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HolobiomicsLab

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1. ▌
   Gpu Accelerated Nearest Neighbor Indexing · holobiomicslab

   Use when when searching unknown mass spectra against large high-resolution spectral libraries for open modification identification and you need to reduce search latency from seconds to milliseconds while maintaining sensitivity and specificity.
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2. ▌
   Graph Database Indexing And Serialization · holobiomicslab

   Use when you have retrieved and deduplicated chemical formulae and metadata from multiple heterogeneous sources (HMDB, ChEMBL, PubChem) and extracted both structural relationships (DBEdges) and biological reactant pairs (BioEdges from KEGG), and now need to merge them into a single queryable.
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3. ▌
   Graph Visualization Interactive Rendering · holobiomicslab

   Use when after constructing a NetworkX graph object from structural clusters (via MamsiStructSearch), when you need to interactively explore feature relationships or publish a static network diagram showing isotopologue links, adduct relationships, and cross-assay connections.
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4. ▌
   Inchikey Extraction From Spectral Library · holobiomicslab

   Use when you have a GNPS DBResult file from spectral library matching that lacks ClassyFire superclass, class, and subclass columns, and you need to restore chemical ontology annotations by submitting compound identifiers to external batch classification services for re-annotation.
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5. ▌
   Interactive Filter Definition Trelliscope · holobiomicslab

   Use when you have omics statistical results (p-values, effect sizes, fold-changes) mapped to individual trelliscope panel rows and need to allow end users to interactively explore subsets of features (proteins, metabolites, genes) by thresholds or annotations without re-running the visualization.
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6. ▌
   Interactive Region Of Interest Refinement · holobiomicslab

   Use when after loading a LA-ICP-MS image into pew², you need to isolate tissue or sample regions from instrumental background or matrix before calculating elemental concentrations or ratios.
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7. ▌
   Internal Standard Area Aberrancy Flagging · holobiomicslab

   Use when after building a SummarizedExperiment from metabolomics data but before quantitative analysis.
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8. ▌
   Linear Regression Absolute Quantification · holobiomicslab

   Use when you have a set of calibration samples (spiked compounds) with known concentrations, measured compound and internal-standard peak areas, and you need to convert compound/internal-standard ratios from study samples into absolute concentrations.
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9. ▌
   Lipidomics Experiment Object Construction · holobiomicslab

   Use when when you have lipidomics quantitation data (lipid abundances across samples) that you need to load into a unified, annotated R object for analysis—either from public Metabolomics Workbench studies via API, Skyline mass spectrometry software exports, or a custom numerical matrix with.
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10. ▌
    Low Resolution Mass Spectrometry Analysis · holobiomicslab

    Use when you have low-resolution MS data (e.g., from quadrupole instruments) in vendor format, a list of target compounds with known monoisotopic m/z and retention times, and need to extract per-isotopologue area and intensity values.
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11. ▌
    Mass Spectral Library Benchmark Execution · holobiomicslab

    Use when you have access to the Flash Entropy Search implementation and want to validate that real-time spectral library querying achieves the reported timing and throughput metrics under the same library sizes, query parameters, and measurement methodology documented in the paper.
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12. ▌
    Mass Spectrometry Data Quality Assessment · holobiomicslab

    Use when when raw mass spectrometry spectral data has been imported into matchms from common file formats (mzML, mzXML, msp, MGF, JSON) and you need to assess whether metadata fields are correctly normalized, validated against schema requirements, and peaks are appropriately filtered before.
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13. ▌
    Mass Spectrometry Data Structure Decoding · holobiomicslab

    Use when when you have a mzPeak file (uncompressed ZIP archive containing Parquet tables) and need to access decoded spectral data arrays (m/z, intensity), spectrum metadata (scan descriptors, precursors), or chromatogram data in a form suitable for Python/R analysis pipelines.
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14. ▌
    Mass Spectrometry Dataset Post Processing · holobiomicslab

    Use when you have a formula-assigned FT-ICR MS dataset (CSV or tab-delimited table with molecular formulas and mass values) and need to quantify molecular chemodiversity, characterize elemental stoichiometry patterns, or explore transformation pathways across samples.
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15. ▌
    Mass Spectrometry Imaging Data Processing · holobiomicslab

    Use when you have raw or processed MSI data (in imzML or rMSIproc formats) and need to identify and annotate matrix-related peaks before statistical analysis or metabolite identification. Use it specifically when your MSI experiment employed a chemical matrix (e.
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16. ▌
    Mass Spectrometry Peak Intensity Encoding · holobiomicslab

    Use when working with imaging mass spectrometry (IMS) datasets where you need to (1) automatically identify marker ions without manual annotation, (2) reduce peak intensity dimensionality while preserving spatial relationships between measurement points, or (3) apply iterative peak picking.
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17. ▌
    Mass Spectrometry Target List Preparation · holobiomicslab

    Use when you have a raw .xlsx or tabular file listing candidate compounds with theoretical or measured m/z values, expected retention times, and ionization polarities, and you need to feed this into TARDIS or another targeted LC–MS analysis pipeline.
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18. ▌
    Mass Spectrometry Wrapper Function Design · holobiomicslab

    Use when when you have a Spectra-based MS analysis workflow in R but need access to specialized spectral similarity, filtering, or normalization algorithms available only in Python libraries.
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19. ▌
    Metabolic Module Significance Calculation · holobiomicslab

    Use when after you have constructed metabolic correlation modules via WGCNA on normalized and imputed metabolomic data, and you want to test whether specific modules (e.g., Module 2: Creatine/Glutathione metabolism) associate significantly with a categorical sample trait.
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20. ▌
    Metabolite Detection Frequency Estimation · holobiomicslab

    Use when after loading spatial metabolomics data (from CSV, imzML, or merged positive/negative ionization modes) into an AnnData object, and before filtering metabolites or performing cross-modal integration.
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21. ▌
    Metabolite Level Expression Summarization · holobiomicslab

    Use when after loading spatial metabolomics data (from CSV, imzML, or merged positive/negative mode files) into an AnnData object and before filtering or integrating with spatial transcriptomics.
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22. ▌
    Metabolomic Biomarker Pathway Association · holobiomicslab

    Use when after marker identification (via fold-change, PLS-DA, t-test, or other feature selection methods) has produced a ranked list of metabolic features.
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23. ▌
    Metabolomic Meta Analysis Workflow Design · holobiomicslab

    Use when you have multiple metabolomic studies with aggregate summary statistics (p-values, fold-change estimates) and need to perform meta-analysis while harmonizing compound nomenclature across datasets. Use this skill when the underlying R package (e.
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24. ▌
    Metabolomics Data Loading Into Containers · holobiomicslab

    Use when you have raw metabolomics data (e.g., in standard bioinformatics formats supported by maplet) and need to initialize an analytical pipeline that will integrate multiple processing and analysis steps.
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25. ▌
    Metadata Column Profiling Across Datasets · holobiomicslab

    Use when you are curating metabolomics datasets with variable column naming conventions and need to detect, normalize, and populate standardized database-ID columns (HMDB_ID, PubChem_ID, KEGG_ID, etc.) across many mwTab files before deposition.
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26. ▌
    Molecular Descriptor Graph Representation · holobiomicslab

    Use when when you need to train or evaluate a graph neural network for molecular property prediction (collision cross section, ion mobility, or related descriptors) and have access to SMILES strings and/or 3D conformer coordinates.
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27. ▌
    Msi Imaging Experiment Class Manipulation · holobiomicslab

    Use when when loading mass spectrometry imaging data from imzML or Analyze 7.
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28. ▌
    Msi Preprocessing Parameter Configuration · holobiomicslab

    Use when you have raw line-scan MSI data (from Bruker .d/.baf, converted to .mzML, or other supported formats) and need to configure MSIGen before calling msigen() to generate image arrays.
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29. ▌
    Msms Spectrum Data Structure Manipulation · holobiomicslab

    Use when you have raw or downloaded MSMS spectra (from online resources via Universal Spectrum Identifier, or from local mzML/mzXML files) that require standardization, cleaning, and fragment assignment before visualization, statistical comparison, or machine-learning feature extraction.
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30. ▌
    Multi Model Molecular Prediction Ensemble · holobiomicslab

    Use when you have a set of molecular structures (as .sdf or .csv with SMILES) and need to predict metabolic susceptibility across multiple CYP isoforms (e.g., 1A2, 2A6, 2B6) to prioritize drug candidates or screen for potential metabolic liabilities across the major human CYP enzyme portfolio.
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31. ▌
    Multi Omics Output Directory Organization · holobiomicslab

    Use when after completing pathway enrichment analysis on differentially expressed features from genes, miRNA, proteins, or lipids using either clusterProfiler or biotranslator.
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32. ▌
    Multivariate Batch Confounding Assessment · holobiomicslab

    Use when after data preprocessing and quality control, when your metabolomics feature matrix and sample metadata show evidence of batch structure (e.
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33. ▌
    Natural Product Classification Prediction · holobiomicslab

    Use when you have molecular structures encoded as SMILES strings and need to classify them into natural-product chemical classes.
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34. ▌
    Natural Product Representation Assessment · holobiomicslab

    Use when you have a natural product dataset with taxonomy labels (Class, Superclass, or Pathway), pre-trained molecular models (both natural-product-specialized and synthetic-molecule baselines), and a question about whether standard molecular representations are sufficient for natural product.
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35. ▌
    Natural Product Structural Representation · holobiomicslab

    Use when when you need to represent natural product molecules as numerical feature vectors for downstream machine learning (e.g., biosynthetic class prediction), comparative analysis, or when standard chemical fingerprints (e.
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36. ▌
    Neural Network Architecture Instantiation · holobiomicslab

    Use when when you have retrieved a model definition file (e.g., TransGNet.py) and need to verify it can be loaded, configured with correct hyperparameters, and executed on representative multimodal inputs (e.g., molecular graphs and SMILES embeddings) before training or inference.
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37. ▌
    Neural Network Model Deployment In Python · holobiomicslab

    Use when you have a trained conformer-based peak-picking model (in ONNX format) and need to apply it to chromatographic or ion-mobility mass spectrometry data (TransitionGroup objects) to detect peak boundaries and apex positions, generating TransitionGroupFeature records for downstream analysis or.
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38. ▌
    Neural Network Module Architecture Design · holobiomicslab

    Use when when building an end-to-end deep learning model that must predict multiple correlated peptide properties (charge, isotope count, retention time) simultaneously from mass spectrometry data, and you need a principled way to merge learned representations from separate task-specific branches.
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39. ▌
    Overlapping Peak Detection Interpretation · holobiomicslab

    Use when annotating matrix-related signals in MSI datasets where chemical formulas or spatial distributions alone are ambiguous, or when multiple ions share nominal m/z values (isobaric peaks).
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40. ▌
    Parameter Configuration Conditional Logic · holobiomicslab

    Use when setting up a LipidMatch analysis run and you need to select among three mutually-exclusive analysis modes (PFAS, Lipid, or Tween-positive detection). The trigger is application-level metadata or user specification that indicates which detection mode should execute.
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41. ▌
    Prefix Stripping And Case Standardization · holobiomicslab

    Use when valueMatcher.series_match has detected database-ID values (HMDB IDs, PubChem CIDs, KEGG compound identifiers) in raw metabolite metadata columns and you need to normalize them into a canonical form for storage in standardized database-ID columns (e.g., HMDB_ID, PubChem_ID, KEGG_ID).
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42. ▌
    Probability Distribution Inverse Sampling · holobiomicslab

    Use when when you need to reduce a large compound set generated by reaction rule application while (1) retaining compounds ranked highest by Tanimoto similarity to target structures, (2) avoiding hard thresholds that may lose borderline-useful compounds, and (3) maintaining stochastic diversity to.
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43. ▌
    Proteomics Normalization Method Selection · holobiomicslab

    Use when after loading peptide or protein-level quantification matrices with sample metadata into pmartR, and before downstream statistical analysis.
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44. ▌
    Quality Control Metabolite Identification · holobiomicslab

    Use when you have loaded a metabolomics dataset with a metabolitedata dataframe containing annotation columns (e.
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45. ▌
    Random Seed Reproducibility Documentation · holobiomicslab

    Use when you have generated multiple random partitions (e.g., 10-fold cross-validation splits) of a test set and need to deposit them in a shareable repository.
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46. ▌
    Reaction Network Generation And Expansion · holobiomicslab

    Use when you have (1) a set of starting compounds as SMILES strings, (2) a collection of reaction rules in SMARTS format (e.g., from MetaCyc), and (3) a need to systematically predict all reachable products within a bounded number of generations.
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47. ▌
    Regression Performance Metric Computation · holobiomicslab

    Use when after generating collision cross section predictions on a validation or test set using a trained graph neural network model, and you need to quantify prediction accuracy and compare against reported performance metrics in the literature or prior experimental runs.
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48. ▌
    Signal Noise Ratio Improvement Validation · holobiomicslab

    Use when after executing multidimensional smoothing, spike removal, or saturation repair on raw TOF-MS or IM-MS data (.d format from Agilent MassHunter) to confirm that signal quality has improved.
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49. ▌
    Spectral Clustering Membership Resolution · holobiomicslab

    Use when you have predicted BGC-spectrum link scores (e.g., IOKR or correlation values) for individual pairs and need to rank GCF-MF associations where each GCF contains multiple BGCs and each MF contains multiple spectra.
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50. ▌
    Spectral Similarity Prediction Evaluation · holobiomicslab

    Use when use when the workflow requires spectral-similarity-prediction-evaluation.
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51. ▌
    Spectrum Fingerprint Contrastive Learning · holobiomicslab

    Use when when you have paired tandem MS spectra and corresponding molecular structures (SMILES or fingerprints), and you want to build a retrieval system for metabolite structure annotation that can rank candidate structures for novel spectra by embedding similarity rather than spectral matching.
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52. ▌
    Structure Validation And Canonicalization · holobiomicslab

    Use when ingesting heterogeneous raw chemical structures from external datasets, publications, or user submissions into a retention time or molecular property prediction pipeline.
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53. ▌
    Summarized Experiment Object Manipulation · holobiomicslab

    Use when when working with metabolomics, proteomics, or other high-throughput replicate experiments stored in Bioconductor SummarizedExperiment format (rows = features/metabolites, columns = samples/replicates), and you need to inspect raw feature dimensions, assess data completeness, or apply.
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54. ▌
    Unit Test Design For Analytical Chemistry · holobiomicslab

    Use when when implementing or modifying metabolomics feature detection pipelines (e.g., adduct detection, m/z matching, feature labeling) where correctness directly impacts downstream analysis.
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55. ▌
    Version Specific Deployment Configuration · holobiomicslab

    Use when you have edited core R scripts (Modular.r, genEIC.r, MS1Spectragen.r, Stats.R) in the InnovativeOmics/Core-Match repository on GitHub and need to integrate those changes into a local LipidMatch-4.2 or FluoroMatch distribution.
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56. ▌
    Volcano Plot Construction From Omics Data · holobiomicslab

    Use when after statistical analysis (e.g., edgeR) has produced a results table containing lipid identities, fold-change values, and p-values for pairwise or multi-condition comparisons.
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57. ▌
    Volcano Plot Interpretation Cancer Benign · holobiomicslab

    Use when when you have performed two-group differential expression analysis on preprocessed LipidomicsExperiment objects (with logged and normalized Area measurements) and need to identify and visualize lipid molecules with statistically significant differential regulation between cancer and benign.
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58. ▌
    In Silico Biotransformation Prediction Workflow · holobiomicslab bundle

    Use when you have a parent structure (drug, natural product, xenobiotic) and untargeted LC-MS/MS data and want to find its biotransformation products — predict plausible metabolites in-silico by rule-based expansion (BioTransformer mammalian/gut-microbial/ environmental rules, EnviPath microbial degradation, or MINE/Pickaxe combinatorial reaction-network generation), filter candidates to those detectable in the experimental mass range, then screen and rank them against the observed LC-MS/MS features to annotate which predicted biotransformation products were actually seen.
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59. ▌
    Design Matrix Construction Experiments · holobiomicslab

    Use when when you have microarray or RNA-seq expression data paired with phenotype/sample metadata describing experimental conditions, treatments, or group assignments, and you need to fit a linear model to test for differential expression across those conditions.
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60. ▌
    Fold Change Interpretation And Ranking · holobiomicslab

    Use when after running DESeq() on a DESeqDataSet and extracting results with results(dds), you have a results table with log₂ fold changes, p-values, and adjusted p-values (padj). Use this skill when you need to (1) identify genes meeting a significance threshold (e.g., padj < 0.
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61. ▌
    Oarfish Quantification Format Handling · holobiomicslab

    Use when you have long-read RNA-seq samples quantified by oarfish (output as quant.gz files) and need to extract transcript-level or gene-level abundance, count, and length matrices for downstream statistical analysis.
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62. ▌
    Pathway Enrichment Statistical Ranking · holobiomicslab

    Use when you have a preranked gene list (e.g., genes sorted by log2 fold-change, t-statistic, or other continuous metric) and a collection of gene sets or biological pathways, and you need to determine which pathways are significantly over-represented among highly-ranked genes (positive enrichment).
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63. ▌
    Psi Calculation From Isoform Abundance · holobiomicslab

    Use when you have transcript-level abundance estimates (from Salmon, kallisto, or similar) and a set of defined alternative splicing events (in ioe or ioi format), and you need to compute event-level or isoform-level inclusion ratios (PSI) to quantify splicing patterns across multiple samples or.
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64. ▌
    Seed Representation Variant Comparison · holobiomicslab

    Use when you have observed mapping rate or quantification disagreement (e.g., >0.1% divergence in mapping rate or Pearson r < 0.
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65. ▌
    Statistical Hypothesis Testing Rna Seq · holobiomicslab

    Use when you have RNA-seq count matrices (from alignment, transcript quantification, or HTSeq-count files) and need to test for differential expression between two or more conditions while controlling for batch effects or other covariates.
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66. ▌
    Effective Mobility Calibration Single Marker · holobiomicslab

    Use when when you have CE-MS data with migration times that vary between runs due to electroosmotic flow drift, but you possess a reliable internal standard with a known effective mobility value.
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67. ▌
    Floating Point Numerical Accuracy Assessment · holobiomicslab

    Use when when implementing or validating a lossy numeric codec for mass-spectrometry data (e.g., MSNumpressCoder in OpenMS). Specifically: after implementing both encoder and decoder, before shipping to production, or when comparing alternative compression schemes.
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68. ▌
    Ms2 Spectral Library Matching Post Denoising · holobiomicslab

    Use when after frequency-based denoising of MS/MS spectra, when you need to validate that denoising improves metabolite identifications and quantify the trade-off between signal retention and annotation confidence. Specifically: (1) you have denoised MS/MS spectra from replicate measurements;
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69. ▌
    Empirical Compound Construction And Grouping · holobiomicslab

    Use when after generating a feature table from mzML data (via Asari) and before performing MS1 or MS2 annotation.
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70. ▌
    Gibbs Sampler Implementation And Convergence · holobiomicslab

    Use when your metabolomics dataset contains missing values below a known detection limit (left-censored MNAR data), and you need to recover these values while respecting the truncation constraint.
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71. ▌
    Low Resolution Mass Spectra Library Matching · holobiomicslab

    Use when when processing low-resolution GC-MS data in NetCDF format where you have already performed retention-index calibration and peak deconvolution, and you need to assign compound identities by comparing experimental mass spectra to a curated reference library such as PNNLMetV20191015.MSL.
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72. ▌
    Marker Feature Identification And Validation · holobiomicslab

    Use when when processing GC–MS or LC–MS data as m/z vs retention time chromatograms and you need to identify biomarker or chemical marker features without conventional peak picking, particularly when false positive detection rates from peak detection algorithms are problematic.
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73. ▌
    Mass Calibration Against Reference Standards · holobiomicslab

    Use when when processing raw FT-ICR transient data (e.g., ESI_NEG_SRFA.d) that requires assignment of molecular formulas to experimental m/z peaks. Calibration is necessary before SearchMolecularFormulas because uncalibrated mass error will cause false formula rejections or incorrect assignments.
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74. ▌
    Molecular Fragment Prediction Interpretation · holobiomicslab

    Use when you have an experimental tandem mass spectrum (collision energy annotated) and a known molecular formula or candidate structure list, and you need to rank or discriminate between isomeric or isobaric candidates by comparing predicted fragment-level spectral patterns.
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75. ▌
    Peak List Simulation With Controlled Effects · holobiomicslab

    Use when you need to validate batch correction or normalization algorithms, require ground-truth condition/batch effect annotations for method benchmarking, or want to systematically evaluate how different batch types (linear, random) and condition allocations affect feature recovery in metabolomic.
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76. ▌
    Quality Control Metric Distribution Analysis · holobiomicslab

    Use when after composite-map peak detection has produced an unfiltered peak list with SNR, peakshape (goodness_fitting), peak_height, and prominence values.
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77. ▌
    Two Dimensional Chromatography Data Handling · holobiomicslab

    Use when you have raw GCxGC-MS chromatogram data in NetCDF format from multiple samples (e.g., case and control groups) and need to prepare them for multivariate analysis such as multiway principal component analysis (MPCA).
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78. ▌
    Word Embedding Aggregation For Spectral Data · holobiomicslab

    Use when when you have pre-processed MS/MS spectra and a pre-trained Word2Vec model, and need to compute fast, scalable similarity scores for library matching or molecular networking that correlate better with structural similarity than cosine-based methods.
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79. ▌
    Word Embedding Based Spectrum Representation · holobiomicslab

    Use when when comparing large numbers of MS/MS spectra against spectral libraries or in molecular networking, particularly when molecules differ by multiple structural modifications and cosine-based scores produce excessive false positives.
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80. ▌
    Adduct And Fragment Neutral Mass Calculation · holobiomicslab

    Use when you have an LC-MS peak-intensity matrix with observed m/z values (from negative or positive mode ionization) and need to map each peak to candidate neutral masses in KEGG. Use this skill when you have a curated Cpd.
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81. ▌
    Background Distribution Threshold Derivation · holobiomicslab

    Use when when you have trained predictive models (e.g., neural networks) on paired microbiome-metabolome data and need to identify which metabolites are genuinely well-predicted above chance.
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82. ▌
    Bag Of Fragments Representation Construction · holobiomicslab

    Use when after noise filtering and polarity selection of MS/MS spectra (from .mgf, .mzML, or .msp files), when you need to prepare spectra for unsupervised discovery of recurring fragmentation patterns without prior compound identification.
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83. ▌
    Baseline Comparison Performance Benchmarking · holobiomicslab

    Use when when you have implemented a novel annotation algorithm or network and must verify it outperforms a simpler reference approach on held-out or independent spectral data. Specifically: (1) you have ground-truth annotations for a common test set;
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84. ▌
    Bayesian Model Update With Experimental Data · holobiomicslab

    Use when you have completed one or more LC-MS gradient runs, extracted separation efficiency metrics from the resulting MS1 and MS2 spectra, and need to incorporate those real experimental observations into your Gaussian process model to improve the next gradient proposal.
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85. ▌
    Class Imbalance Mitigation Via Normalization · holobiomicslab

    Use when preparing training batches for a neural network classifier on LCMS peak data where class counts are unequal (e.g., more high-quality peaks than low-quality peaks).
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86. ▌
    Class Specific Modification Rule Application · holobiomicslab

    Use when after identifying putative BGC-encoded precursor peptides from a genome assembly via MetaMiner's BGC identifier, when preparing a RiPP structure database for downstream spectral matching via Dereplicator.
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87. ▌
    Component Candidate Prioritization Filtering · holobiomicslab

    Use when after generateComponents has assigned candidate TP features to parent features and computed similarity metrics (spectrum similarity, fragment matches, neutral loss matches, retention time differences), use this skill to narrow the candidate pool to high-confidence parent–TP pairs that.
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88. ▌
    Compound Internal Standard Ratio Calculation · holobiomicslab

    Use when immediately after loading metabolomics measurements into a SummarizedExperiment object, before any batch correction or quality filtering.
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89. ▌
    Cross Dataset Feature Correspondence Mapping · holobiomicslab

    Use when you have two or more nontargeted LCMS feature tables from the same analytical method (same column, ionization mode, and acquisition parameters) and need to identify which features in one dataset correspond to features in another.
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90. ▌
    Deep Learning Model Training With Monitoring · holobiomicslab

    Use when you have a pre-trained deep learning encoder (e.g., TCN spectrum encoder trained on a large corpus) and want to adapt it to a new task (e.
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91. ▌
    Electronic Noise Generation Uniform Sampling · holobiomicslab

    Use when when you need to create synthetic noisy MS/MS spectra for benchmarking or validating denoising algorithms.
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92. ▌
    Exploitation Exploration Trade Off Balancing · holobiomicslab

    Use when you are in the Bayesian optimization loop after fitting a Gaussian Process model to observed LC gradient runs, and you need to propose the next gradient to evaluate.
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93. ▌
    Extracted Ion Chromatogram Eic Visualization · holobiomicslab

    Use when after running MS1 extraction and prescreening on mzML files with assigned adducts and tags, when you need to inspect detected compounds visually to verify peak shape, confirm retention time consistency across samples (e.
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94. ▌
    Feature Annotation With Chemical Descriptors · holobiomicslab

    Use when you have a feature list (m/z values, retention times, and optionally molecular formulas or neutral masses) from LC- or GC-HRMS data and need to rapidly identify features matching characteristic chemical signatures (e.
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95. ▌
    Feature Detection In Chromatographic Ms Data · holobiomicslab

    Use when you have vendor-independent centroided DDA mzML files from LC- or GC-HRMS and need to delineate chromatographic peaks across the mass-to-charge and retention-time dimensions before applying mass defect analysis, diagnostic fragment matching, or other prioritization rules.
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96. ▌
    Feature Table Generation From Chromatography · holobiomicslab

    Use when after retention-time correction and alignment of centroided mzML or mzXML LC-MS files across a sample cohort.
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97. ▌
    Feature Vector Similarity Search Preparation · holobiomicslab

    Use when you have millions of MS/MS spectra in mzML, mzXML, or MGF format and need to identify similar spectra for clustering, but exhaustive pairwise cosine-similarity computation would be prohibitively slow.
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98. ▌
    File Format Identification Mass Spectrometry · holobiomicslab

    Use when you have raw MS data files from one or more instrument vendors (Agilent, Bruker, Thermo Fisher, or mzML-formatted) and need to convert them to a vendor-agnostic HDF5-based storage format for downstream software development, machine learning, or cross-platform data access.
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99. ▌
    Formula Transformer Architecture Application · holobiomicslab

    Use when you have tandem mass spectra (MS/MS) with unknown precursor formulas and need to rank chemical formula candidates conditioned on observed fragment m/z values and precursor mass. Use this skill when fragmentation tree computation (e.
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100. ▌
     Fragmentation Pattern Extraction And Ranking · holobiomicslab

     Use when you have a collection of MS/MS spectra (≥2 spectra) and wish to identify fragmentation signatures common to subsets of those spectra.
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