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HolobiomicsLab

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1. ▌
   Metabolite Mass To Charge Ratio Matching · holobiomicslab

   Use when you have a negative-mode or positive-mode LC-MS feature table with observed m/z values and peak intensities, and you need to identify which metabolites (by KEGG ID) are likely represented.
   0 installs
2. ▌
   Metabolite Pubchemcid Annotation Mapping · holobiomicslab

   Use when after metabolite annotation has been completed (level-1 confidence via spectral library matching in margheRita or equivalent), and you need to perform pathway enrichment analysis on a subset of significant features (e.
   0 installs
3. ▌
   Metabolomic Feature Retention Statistics · holobiomicslab

   Use when after applying the CV_ratio() filtering function to a normalized metabolomic feature matrix (e.g., Urine_RP_NEG_norm.txt) in margheRita, generate retention statistics to report how many features passed the threshold (CV ratio > 1.0) and characterize the distribution of retained CV ratios.
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4. ▌
   Metabolomic Feature Table Interpretation · holobiomicslab

   Use when after quality control, filtering, and normalization of an MS-DIAL-derived feature abundance matrix (e.g., Urine_RP_NEG_norm.txt or Urine_RP_POS_norm.txt), when you have samples assigned to discrete experimental classes (e.
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5. ▌
   Missing Value Imputation In Metabolomics · holobiomicslab

   Use when after feature extraction and quality control filtering (blank masking, sample dropping, normalization) have been applied, but before statistical analysis or machine learning.
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6. ▌
   Molecular Formula Inference From Adducts · holobiomicslab

   Use when when you have grouped features consolidated into empirical compounds (EmpCpds) with inferred adduct assignments from khipu, and need to perform MS1-level annotation by matching neutral formulas against JMS-compliant reference libraries (HMDB, LMSD).
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7. ▌
   Molecular Network Annotation Integration · holobiomicslab

   Use when you have a GNPS mass spectral molecular network (classical or feature-based) and MS2LDA-derived Mass2Motif data, and you need to annotate network nodes with both chemical class information from GNPS library matches and substructural motifs from MS2LDA to enable joint interpretation of.
   0 installs
8. ▌
   Molecular Structure Graph Representation · holobiomicslab

   Use when when you have a set of chemical structures (SMILES strings or SDF files) that need to be processed for training a graph neural network model on molecular property prediction tasks, specifically when the target property (e.
   0 installs
9. ▌
   Ms Ms Fragment Assignment And Annotation · holobiomicslab

   Use when when you have predicted MS/MS fragments from quantum chemistry calculations on N-Me derived unsaturated sterol structures and need to map each fragment to its precursor lipid, calculate exact m/z values, estimate relative intensities, and produce a machine-readable reference table for.
   0 installs
10. ▌
    Multi Hypothesis Scoring And Enumeration · holobiomicslab

    Use when when you have an unknown tandem mass spectrum (MS/MS peaks with m/z and intensity) and need to assign both the precursor chemical formula and its ionization adduct type (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+).
    0 installs
11. ▌
    Multi Sample Abundance Pattern Detection · holobiomicslab

    Use when after initial retention-time-based feature grouping (e.
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12. ▌
    Multivariate Statistical Quality Control · holobiomicslab

    Use when after data normalization (Step 7) when you have a preprocessed feature matrix and need to identify samples that deviate significantly from the multivariate center of the data distribution due to instrumental drift, batch effects, sample degradation, or genuine biological outliers that.
    0 installs
13. ▌
    Neural Network Regularization Techniques · holobiomicslab

    Use when when training a deep neural network on paired MS/MS spectra to predict structural similarity scores, especially when the training dataset is moderate-sized (109,734 spectra across 15,062 molecules) and overfitting risk is high.
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14. ▌
    Neutral Mass Inference From Ion Ensemble · holobiomicslab

    Use when you have a connected subnetwork of feature ions that have been validated as belonging to the same empirical compound (khipu instance), with isotope and adduct edges assigned, and you need to estimate the true neutral mass M0 rather than relying on any single observed m/z.
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15. ▌
    Noise Model Implementation And Selection · holobiomicslab

    Use when when constructing synthetic LC-MS/MS runs in SMITER, you must choose a noise model before calling write_mzml.
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16. ▌
    Nontargeted Metabolomics Data Processing · holobiomicslab

    Use when you have raw LC-MS data in vendor or mzML format and need to systematically discover and extract all detectable metabolite features across the full retention time range, without predefined target lists.
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17. ▌
    Nucleoside Fragmentation Model Selection · holobiomicslab

    Use when when your input biomolecule is a nucleoside or modified nucleoside (not a peptide) and you are building a synthetic LC-MS/MS run with SMITER.
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18. ▌
    Open Modification Peptide Identification · holobiomicslab

    Use when when you have high-resolution tandem mass spectra (query spectra) and need to identify peptides with unanticipated or open modifications (any mass shift on any amino acid position).
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19. ▌
    Pathway Activity Decomposition Via Plage · holobiomicslab

    Use when you have a log2-transformed, standardized peak intensity matrix (rows = metabolite features, columns = samples) with compound annotations mapped to curated pathway databases (KEGG, Reactome, or custom metabolite sets), and you need to rank pathways by their activity level while tolerating.
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20. ▌
    Pathway Over Representation Analysis Ora · holobiomicslab

    Use when apply ORA after conducting statistical tests (e.
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21. ▌
    Peak Prominence Calculation Local Maxima · holobiomicslab

    Use when when processing LC-MS mass tracks (EICs) and you need to identify genuine chromatographic peaks rather than noise artifacts.
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22. ▌
    Peptide Spectrum Representation Learning · holobiomicslab

    Use when you have a collection of MS/MS spectra (in mzML or MGF format) and need to group or compare spectra from the same peptide without prior sequence annotation.
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23. ▌
    Pfas Candidate Prioritization By Scoring · holobiomicslab

    Use when you have a feature list (containing m/z, retention time, and molecular formula or neutral mass per feature) extracted from LC- or GC-HRMS data in mzML format with data-dependent acquisition, and you need to rank or flag features as likely PFAS compounds to reduce manual review burden in.
    0 installs
24. ▌
    Polynomial Regression Quality Assessment · holobiomicslab

    Use when after fitting a polynomial calibration model to tunemix reference data in DEIMoS, assess whether the model explains sufficient variance in the m/z–drift-time–CCS relationship.
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25. ▌
    Pytorch State Dict Checkpoint Management · holobiomicslab

    Use when when training a multi-component deep learning model where some components (e.g., a pretrained TCN spectrum encoder) should remain frozen while others (e.
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26. ▌
    Qc Replicate Identification And Grouping · holobiomicslab

    Use when you have a QC-annotated LC-MS feature table (CSV or data frame format with sample metadata) and need to isolate QC replicate measurements prior to computing quality metrics such as D-Ratio or performing signal drift correction.
    0 installs
27. ▌
    Retention Time Intensity Data Extraction · holobiomicslab

    Use when you have xcms-processed LC-MS data with detected misaligned feature groups and need to recover the underlying raw retention time–intensity profiles for each feature and sample combination prior to realignment.
    0 installs
28. ▌
    Retention Time Prediction Chromatography · holobiomicslab

    Use when you have a new chromatographic dataset with molecular structures (as InChI or SMILES) and experimentally measured retention times, and you want to predict retention times for unannotated metabolites or validate predictions on a held-out test set without retraining from scratch.
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29. ▌
    Retention Time Window Tolerance Checking · holobiomicslab

    Use when you have acquired LC-MS data and need to verify run quality before proceeding to metabolite identification or quantification. Retention time checking is essential when: (1) you have established expected retention time ranges for known internal standards or reference compounds;
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30. ▌
    Ripp Peptide Identification From Spectra · holobiomicslab

    Use when you have LC-MS/MS spectra (MGF, mzXML, mzML, or mzData format) and either raw genome nucleotide sequences or antiSMASH/BOA genome mining tool output, and you need to identify which RiPPs are present in your sample.
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31. ▌
    Robust Statistical Spread Quantification · holobiomicslab

    Use when after drift correction of LC-MS peak intensity data, when you need to identify metabolic features with excessive internal spread (within-group variability in QC samples) or poor biological-to-technical reproducibility (QC-versus-sample spread).
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32. ▌
    Siamese Network Inference Spectrum Pairs · holobiomicslab

    Use when you have a collection of preprocessed tandem mass spectra (binned into 10,000 equally-sized m/z bins, intensities square-root transformed, top 1,000 peaks retained), a trained MS2DeepScore Siamese model, and you need to predict structural similarity scores (Tanimoto or Dice) for all or a.
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33. ▌
    Similarity Matrix Generation And Storage · holobiomicslab

    Use when when you have cleaned and filtered mass spectrometry spectral data (in mzML, mzXML, msp, MGF, or JSON format) and need to identify or rank spectra by similarity for library matching, metabolite annotation, or network analysis.
    0 installs
34. ▌
    Smiles Structure Representation Handling · holobiomicslab

    Use when you have generated a TPs object (via generateTPs) containing transformation products with structural information and need to export these structures for MetFrag database creation, suspect screening list construction, or chemical similarity filtering.
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35. ▌
    Spectral Cluster Connectivity Assessment · holobiomicslab

    Use when after running spectral networking on tandem MS data and obtaining a network graph, when you need to assess which spectra cluster together, determine cluster representatives, and propagate RiPP identifications across clusters at distance 1 or 2 to enlarge the set of identified RiPPs beyond.
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36. ▌
    Spectral Feature Annotation And Labeling · holobiomicslab

    Use when you have a detected feature table (m/z, drift_time, retention_time, intensity) and need to identify and label C13 isotopic clusters for singly-charged features (z=+1).
    0 installs
37. ▌
    Spectral Library Query Reference Pairing · holobiomicslab

    Use when when evaluating a trained spectral embedding model on publicly available datasets (GNPS, MoNA, MTBLS1572, MassBank, or MassSpecGym) and you need to report averaged performance metrics with standard deviation to demonstrate robustness and reproducibility.
    0 installs
38. ▌
    Spectral Peak Alignment With Mass Offset · holobiomicslab

    Use when when comparing two MS/MS spectra where the precursor m/z values differ (indicating potential mass modifications, adducts, or related compounds), and you want to detect structurally conserved fragmentation patterns that would be missed by direct m/z matching.
    0 installs
39. ▌
    Spectral Peak Smoothing Artifact Removal · holobiomicslab

    Use when raw Agilent MassHunter (.d) or UIMF mass spectrometry files exhibit jagged or noisy peaks, particularly for low-abundance ions where signal-to-noise ratio is poor.
    0 installs
40. ▌
    Spectral Similarity Network Construction · holobiomicslab

    Use when after acquiring MS/MS spectral data from untargeted metabolomics experiments and having candidate transformed structures from biotransformation rule application.
    0 installs
41. ▌
    Spectrum Preprocessing And Normalization · holobiomicslab

    Use when you have raw MS/MS spectra in MGF or other standard formats that need to be ingested into a machine learning pipeline for cross-modal matching against molecular structures, or when spectra from different collision energy levels or instruments require standardization before comparative.
    0 installs
42. ▌
    Spectrum Preprocessing Precursor Masking · holobiomicslab

    Use when when training a formula-prediction model with a frozen pretrained TCN spectrum encoder and unfrozen FormulaEncoder and RescoreHead components, or when you suspect the model may use precursor intensity as a spurious feature rather than fragment-pattern information for molecular formula.
    0 installs
43. ▌
    Tandem Mass Spectra Embedding Generation · holobiomicslab

    Use when you have tandem mass spectra (in .msp or compatible format) from instruments like Orbitrap, and you need to compute library matching scores, cluster spectra by chemical similarity, or embed spectra into a learned vector space for downstream similarity or clustering tasks.
    0 installs
44. ▌
    Technical Variation Removal In Lcms Data · holobiomicslab

    Use when after merging feature tables from non-targeted LC-MS/MS data and before statistical analysis, when samples were acquired across multiple instrument runs, different days, or instrumental calibration cycles.
    0 installs
45. ▌
    Theoretical Isotope Envelope Calculation · holobiomicslab

    Use when when processing mass spectrometry data from stable isotope probing (SIP) experiments where peptides contain known levels of heavy isotope incorporation (13C, 15N, 2H, 18O), and you need to annotate observed MS2 peaks by matching them to theoretical B and Y ion fragments.
    0 installs
46. ▌
    Time And Memory Complexity Visualization · holobiomicslab

    Use when when deploying a metabolomics processing tool (such as asari) and needing to predict resource requirements or validate claimed scalability on laptop-class hardware (≤16 GB RAM, single CPU core).
    0 installs
47. ▌
    Unknown Chemical Extraction From Spectra · holobiomicslab

    Use when when you have an existing real mzML file from a metabolomics LC-MS/MS acquisition (e.g., beer or urine samples) and need to populate a virtual mass spectrometer with the actual chemicals that were measured, so that you can replay the acquisition with alternative fragmentation strategies (e.
    0 installs
48. ▌
    Untargeted Metabolomics Feature Analysis · holobiomicslab

    Use when you have a feature table from untargeted metabolomics (with m/z, retention time, and p-values from differential abundance testing) but lack or wish to bypass metabolite annotation.
    0 installs
49. ▌
    Compound Identification Ranking Evaluation · holobiomicslab

    Use when after training a FlavorFormer model end-to-end with weighted loss on 1H NMR spectra and compound labels, apply this skill to a held-out test set to measure compound identification accuracy and ranking quality.
    0 installs
50. ▌
    Fragment Assembly Transformer Architecture · holobiomicslab

    Use when when you have 1D NMR spectra (1H and/or 13C) of an unknown compound with up to ~19 heavy atoms and need to predict both molecular formula and connectivity without manual structure hypothesis generation.
    0 installs
51. ▌
    Linear Score Calculation From Coefficients · holobiomicslab

    Use when when you have 1H-NMR metabolite measurements from Nightingale Health assayed on a new cohort and wish to compute risk scores (e.g., all-cause mortality, cardiovascular event, type 2 diabetes) using published metabolic biomarker weights from a reference study.
    0 installs
52. ▌
    Metabolic Age Prediction From Nmr Features · holobiomicslab

    Use when you have Nightingale Health 1H-NMR metabolomics data (feature matrix with named metabolite columns) and need to compute predicted metabolic age for each sample, typically to assess whether individuals' metabolic profiles align with or diverge from age-expected trajectories.
    0 installs
53. ▌
    Metabolomics Model Coefficient Application · holobiomicslab

    Use when you have a matrix of Nightingale Health 1H-NMR metabolomics measurements (samples × features) and need to generate predicted metabolic scores published in peer-reviewed studies (MetaboAge, mortality score, cardiovascular event risk, Type-2 diabetes score, COVID-severity score, or surrogate.
    0 installs
54. ▌
    Multi Modal Spectroscopic Data Integration · holobiomicslab

    Use when you have acquired complementary spectroscopic measurements (NMR, HSQC, COSY, IR) for the same molecular sample and need to combine them for structure elucidation.
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55. ▌
    Pre Analytical Delay Effect Quantification · holobiomicslab

    Use when you have uploaded a pre-analytical data table containing sample metadata, processing delay annotations (pre- and post-centrifugation timestamps or duration), and paired NMR metabolomic measurements for a sample cohort, and you need to quantify how delays at different time-points affect.
    0 installs
56. ▌
    Published Metabolic Profile Implementation · holobiomicslab

    Use when you have Nightingale Health 1H-NMR metabolomics measurements for a new cohort and wish to compute one or more established metabolic risk scores (mortality, MetaboAge, cardiovascular event, type-2 diabetes, COVID-19 severity) without recalibration.
    0 installs
57. ▌
    Spectral Quality Filtering Signal To Noise · holobiomicslab

    Use when you have raw spectroscopic datasets (NMR, HSQC, COSY, IR) in standardized array or DataFrame format and need to curate them for multimodal transformer training.
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58. ▌
    Structural Scaffold Feature Identification · holobiomicslab

    Use when you have 2D NMR spectral data (HSQC, HMBC, COSY) from multiple samples in a library or mixture and need to identify which structural scaffolds are shared across samples, prioritize samples for further analysis based on scaffold novelty or frequency, or characterize the core structural.
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59. ▌
    Background Peak Selection Normalization · holobiomicslab

    Use when after computing expected accessibility from filtered peak and sample counts, and before computing final deviation scores. Use this skill when working with sparse ATAC-seq or DNase-seq data where GC bias and accessibility depth are known confounders of motif-associated variability.
    0 installs
60. ▌
    Benchmark Table Parsing And Aggregation · holobiomicslab

    Use when you are reproducing a comparative benchmarking claim (e.
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61. ▌
    Chromatin Accessibility Bias Correction · holobiomicslab

    Use when you have loaded raw ATAC-seq fragment counts into a SummarizedExperiment object and are preparing to compute motif deviations.
    0 installs
62. ▌
    Numeric Range Validation Bioinformatics · holobiomicslab

    Use when after applying a quantitative analysis function (e.g., cooltools.insulation, contact frequency calculations) to Hi-C cooler files or other genomic datasets, validate that the output numeric columns contain values within plausible ranges (e.
    0 installs
63. ▌
    Pep8 Code Style Compliance Verification · holobiomicslab

    Use when developing or reviewing Python code for a scientific package (e.g., cooltools) that targets collaborative development with multiple contributors.
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64. ▌
    Chemical Structure Similarity Matching · holobiomicslab

    Use when when you have a set of query chemicals (e.g., ethyl hexanoate, methyl salicylate) and need to find their -matched structural analogues within a reference library (e.
    0 installs
65. ▌
    Community Dependent Reaction Inference · holobiomicslab

    Use when you have consensus metabolic reconstructions for multiple community members (e.g., plant-associated microbes or plant-microbial consortia) and those individual models contain incomplete or disconnected metabolic pathways.
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66. ▌
    Cosine Annealing Schedule Optimization · holobiomicslab

    Use when when training a regularized deep neural network for molecular property regression (e.g., retention time prediction on the METLIN SMRT dataset with 80,038+ samples), use cosine annealing warm restarts to escape plateaus and improve convergence.
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67. ▌
    CSV Data Aggregation And Deduplication · holobiomicslab

    Use when when you have downloaded a multi-file .csv library repository (e.g., LipidMatch) and need to verify that it meets minimum thresholds for species diversity (e.g., 500,000+ distinct lipid species) and category breadth (e.g., 60+ lipid-type categories).
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68. ▌
    Custom Code Execution In Data Pipeline · holobiomicslab

    Use when when standard conversion directives (headers, collate, fields_to_headers, exclusion_headers, values_to_str, sort_by, test) cannot express the required transformation logic, or when domain-specific aggregation, conditional logic, or value derivation must be applied to records after field.
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69. ▌
    Dimensionality Reduction Visualization · holobiomicslab

    Use when after generating large feasible flux distributions (e.g., 1 million sampled solutions per cell line) from constrained metabolic models, apply t-SNE when you need to assess whether distinct biological samples (e.
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70. ▌
    Dynamic Method Signature Introspection · holobiomicslab

    Use when when building an automated converter discovery and job enumeration system where converter classes are dynamically loaded from package directories and you need to extract and validate their internal conversion method signatures without prior knowledge of which converters will be available.
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71. ▌
    Execution Time Aggregation By Category · holobiomicslab

    Use when you have measured execution times from multiple scripts that exercise different combinations of categorical variables (e.g., plot types: chromatogram, mobilogram, peakmap, peakmap-marginals, spectrum, subplots;
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72. ▌
    Feature Condition Comparative Analysis · holobiomicslab

    Use when when you have molecular structures, a regression target (e.g. retention time), and want to establish whether one class of molecular features (e.g., fingerprints) outperforms another (e.g., descriptors) or whether combining them yields marginal gains.
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73. ▌
    Fingerprint Vector Loading And Parsing · holobiomicslab

    Use when when you have deposited or archived biosynfoni fingerprint vectors (such as from Zenodo 10.5281/zenodo.14822624) and need to ingest them into a Python workflow to compute distributional statistics, bit-frequency profiles, sparsity metrics, or pairwise similarity coefficients.
    0 installs
74. ▌
    Fragment Canonicalization And Matching · holobiomicslab

    Use when you have a collection of molecular fragments (e.g., from molecular decomposition, retrosynthesis, or synthetic planning) that must be matched to known fragment libraries or standardized representations before feeding them into a transformer assembly model.
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75. ▌
    Inchikey Structural Similarity Scoring · holobiomicslab

    Use when you have candidate library matches from MS2Deepscore ranking (top 2000 spectra per query) with InChIKey annotations, and need to quantify structural similarity between query and candidate compounds to inform downstream match ranking and filtering by MS2Query's random forest model.
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76. ▌
    Internal Standardization Lipid Mapping · holobiomicslab

    Use when you have (LC-)IM-MS lipidomics data from samples spiked with U13C-labeled yeast extract, measured CCS values for both labeled and unlabeled lipids, and need to quantify systematic CCS bias between your instrument and a reference library before applying bias correction.
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77. ▌
    Mass Spectrum Histogram Interpretation · holobiomicslab

    Use when after computing all pairwise mass differences from a mass spectrometry imaging dataset, when you need to identify which mass differences correspond to real molecular adducts (e.g., metabolite–matrix or metabolite–salt ions) rather than noise.
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78. ▌
    Metabolite Background Set Construction · holobiomicslab

    Use when when preparing to run Over-representation Analysis (ORA) on metabolomics pathway data, after you have loaded both a metabolomics pathway database (e.g., KEGG, MetExplore) and an experimental detection list (metabolites measured in your study).
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79. ▌
    Metadata Field Extraction From Headers · holobiomicslab

    Use when you have tabular data (CSV or Excel) with column headers annotated using MESSES tagging syntax (#<table_name>.id for record identifiers, #.
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80. ▌
    Microbe Metabolite Module Construction · holobiomicslab

    Use when when you have trained multi-layer perceptron neural network models on paired microbiome-metabolome data (from ≥10-fold cross-validation iterations) and need to identify functional modules—groups of microbes and metabolites with co-varying or synergistic relationships—for systems-level.
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81. ▌
    Molecular Graph Representation Parsing · holobiomicslab

    Use when you have molecular identifiers (SMILES strings or molecular structure files) that need to be converted into node-edge graph tensors for input to message passing neural network models like chemprop or chemprop-IR.
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82. ▌
    Multiple Testing Correction Bonferroni · holobiomicslab

    Use when when you have computed raw p-values for multiple independent statistical tests (e.g., Pearson correlation tests across all pairwise ion combinations in MSI data) and need to report which results remain significant after accounting for multiple comparisons.
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83. ▌
    Package Version Compatibility Checking · holobiomicslab

    Use when after creating a fresh conda environment from a pinned dependency specification (environment.yml or requirements.txt) and installing packages via conda and/or pip.
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84. ▌
    Python Package Dependency Installation · holobiomicslab

    Use when you need to enable optional modules in Pyteomics that depend on external libraries not bundled with the core package—such as h5py and hdf5plugin for mzMLb format access, sqlalchemy for Unimod database queries, or psims for ProForma parsing.
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85. ▌
    Rdkit Molecular Descriptor Computation · holobiomicslab

    Use when when annotating .msp mass spectrometry files with chemical structure metadata and you need fast, offline molecular transformations (SMILES↔InChI, canonical SMILES generation) without network latency or service availability constraints;
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86. ▌
    Robustness Analysis Under Perturbation · holobiomicslab

    Use when you have completed pathway analysis using multiple competing methods (e.g., PALS, ORA, GSEA) on a metabolomics peak intensity dataset and need to verify that ranking results remain stable when input data is intentionally corrupted.
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87. ▌
    Search Mode Configuration Optimization · holobiomicslab

    Use when when you have a calibrated FT-ICR mass spectrum (e.g., ESI-NEG mode) and need to decide between rapid single-assignment (first_hit=True) and exhaustive multi-assignment (first_hit=False) modes.
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88. ▌
    Spectra Data Extraction And Subsetting · holobiomicslab

    Use when when you need to extract m/z and intensity peak values from a Spectra object backed by MsBackendMzR or similar on-disk backends; when analyzing subsets of spectra without loading all peaks into memory;
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89. ▌
    Spectrum Record Consistency Validation · holobiomicslab

    Use when when you have a mass spectrometry data file (such as mzPeak) that has been read by two or more independent implementations (e.g., Rust, Python/pyarrow, R/arrow) and need to verify that all implementations produce identical spectrum metadata, data types, row counts, and numerical values.
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90. ▌
    Statistical Distribution Visualization · holobiomicslab

    Use when after computing aggregate statistics (mean, median, standard deviation, frequency distributions, similarity coefficients) over a large dataset of molecular fingerprints or feature vectors, when you need to verify that computed metrics exhibit expected distributional shapes and to identify.
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91. ▌
    Batch Effect Correction And Adjustment · holobiomicslab

    Use when your m/z peak data spans multiple batches (recorded in metadata as a batch ID column) or samples have varying concentrations that are documented in metadata.
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92. ▌
    Batch Effect Correction Chromatography · holobiomicslab

    Use when when analyzing untargeted LC/HRMS data from population-scale projects (n > 500) spanning multiple sample batches or instrument runs, peaks with identical or near-identical m/z values appear at systematically shifted retention times across batches due to instrument drift, column aging, or.
    0 installs
93. ▌
    Biosynthetic Gene Cluster Tokenization · holobiomicslab

    Use when you have GenBank-formatted BGC records and need to prepare them for sub-cluster detection, domain pattern analysis, or comparison across multiple BGCs.
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94. ▌
    Bonferroni Multiple Testing Correction · holobiomicslab

    Use when when you have performed many pairwise correlation tests between candidate parent and adduct ion intensity pairs in MSI data and need to identify statistically significant relationships while controlling for multiple-comparison bias.
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95. ▌
    Calibration Curve Fitting Metabolomics · holobiomicslab

    Use when your experiment contains calibration-line samples with known spiked concentrations and you need to convert compound/internal-standard ratios into absolute concentrations for study samples (those with concentration = NA).
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96. ▌
    Cascade Search Strategy Implementation · holobiomicslab

    Use when when performing open modification spectral library searches on high-resolution mass spectra where computational cost is prohibitive if every query is scored against every library spectrum.
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97. ▌
    Chemical Similarity Metric Aggregation · holobiomicslab

    Use when when you have an unknown metabolite compound with mass spectral data, have retrieved candidate structures from a molecular structure database (PubChem, HMDB), and have obtained predictions of structurally related metabolites from a deep-learning semantic similarity model (e.g., DeepMASS2).
    0 installs
98. ▌
    Chemical Structure Format Verification · holobiomicslab

    Use when working with mass spectrometry spectral libraries (GNPS, MoNA, MTBLS1572, MassBank) that have been preprocessed by prior teams but may contain formatting errors or entries with missing/null SMILES fields.
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99. ▌
    Chemical Structure To Taste Prediction · holobiomicslab

    Use when you have a CSV or EXCEL file containing molecular descriptors (pre-computed structural features) for one or more chemical compounds, and you need binary bitterness predictions (bitter vs. non-bitter) for each molecule.
    0 installs
100. ▌
     Chemical Transformation Library Lookup · holobiomicslab

     Use when you have a histogram of mass differences (from pairwise comparisons of detected m/z values in MALDI-MS or MSI data) and need to annotate which differences correspond to known molecular adducts—particularly when investigating unexpected or ambiguous peaks in the mass spectrum, or when.
     0 installs