements before conducting metabolite identification or statistical analysis.
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45. ▌
    M Z Database Matching With Mass Tolerance · holobiomicslab

    Use when you have a set of observed m/z values extracted from a Cardinal MSImagingExperiment object, raw LC-MS data, or similar high-throughput MS dataset, and you need to assign them to known metabolites in a reference database (HMDB, Lipidmaps, etc.) with control over mass accuracy tolerance and.
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46. ▌
    M Z Retention Time Correspondence Mapping · holobiomicslab

    Use when you have two peak-picked, conventionally aligned untargeted LC-MS metabolomics datasets (as metabData objects) acquired under different conditions or at different times, and you need to determine which features in dataset X correspond to which features in dataset Y so their sample.
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47. ▌
    Mass Fragment Neutral Loss Representation · holobiomicslab

    Use when when you have raw or minimally processed MS/MS spectra (in positive or negative ion mode) and aim to infer recurring fragmentation patterns (Mass2Motifs) using topic modeling.
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48. ▌
    Mass Spec Tolerance Parameter Application · holobiomicslab

    Use when when you have a feature table from Orbitrap LC-MS containing m/z, retention time, and intensity columns, and you need to group individual mass features into putative metabolites that represent the same chemical entity across different ionization states and isotopic compositions.
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49. ▌
    Mass Spectrometer Simulator Configuration · holobiomicslab

    Use when when you have a list of chemical compounds (with m/z values, retention times, and intensities) and need to simulate their acquisition behavior under a specific ionization polarity and mass spectrometer configuration.
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50. ▌
    Mass Spectrometry Chromatogram Extraction · holobiomicslab

    Use when you have raw profile LC-MS data in .mzML format and need to prepare candidate peak regions for classification by a neural network detector (e.g., QuanFormer).
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51. ▌
    Mass Spectrometry Chromatogram Generation · holobiomicslab

    Use when after MS2 annotation and sample alignment have been completed in JPA, when you need to visualize ion chromatograms for quality control, validate feature identities, or export chromatographic evidence for specific metabolic features across multiple samples.
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52. ▌
    Mass Spectrometry Data Loader Integration · holobiomicslab

    Use when you have mass spectrometry data available in multiple identifier formats (GNPS Task ID, Universal Spectrum Identifier, or Feature-Based Molecular Networking task reference) and need to route each format to its specific loader without manual preprocessing.
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53. ▌
    Mass Spectrometry Metadata Interpretation · holobiomicslab

    Use when when integrating LC-MS/MS data from diverse sources (e.g., public repositories like MSV000080102, instrument outputs, or precomputed workflows) into NPDtools pipelines.
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54. ▌
    Mass Spectrometry Substructure Assignment · holobiomicslab

    Use when after Mass2Motif discovery via LDA on preprocessed MS/MS spectra, when you have a set of inferred motifs (fragments and neutral losses with LDA probabilities) and need to assign putative substructure identities rather than retain anonymous motif labels.
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55. ▌
    Metabolite Feature Annotation Aggregation · holobiomicslab

    Use when after selecting statistically significant features from multi-assay LC-MS metabolomics datasets (e.g., via MB-VIP and permutation testing with p < 0.01).
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56. ▌
    Metabolite Signal Extraction From Lc Hrms · holobiomicslab

    Use when you have raw untargeted LC/HRMS data (mzXML, mzML, or netCDF format) from population-scale studies (n > 500 samples) and need to extract a comprehensive peaklist with aligned features across all samples.
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57. ▌
    Metabolomics Model Performance Comparison · holobiomicslab

    Use when you have trained multiple machine learning classifiers (e.g., AdaBoost, SVM, Random Forest) on the same metabolomics peak-quality training set using k-fold cross-validation with repeated runs (e.
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58. ▌
    Metadata Normalization And Reconciliation · holobiomicslab

    Use when you have multiple CSV feature lists from different acquisition methods (e.g., LC-MS vs LC-IMS-MS) or processing software, each using different naming conventions, retention time scales, or m/z precision;
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59. ▌
    Model Uncertainty Quantification Variance · holobiomicslab

    Use when when you have predictions from multiple independently trained models (e.g., ROASMI_1–ROASMI_5) for the same set of compounds in a reversed-phase liquid chromatography system at eluent pH ~2.7, and you need to estimate prediction reliability without ground-truth labels.
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60. ▌
    Molecular Graph Neural Network Adaptation · holobiomicslab

    Use when you have an in-house collection of liquid chromatography spectra and retention time measurements for small molecules, and you want to improve structural identification accuracy by predicting retention times.
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61. ▌
    Ms Spectra Inference With Neural Networks · holobiomicslab

    Use when you have MGF or native MS/MS arrays (mz_array, intensity_array, precursor_mz, adduct) and want to predict the most likely molecular formula. The input spectra must include required MGF fields (TITLE, PRECURSOR_MZ, PRECURSOR_TYPE, COLLISION_ENERGY) or equivalent Python API parameters.
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62. ▌
    Ms2 Fragment M Z Grouping And Aggregation · holobiomicslab

    Use when you have extracted MS/MS spectra for a given metabolomic feature across multiple replicates (e.g., after top-TIC filtering) and need to identify which fragments are reproducible across replicates.
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63. ▌
    Ms2 Spectral Deconvolution And Annotation · holobiomicslab

    Use when you have acquired LC-IMS-MS/MS data (or equivalent multidimensional MS/MS acquisition) in mzML or mzML.gz format and need to disambiguate overlapping fragmentation spectra arising from co-eluting or co-drifting precursor ions.
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64. ▌
    Neural Network Model Inference Deployment · holobiomicslab

    Use when you have a pre-trained neural network model (e.g., MSBERT weights in .
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65. ▌
    Pareto Front Multi Objective Optimization · holobiomicslab

    Use when you have evaluated a parametric denoising strategy (e.g., frequency-based filtering) at multiple threshold values and computed two or more competing metrics (e.g., signal loss and noise reduction) for each threshold.
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66. ▌
    Post Translational Modification Detection · holobiomicslab

    Use when when you have tandem mass spectrometry data (LC-MS/MS in MGF, mzML, mzXML, or mzData format) paired with either genome sequences or precursor peptide predictions, and you want to confirm the presence and identity of modified ribosomally synthesized and post-translationally modified.
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67. ▌
    Probability Prediction Metric Computation · holobiomicslab

    Use when after running ModiFinder's probability generation on a known compound–modified compound pair, you have a vector of per-atom modification probabilities and need to validate whether the predicted probability peaks align with the true modification sites.
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68. ▌
    Pseudo Ms Ms Spectra Ranking And Curation · holobiomicslab

    Use when after executing annotateRC to match six or more lipidomics/metabolomics features against ion fragment databases (e.
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69. ▌
    Retention Time Mass Tolerance Calibration · holobiomicslab

    Use when you have multiple feature tables (CSV files) from different LC-MS analytical experiments, each containing mass, retention time, intensity, isotope, and adduct annotations, and you need to merge them into a single aligned feature matrix.
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70. ▌
    Retention Time Prediction From Structures · holobiomicslab

    Use when you have molecular structures (SMILES or SDF format) for which you need to predict retention time in liquid chromatography, especially when your target dataset contains fewer than ~500 annotated examples.
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71. ▌
    Sensitivity Specificity Tradeoff Analysis · holobiomicslab

    Use when you have a trained neural network model and a labelled validation dataset (with high-quality and low-quality peak annotations), and you need to determine the optimal probability threshold that maximizes the difference between true positive rate and false positive rate for classifying MS1.
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72. ▌
    Siamese Network Architecture Modification · holobiomicslab

    Use when when you need to reduce overfitting in a Siamese neural network trained on mass spectrometry spectral pairs by adding weight regularization, or when users require flexible control over L1 and L2 penalty coefficients rather than hard-coded defaults.
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73. ▌
    Signal Trend Assessment Across Injections · holobiomicslab

    Use when you have QCpool (pooled quality control) samples measured at regular intervals across one or more LC-MS/MS sequences and need to detect whether instrument performance degrades, drifts, or destabilizes during the analytical run.
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74. ▌
    Singular Value Decomposition Metabolomics · holobiomicslab

    Use when when you have a log2-normalized, zero-mean, unit-variance intensity matrix (rows=metabolites, columns=samples) and a curated metabolite set database (e.
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75. ▌
    Spectral Data Preprocessing Normalization · holobiomicslab

    Use when you have raw MS/MS spectral data in MGF format from multiple sources or instruments with inconsistent metadata fields, varying intensity scales, and potential low-quality spectra that need standardization before metadata harmonization or spectral library compilation.
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76. ▌
    Spectral Document Representation Handling · holobiomicslab

    Use when you have per-sample MS2 spectra (in matchms-compatible formats like mzML, mzXML, MGF, or msp) and need to compare metabolomic samples across different LC methods, mass spectrometers, or retention-time regimes—especially when samples are chemodiverse with poor feature overlap or strong RT.
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77. ▌
    Spectral Peak Detection And Deconvolution · holobiomicslab

    Use when you have raw LC-MS data in mzML or mzXML format and need to extract reproducible, high-quality metabolite features (m/z, retention time, intensity) for global metabolomics.
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78. ▌
    Spectral Peak Filtering And Normalization · holobiomicslab

    Use when working with raw or minimally processed MS/MS spectra from repositories like GNPS that contain variable peak intensities, noise, and formatting inconsistencies that would interfere with downstream deep learning models trained on normalized spectral representations.
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79. ▌
    Spectrum Processing Pipeline Construction · holobiomicslab

    Use when you have raw mass spectrometry spectra (in MGF, mzML, or similar formats) that must undergo standardized preprocessing before library matching, similarity searching, or performance benchmarking.
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80. ▌
    Stable Isotope Probing Abundance Modeling · holobiomicslab

    Use when you have a high-resolution mass spectrum (FT scan) containing a peptide precursor at known charge state with known or suspected stable isotope labeling (e.
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81. ▌
    Statistical Filtering Metabolite Features · holobiomicslab

    Use when when you have peak area tables (unlabeled C12 and labeled C13) from LC-MS metabolomics with sample metadata indicating case and control groups, and you need to distinguish true metabolic changes from instrumental noise or batch artifacts before attempting isotopic pairing.
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82. ▌
    Summarized Experiment Object Construction · holobiomicslab

    Use when when you have imported a tab-delimited metabolomics file (via readData or similar) containing columns for compound identifiers, sample/aliquot names, peak areas (primary assay), internal standard areas (secondary assay), and sample type classifications, and you need to organize these into.
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83. ▌
    Tandem Mass Spectra Fragmentation Parsing · holobiomicslab

    Use when when you have raw tandem mass spectra in mz/intensity format with precursor m/z values, and need to extract all fragmentation features (observed peaks and neutral losses) as a foundation for building interpretable machine learning models.
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84. ▌
    Targeted Metabolomics Feature Elaboration · holobiomicslab

    Use when you have targeted metabolomics data with peak area intensities organized in rows (samples) × columns (compounds), accompanying sample metadata indicating which samples are blanks, calibration curve points, or QC samples with known concentration values, and a compound legend assigning.
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85. ▌
    Tissue Specific Metabolite Quantification · holobiomicslab

    Use when you have LC-IM-MS/MS raw data from multiple tissue samples and need to identify and quantify unsaturated sterol lipids at the isomer level (distinguishing double-bond position and stereochemistry).
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86. ▌
    Top N Accuracy And Auc Metric Calculation · holobiomicslab

    Use when you have a ranked candidate list (e.g., BGCs sorted by IOKR or strain-correlation score) for each test spectrum, a known ground-truth BGC for each spectrum, and you want to measure retrieval performance across multiple recall depths (top-1 through top-200) and overall discrimination.
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87. ▌
    Transformer Encoder Architecture Training · holobiomicslab

    Use when you have paired tandem MS spectra and either (1) molecular fingerprints or structures as labels for supervised fingerprint prediction, or (2) both spectra and unpaired structure/SMILES libraries and want to train embeddings for database-free structure lookup.
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88. ▌
    Unmatched Peak Detection And Penalization · holobiomicslab

    Use when when annotating MS/MS spectra against spectral libraries and chimeric spectra (spectra containing fragments from multiple precursor ions) are suspected or known to be present in your dataset.
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89. ▌
    Wmy Network Prediction Confidence Scoring · holobiomicslab

    Use when after initial lipid candidate annotation via spectral library matching (e.g., from XCMS/CAMERA peak alignment and LipidIN EQ module querying), when you need to improve coverage and annotation confidence on unannotated or low-confidence lipid signals.
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90. ▌
    Coefficient Of Variation Threshold Analysis · holobiomicslab

    Use when you have per-feature CV values from quality control analysis of NMR or MS metabolomic data and need to: (1) establish whether your dataset meets FDA reproducibility standards for downstream biomarker discovery or quantification; (2) benchmark data quality against regulatory thresholds;
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91. ▌
    Compound Abundance Quantification From Flow · holobiomicslab

    Use when you have an NMR mixture spectrum (1D or 2D) and a library of reference spectra for pure compounds, and you need to identify which compounds are present and in what proportions.
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92. ▌
    Compound Clustering From Inadequate Spectra · holobiomicslab

    Use when after peak picking has identified individual signals in an INADEQUATE NMR spectrum, apply this skill when you need to collapse thousands of individual peaks into fewer, more interpretable compound-level peak networks.
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93. ▌
    Data Pipeline Module Design And Integration · holobiomicslab

    Use when you have raw spectroscopic datasets from heterogeneous sources (multiple Zenodo repositories with different file formats and scales) that must be jointly normalized, deduplicated, and aligned by molecular identifier to feed a multimodal deep learning architecture.
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94. ▌
    HTTP API Integration With External Services · holobiomicslab

    Use when when your application needs to enrich or predict spectral properties (NMR peaks, molecular structure) by querying external databases, and you have peak data (chemical shift, multiplicity, integration) that must be transformed into a remote service's query format, validated, and the.
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95. ▌
    Molecular Structure Prediction From Spectra · holobiomicslab

    Use when you have preprocessed 1D ¹H and/or ¹³C NMR spectra (as numerical arrays or feature tensors) from an unknown organic compound with ≤19 heavy atoms, and you need to predict both the molecular formula and the connectivity graph of the compound without prior structural hypotheses or reference.
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96. ▌
    Spectral Overview Representation Generation · holobiomicslab

    Use when you have processed LC-MS/MS spectral data (as a .mgf file with feature identifiers) and computed pairwise ms2deepscore similarity scores, and you need to create a 2-D projection suitable for dashboard visualization or high-level pattern discovery without losing similarity structure.
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97. ▌
    Spectral Similarity Matching And Comparison · holobiomicslab

    Use when after extracting and optionally combining MS2 spectra from a chromatographic peak (e.g., at a known m/z value like 304.1131), you need to determine which compound(s) in a reference library match the experimental spectrum.
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98. ▌
    Bam File Coordinate Sorting Verification · holobiomicslab

    Use when before invoking pp.make_fragment_file on a BAM file from alignment or external sources, especially when the BAM's sort order is unknown or when integrating BAM files from multiple sequencing platforms (10X, standard genomics pipelines, or custom aligners) into a unified SnapATAC2 analysis.
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99. ▌
    Dependency Installation And Verification · holobiomicslab

    Use when when setting up HiC-Pro or similar multi-tool pipelines where tool availability and version constraints are prerequisites for downstream analysis.
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100. ▌
     Dna Methylation Block Detection Analysis · holobiomicslab

     Use when you have loaded normalized methylation data from EPIC or 450k arrays and need to identify differentially methylated blocks rather than individual CpG sites or DMRs.
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