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HolobiomicsLab

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1. ▌
   Neighbor Wise Constraint Propagation In Dtw · holobiomicslab

   Use when when XCMS or other DTW-based aligners have produced misaligned LC-MS feature groups across hundreds of samples or long acquisition periods (>1 week), particularly when individual m/z bins or compounds show inconsistent retention-time drift patterns across neighboring samples.
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   Neural Network Regularization Configuration · holobiomicslab

   Use when implementing or modifying a Siamese model (such as SiameseModel in ms2deepscore) if you need to make regularization hyperparameters user-configurable rather than hard-coded, or when training on spectral datasets where overfitting risk is high due to limited training examples or high model.
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   Neutral Loss Extraction From Precursor Mass · holobiomicslab

   Use when after loading and noise-filtering raw MS/MS spectra (in .mgf, .mzML, or .msp format) and before applying LDA for Mass2Motif inference.
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   Open Format Mass Spectrometry File Handling · holobiomicslab

   Use when you have raw mass spectrometry data in vendor-proprietary formats (e.g., .raw, .d, .ms) that you need to upload to MassIVE for public sharing or submit to GNPS for spectral library matching and chemical annotation.
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   Orbitrap Spectrum Extraction From Raw Files · holobiomicslab

   Use when you have a Thermo Fisher Orbitrap .raw file and need to retrieve a specific scan's spectral data (m/z and intensity arrays), validate instrument parameters (resolving power, AGC injection time), or assess signal-to-noise characteristics of fragment ions for a known precursor peptide (e.
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6. ▌
   Peak Annotation Refinement Structure Driven · holobiomicslab

   Use when you have two related compounds (a known reference and its structural analog with unknown modification site), baseline peak annotations from cosine alignment, and newly available structural information (SMILES or molecular structure) for the modified compound.
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7. ▌
   Pfas Characteristic Fragment Identification · holobiomicslab

   Use when you have centroided data-dependent acquisition (ddMS2) mzML spectra from LC- or GC-HRMS and need to prioritize putative PFAS features.
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8. ▌
   Principal Component Analysis Interpretation · holobiomicslab

   Use when when you have normalized MS1 ion intensity features from multiple samples and need to assess overall data quality, detect systematic batch effects or instrumental drift, or identify which samples deviate significantly from the cohort norm in an omics-agnostic manner (metabolomics.
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   Python Object Serialization Deserialization · holobiomicslab

   Use when when you have completed spectral data preprocessing in Jupyter notebooks and generated a specXplore session data object saved to disk, use this skill to restore that object into a live specXplore dashboard session instance for interactive LC-MS/MS spectral data exploration, without.
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10. ▌
    Annotation Accuracy And Coverage Metrics Computation · holobiomicslab

    Use when after executing an end-to-end structure annotation pipeline (such as BAM) on a validation dataset with known reference annotations.
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11. ▌
    Differential Expression Analysis Algorithm Selection · holobiomicslab

    Use when after preprocessing and normalizing count matrices from transcriptomics or other omics data, when you need to identify differentially expressed features between experimental groups and must choose between edgeR (for flexible formula/contrast designs), DESeq2 (for designs with explicit.
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12. ▌
    Euclidean Distance Computation For Spatial Alignment · holobiomicslab

    Use when when you have preprocessed ST and SM AnnData objects with spatial coordinates and need to establish one-to-one spot correspondence between the two modalities prior to joint analysis.
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13. ▌
    Feature Table Construction For Multivariate Analysis · holobiomicslab

    Use when when you have high-resolution mass-spectrometry (HRMS) breath data in mzML or mzXML format from multiple subjects or conditions and need to prepare a sample-by-feature intensity matrix for downstream statistical comparison, biomarker discovery, or classification tasks.
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14. ▌
    Mass Spectrometry Mass Accuracy And Precision Tuning · holobiomicslab

    Use when when extracting and validating chromatographic peaks for target molecules from centroided mzML files, particularly when working with multiple isotopologues and adducts where mass tolerance directly affects whether predicted isotopologues are correctly grouped and detected as part of the.
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15. ▌
    Mass Spectrometry Peak Identification And Extraction · holobiomicslab

    Use when when you have raw mass spectrometry data from direct-infusion (DI-MS) or ambient surface analysis probe (ASAP-MS) instruments and need to identify which m/z peaks are biologically or chemically informative for sample classification, rather than processing the entire spectrum including.
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16. ▌
    Missing Value Imputation And Completeness Assessment · holobiomicslab

    Use when after loading raw omics expression data (protein, peptide, metabolite abundances) with inherent missing values.
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17. ▌
    Mobility Dimension Interpolation For Peak Resolution · holobiomicslab

    Use when working with raw multiplexed IM-MS data (UIMF or Agilent MassHunter .
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18. ▌
    Sequential And Random Access File Interface Patterns · holobiomicslab

    Use when when you need to support custom data storage backends (e.g., SQLite, HDF5, or proprietary databases) for mzML-equivalent spectra, but the pymzML parser expects only standard file formats (mzML, mzML.gz, indexed gzip).
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19. ▌
    Software Dependency Management And Environment Setup · holobiomicslab

    Use when you are preparing to run the MetaboDirect pipeline for the first time on a new machine, or you need to reproduce a published benchmark or analysis on a different OS (Windows, Linux, MacOS) and want to guarantee that all six pipeline steps (data pre-processing, diagnostics, exploration.
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20. ▌
    Transcript Abundance Normalization Across Cell Lines · holobiomicslab

    Use when you have computed raw RAS values for multiple cell lines or samples by resolving Gene-Protein-Reaction (GPR) logical rules against RNA-seq FPKM abundances, and you need to make reaction activity scores comparable across cell lines by removing sample-level abundance differences so that.
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21. ▌
    Deseq2 Object Initialization From Transcript Data · holobiomicslab

    Use when you have transcript-level abundance, count, and length estimates (from salmon, Sailfish, or kallisto via tximport) and want to perform gene-level differential expression analysis in DESeq2 using the 'original counts and offset' method, which accounts for changes in effective gene length.
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22. ▌
    Independent Filtering Multiple Testing Correction · holobiomicslab

    Use when analyzing RNA-seq count data from a DESeq2 workflow where you have fitted negative binomial generalized linear models and need to extract final results. Use it specifically when you want to identify genes with adjusted p-value below a predetermined significance threshold (e.g., α=0.
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23. ▌
    Ms2 Spectrum Collection From Data Dependent Acquisition · holobiomicslab

    Use when you have DDA LC-MS/MS raw data (mzML format) with detected chromatographic peaks at a target m/z value and retention time window, and you need to build a high-confidence MS2 consensus spectrum for that peak to match against reference spectra (e.g., Metlin, GNPS).
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24. ▌
    Sample Batch Balancing Across Classification Dimensions · holobiomicslab

    Use when designing multi-batch LC/GC-MS experiments where samples belong to multiple groups or conditions and you need to ensure that each injection plate receives a balanced representation of all groups.
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25. ▌
    Biotransformation Candidate Integration With Networking · holobiomicslab

    Use when you have output from a biotransformation rules module (candidate transformed structures linked to anchor molecules) and untargeted MS/MS spectral data, and you want to identify molecular families and annotate features with predicted structures by leveraging spectral similarity and network.
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26. ▌
    Biotransformation Prediction Across Microbiota Contexts · holobiomicslab

    Use when you have one or more small-molecule chemical structures (as SMILES, MOL, or SDF) and need to systematically explore their fate across mammalian biotransformation, human gut microbial degradation, or environmental (soil/aquatic) microbial degradation.
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27. ▌
    Chromatographic Peak Detection Retention Time Alignment · holobiomicslab

    Use when you have multiple high-resolution mzML files from LC-MS/MS experiments (e.
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28. ▌
    Empirical Compound Construction From Feature Clustering · holobiomicslab

    Use when after feature detection and quality control have produced a feature table in TSV format from Asari or equivalent preprocessing.
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29. ▌
    Gene Cluster Family Formation And Similarity Clustering · holobiomicslab

    Use when you have antiSMASH v5.0.0 BGC predictions from a set of microbial genomes and you need to integrate those predictions with GNPS metabolomic data (MS2 spectra and molecular families).
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30. ▌
    Hyperparameter Tuning Learning Rate Optimizer Selection · holobiomicslab

    Use when when training a fresh NeatMS CNN model from scratch on LCMS peak classification and you need to determine which optimizer (Adam vs. SGD) and learning rate will produce an AUC ROC > 0.9 without overfitting.
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31. ▌
    Ion Tree Structure Optimization And Trunk Establishment · holobiomicslab

    Use when after you have partitioned a feature network into connected subnetworks (each containing ion features linked by isotope or adduct mass differences).
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32. ▌
    Peak Evaluation Metrics Cselectivity Snr Gaussian Fit · holobiomicslab

    Use when after scipy.signal.find_peaks has identified candidate peaks on a composite mass track segment, evaluate each peak to decide whether to retain it in the final feature table.
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33. ▌
    False Positive Negative Detection In Pattern Matching · holobiomicslab

    Use when when reconstructing or validating the ColumnFinder component in mwtab, you need to assess whether the combined NameMatcher.dict_match and ValueMatcher.series_match operations are correctly populating database-ID columns. Apply this skill after defining NameMatcher patterns (e.
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34. ▌
    Knn Nearest Neighbor Matching For Spot Correspondence · holobiomicslab

    Use when when integrating two spatial omics modalities (ST and SM) measured on the same tissue sample but at different spatial resolutions or spot coordinates.
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35. ▌
    Raw Spectral Data Input Handling Without Peak Picking · holobiomicslab

    Use when when you have raw mass spectrometry imaging data (full m/z profiles with intensity arrays) and want to classify spatial regions (e.
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36. ▌
    Similarity Network Construction From Domain Adjacency · holobiomicslab

    Use when you have a collection of tokenized BGCs (each gene represented as a combination of Pfam domains and subPfams) and need to identify and remove redundant or highly similar clusters before downstream analysis such as sub-cluster detection or natural product annotation.
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37. ▌
    Spearman Correlation Background Distribution Analysis · holobiomicslab

    Use when after cross-validated neural network or regression models have generated predicted metabolite abundances and you need to distinguish genuinely predictable metabolites from those with spuriously high correlations.
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38. ▌
    Spectrum Random Access Retrieval Via Bracket Notation · holobiomicslab

    Use when when you need to retrieve specific spectra from mzML files by custom identifier (integer or string) rather than sequential iteration, especially when the mzML file uses non-standard index formatting that requires a regex pattern to parse spectrum IDs and file offsets.
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39. ▌
    Volcano Plot Construction From Fold Change And Pvalue · holobiomicslab

    Use when you have omu_summary output containing log2FoldChange and adjusted p-values (e.g., BH-corrected) for a pairwise contrast (e.g., Strep vs Mock treatment), and you want to visualize metabolite significance and magnitude simultaneously while stratifying points by hierarchical class (e.
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40. ▌
    Differential Expression Empirical Bayes Moderation · holobiomicslab

    Use when after fitting a linear model with lmFit on voom-transformed or log2-normalized RNA-seq or microarray expression matrices, apply eBayes moderation to moderate gene-wise variance estimates before extracting top differentially expressed genes.
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41. ▌
    Nested Data Structure Construction For Chemical Metadata · holobiomicslab

    Use when you have loaded raw Agilent Unknowns Analysis CSV output with required columns (Component.RT, Base.Peak.MZ, Component.Area, Compound.Name, Match.Factor, File.
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42. ▌
    Retention Time And Mass Sorting Of Chromatographic Peaks · holobiomicslab

    Use when you have raw GC-MS output from Agilent Unknowns Analysis (a .CSV with columns Component.RT, Base.Peak.MZ, Component.Area, Compound.Name, Match.Factor, File.
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43. ▌
    Mass Spectrometry Domain Specific Language Comprehension · holobiomicslab

    Use when you need to translate user-facing mass spectrometry query intent (e.g., 'find all MS2 spectra with a precursor ion at m/z 572.
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44. ▌
    Retention Time Projection Across Chromatographic Methods · holobiomicslab

    Use when when you have retention times measured on one chromatographic method and need to predict or map them to another method with minimal or no overlap in measured molecules.
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45. ▌
    Tree Structure Optimization For Metabolite Deconvolution · holobiomicslab

    Use when you have a connected subnetwork of LC-MS features that matched isotope or adduct patterns, and you need to establish a canonical tree representation with a single neutral mass assignment.
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46. ▌
    Editable Development Mode Installation And Verification · holobiomicslab

    Use when when you are developing or contributing to a Python package (like cooltools) and need to test changes to utility functions, library integrations, or API implementations without reinstalling the package after each modification. Apply this when you must verify that a new function (e.
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47. ▌
    Python Collection Subclassing And Operator Overloading · holobiomicslab

    Use when you need to represent structured scientific data (e.g., molecular formulas with element–count mappings) as a dict-like object, but standard dict does not preserve type across arithmetic operations, does not support domain-specific validation, or lacks convenient string representations.
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48. ▌
    Random Forest Classification For Metabolite Prediction · holobiomicslab

    Use when you have a metabolomics count table (rows=metabolites, columns=samples) with associated metadata containing a categorical grouping factor (e.
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49. ▌
    Exact Mass Lookup And Aggregation From Chemical Databases · holobiomicslab

    Use when after loading raw Agilent Unknowns Analysis CSV output and when you need to convert tentative compound identifications (matched only by GC-MS library cosine similarity or Match.Factor score) into searchable, curated chemical records with exact masses and multi-source confirmation.
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50. ▌
    Mass Spectrometry Compound Annotation Database Generation · holobiomicslab

    Use when after generating transformation products using generateTPs() with structural information (SMILES), when you need to screen for predicted TP compounds in environmental MS/MS data via MetFrag's in-silico fragmentation annotation.
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51. ▌
    Search Result Integration With Raw Mass Spectrometry Data · holobiomicslab

    Use when when you have completed a DIA-MS proteomics search (e.
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52. ▌
    Transcription Factor Motif Genomic Coordinate Annotation · holobiomicslab

    Use when you have ATAC-seq BAM files and want to detect transcription factor footprints—characteristic depletion patterns of Tn5 insertions around protein-bound motif sites.
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53. ▌
    Multi Regime Model Performance Comparison Visualization · holobiomicslab

    Use when you have paired microbiome-metabolome (or similar multivariate) datasets and want to quantify whether training on a superset of features (e.g., both annotated and unannotated metabolites) improves prediction accuracy for a well-defined target subset (e.
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54. ▌
    Benchmark Dataset Generation From Reference Metabolites · holobiomicslab

    Use when you have a set of centroided mzML files from LC-HRMS analysis, a list of target metabolites with known molecular formula and retention time windows, and you need to create a reference dataset to benchmark the reliability of NPP tools (XCMS, MZmine, MS-DIAL, etc.) on the same files.
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55. ▌
    Chemical Transformation Prediction Via Rule Application · holobiomicslab

    Use when you have: (1) a set of starting compounds in SMILES format; (2) a library of reaction rules (SMARTS-encoded) from MetaCyc or custom sources; (3) a need to discover predicted reaction products and novel compounds across multiple reaction generations; and (4) constraints (e.
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56. ▌
    Compression And Serialization Of Large Tabular Datasets · holobiomicslab

    Use when when consolidating multiple cleaned and validated data sources (organism, structure, reference subgraphs) into a single denormalized table containing hundreds of thousands of rows and many columns, and the result must be stored, archived, or transmitted with minimal storage overhead while.
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57. ▌
    Dictionary And Metadata Extraction From Relational Data · holobiomicslab

    Use when after performing left-join operations on cleaned organism, structure, and reference subgraphs but before final table denormalization. Trigger conditions: (1) you have a unified table with repeated entity_id columns (e.
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58. ▌
    Environment Variable Configuration For Distributed Jobs · holobiomicslab

    Use when you have a multi-fold cross-validation training workflow that must run as parallel SLURM array jobs, each with its own GPU, and you need to ensure that all jobs can locate the same training data, code repository, container image, and results directory without hardcoded absolute paths or.
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59. ▌
    Heterogeneous Data Format Integration And Harmonization · holobiomicslab

    Use when your input consists of multiple external databases with different file formats, column names, identifier schemes, and taxonomic/chemical vocabularies (e.g., 31 open natural product databases with heterogeneous structure and organism metadata).
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60. ▌
    Metabolite Compound List Mapping With Adduct Assignment · holobiomicslab

    Use when when you have mzML files from targeted or untargeted metabolomics experiments run in multiple ionization modes (e.g., KO_NEG, KO_POS, STD_NEG, STD_POS, WT_NEG, WT_POS) and a CSV list of reference compounds (e.
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61. ▌
    Open Source Mass Spectrometry File Format Interoperability · holobiomicslab

    Use when when working with Bruker .d/.baf mass spectrometry imaging data and needing to feed it into MSIGen or other open-source MSI processing pipelines.
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62. ▌
    Image Processing On Two Dimensional Mass Spectrometry Maps · holobiomicslab

    Use when when you have raw GC–MS data in two-dimensional m/z × retention time format (NetCDF or proprietary binary) and need to identify marker features across aroma or breath samples at parts-per-billion concentration levels, particularly when conventional peak picking introduces false positives.
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63. ▌
    Computational Efficiency Single Pass Vs Repeated Detection · holobiomicslab

    Use when when processing aligned LC-MS data across multiple samples where the computational bottleneck is repeated peak-detection algorithm calls (one per sample per m/z value). Typical scenario: >10 samples with >1000 m/z values each, where N individual find_peaks invocations dominate runtime.
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64. ▌
    Kernel Regression Learning From Spectral Fingerprint Pairs · holobiomicslab

    Use when when you have a training set of MS2 spectra with known chemical structures (e.
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65. ▌
    Mass Spectrometry Feature Extraction From Cardinal Objects · holobiomicslab

    Use when you have a Cardinal MSImagingExperiment object (e.g., from imzML or Analyze 7.5 files) and need to retrieve the complete set of m/z values and their intensities for annotation against metabolite databases (HMDB, Lipidmaps) or for statistical analysis.
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66. ▌
    Mass Spectrum Tokenization And Bag Of Fragments Generation · holobiomicslab

    Use when after filtering and cleaning MS/MS spectra (positive/negative ion mode) but before applying Latent Dirichlet Allocation for Mass2Motif discovery.
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67. ▌
    Metabolite Cluster Identification From Correlated Features · holobiomicslab

    Use when after preprocessing, imputation, and batch correction of LC-MS peak tables when you need to group redundant or related feature measurements (e.g., [M+H]+ and [M+Na]+ adducts, or isotope peaks) into metabolite-level clusters before statistical testing or identification.
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68. ▌
    Software Performance Characterization And Scaling Analysis · holobiomicslab

    Use when when a tool claims to be 'scalable' or 'performance-conscious' but lacks published performance benchmarks, or when you need to confirm that runtime and memory scale linearly (or predictably) with sample count before deploying the tool on large LC-MS datasets (e.g., >100 samples).
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69. ▌
    Chemical Name Normalization And Publish Database Integration · holobiomicslab

    Use when after spreadOut() has converted raw CSV peak data into a structured list, when you have one or more Compound.Name entries from GC-MS that may be ambiguous, non-canonical, or missing standardized properties (exact mass, published retention times, reactive groups, database presence).
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70. ▌
    Backend Compatibility Verification Visualization Library · holobiomicslab

    Use when you have mass spectrometry data loaded into a pandas DataFrame with m/z, retention time, and intensity columns, and need to confirm that pyOpenMS-Viz can produce visualizations (spectra, chromatograms, or peak maps) using a specific plotting backend (matplotlib, Bokeh, or Plotly) on real.
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71. ▌
    Command Line Pipeline Execution And Runtime Benchmarking · holobiomicslab

    Use when when you have peak-abundance .csv files and assigned molecular formula data from FT-ICR MS preprocessing, and you need to verify that a published pipeline's runtime claims (e.g., '<1 min for 40 samples', '~2 min for 120 samples') hold true on your own datasets or reference datasets.
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72. ▌
    Data Transformation Centered Log Ratio With Pseudocounts · holobiomicslab

    Use when you have raw microbiome (e.g., 16S rRNA or metagenomic) or metabolomic count tables (samples × features) and plan to train predictive models (e.g., MiMeNet, linear regression) that require normally distributed or near-normally distributed inputs.
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73. ▌
    Feature Attribution Score Interpretation Neural Networks · holobiomicslab

    Use when after training a neural network model (e.
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74. ▌
    Chemical Identifier Mapping Smiles To Pubchem Chemspider · holobiomicslab

    Use when you have a chemical structure in SMILES format and need to find matching compound records and standardized identifiers (e.
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75. ▌
    Fingerprint Computation Maccs Extended Connectivity Path · holobiomicslab

    Use when when you have a set of chemical compounds (as SMILES, mol, SDF, mol2, or hin files) and need to convert their structural information into numerical fingerprint vectors for training machine learning regressors on experimental retention time data or other molecular property prediction tasks.
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76. ▌
    Natural Product Database Querying Norine Coconut Npatsas · holobiomicslab

    Use when you have a chemical structure (as SMILES string or identifier) and need to discover matching records across specialized natural product databases.
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77. ▌
    Principal Component Analysis Dimensionality Reduction · holobiomicslab

    Use when your input is a normalized, centered gene expression matrix with many genes (e.g., 12,000+) and you need to validate whether reducing to a smaller number of principal components (e.g., 10) preserves pathway enrichment statistics (pathway scores and p-values from geseca or fgsea).
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78. ▌
    Pytest Test Suite Execution And Result Interpretation · holobiomicslab

    Use when after forking and cloning a repository (e.g., scverse/scanpy) to verify that the development environment is correctly configured, or after implementing a feature or bugfix to ensure no regressions were introduced.
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79. ▌
    Single Cell Rna Seq Quality Control And Normalization · holobiomicslab

    Use when when you have a raw or minimally processed scRNA-seq dataset (e.g., a Seurat object loaded from GEO) and need to prepare it for pathway enrichment or coregulation analysis.
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80. ▌
    Microbiome Metabolome Data Preprocessing Clr Transformation · holobiomicslab

    Use when you have paired microbiome and metabolomic abundance tables (samples × features) with relative abundance or raw count values, and you are preparing data for downstream regression or neural network modeling of microbe-metabolite relationships.
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81. ▌
    Tandem Mass Spectrometry Ion Fragmentation Pattern Analysis · holobiomicslab

    Use when after MS1 feature extraction and prescreening quality control have completed on mzML files, and you need to inspect the MS2 fragmentation patterns of candidate compounds to verify their identity or assess whether extracted features are genuine metabolites rather than noise or artifacts.
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82. ▌
    Model Generalization Assessment Across Molecular Size Regime · holobiomicslab

    Use when a deep learning model for molecular structure prediction (e.g., NMR2Struct) has been trained and evaluated on a limited molecular size range (e.
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83. ▌
    Distance Metric Calculation Bray Curtis Euclidean Jaccard · holobiomicslab

    Use when you have normalized peak intensity data (or absence/presence matrices) from metabolomics experiments with multiple samples and need to quantify compositional differences between them prior to multivariate analysis. This step is essential when testing whether categorical factors (e.
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84. ▌
    Microbe Metabolite Feature Selection By Annotation Status · holobiomicslab

    Use when when you have paired microbiome-metabolome datasets where only a subset of metabolites carry curated biochemical annotations (e.
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85. ▌
    Spatial Correlation Analysis In Imaging Mass Spectrometry · holobiomicslab

    Use when after annotating ion pairs with known adducts (using adductMatch or diffGetPeaks), use this skill to filter suspected adduct pairs by testing if their pixel-level intensity profiles are significantly correlated.
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86. ▌
    Functional Annotation Table Generation And Interpretation · holobiomicslab

    Use when after running CPAT, signalP, Pfam, and fimo tools on differentially expressed isoform or exon FASTA sequences, and you need to consolidate their individual outputs into a single indexed table to compare functional properties across isoforms, identify isoforms with specific domain.
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87. ▌
    Machine Learning Model Evaluation With Confusion Matrices · holobiomicslab

    Use when after fitting a random forest model to metabolomics count data with a categorical response variable (e.g., wild-type vs. knockout treatment groups), you need to quantify how accurately the model predicts group membership and identify which metabolites drive the classification.
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88. ▌
    Metabolomicshub Cross Repository Dataset Search · holobiomicslab

    Use when assembling a reanalysis or meta-analysis cohort from public metabolomics studies held in MetaboLights, Metabolomics Workbench and GNPS/MassIVE, and the instrument, polarity or open-format filters have to be honest about which repositories they silently exclude.
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89. ▌
    Chromatographic Peak Extraction And Retention Time Filtering · holobiomicslab

    Use when when you have LC-MS/MS raw data (mzML or netCDF format) and need to isolate a specific compound's signal based on its known or suspected m/z value and retention time range.
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90. ▌
    Baseline And Noise Level Estimation From Quartile Statistics · holobiomicslab

    Use when before peak detection on a composite or individual mass track when you need to filter out low-intensity noise and baseline drift without removing true signal.
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91. ▌
    Cheminformatics Substructure Matching And Reaction Templates · holobiomicslab

    Use when you have a chemical substrate and need to predict its biotransformation products using rule-based metabolism prediction. This applies when: (1) you possess a library of biotransformation rules extracted from a curated database (e.
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92. ▌
    Interactive Mass Spectrometry Plot Generation And Annotation · holobiomicslab

    Use when when you have extracted ion chromatograms (XICs), ion mobilograms (IMs), or mass spectra from diaPASEF or other DIA workflows and need to visualize them interactively to inspect peak boundaries, compare MS1 vs MS2 traces, validate feature identifications, or communicate results.
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93. ▌
    Ion Chromatogram Mobilogram Visualization And Interpretation · holobiomicslab

    Use when after loading raw diaPASEF or DIA mass spectrometry data (mzML format) and search results (DIA-NN, OpenSwath, or equivalent) to visually inspect extracted ion chromatograms and mobilograms for selected peptide precursors.
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94. ▌
    Training Data Inventory Construction From Spectral Libraries · holobiomicslab

    Use when you are preparing to apply Probability Product Kernel–based scoring to MS2 spectra for genomic–metabolomic linking, and you need to establish a reference set of ion peaks that represent robust spectral diversity without overfitting to any single study.
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95. ▌
    Sphinx Documentation Generation And API Reference Inclusion · holobiomicslab

    Use when after implementing or modifying utility functions in a library subpackage (e.g., cooltools.
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96. ▌
    Metabolite Class Visualization By Statistical Significance · holobiomicslab

    Use when after running omu_summary statistical comparison on count data with assigned hierarchical class annotations (KEGG, KO_Number, Prokaryote, or Eukaryote), use this skill to visualize log2FoldChange and adjusted p-values for metabolites grouped by class.
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97. ▌
    Tensor Shape Validation And Numerical Correctness Checking · holobiomicslab

    Use when after implementing a shared-weight ResNet18 encoder module but before integrating it into the DeepION data augmentation and projection workflow.
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98. ▌
    Alternative Splicing Event Comparison Across Conditions · holobiomicslab

    Use when you have PSI (percent-spliced-in) matrices calculated independently for two or more biological conditions, each with two or more replicate samples, and you want to identify which alternative splicing events show statistically significant changes in inclusion levels between conditions.
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    Metabolite Detection And Acquisition Performance Benchmarkin · holobiomicslab

    Use when you have simulated or experimental mzML data from two or more fragmentation controllers (e.
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     Interactive Visualization Interpretation Methylation Results · holobiomicslab

     Use when after running ChAMP detection functions (champ.
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