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HolobiomicsLab

@holobiomicslab source repo

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1. ▌
   Fragment Length Prediction And Extension · holobiomicslab

   Use when after filtering duplicate reads from ChIP-Seq data but before generating pileup coverage tracks.
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2. ▌
   Hi C Matrix Normalization Pipeline Setup · holobiomicslab

   Use when before running HiC-Pro's normalization stage on aligned Hi-C BAM files. Specifically, when you have SAM/BAM-formatted aligned Hi-C reads that need bias correction and matrix balancing to produce normalized contact maps suitable for downstream chromatin structure analysis.
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3. ▌
   Illumina Methylation Array Preprocessing · holobiomicslab

   Use when your input is raw .idat files or a beta-valued matrix from Illumina HumanMethylation450 or EPIC arrays, and you need to remove unreliable probes (those with detection p-value > 0.01 or insufficient bead counts) before performing differential methylation or other downstream analyses.
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4. ▌
   Kilobase Resolution Contact Map Analysis · holobiomicslab

   Use when you have paired-end Hi-C FASTQ files from a public repository (NCBI SRA, GEO, or ENCODE-deposited) and need to produce standardized .hic binary contact maps that conform to ENCODE reference formats and integrity standards for downstream 3D genome analysis.
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5. ▌
   Nucleotide Footprint Pattern Recognition · holobiomicslab

   Use when you have aligned ATAC-seq BAM files and want to discriminate between transcription factor binding sites that are actually occupied by protein versus sites with matching sequence motifs that are unbound.
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6. ▌
   Statistical Overlap Proportion Reporting · holobiomicslab

   Use when after identifying differentially methylated bases (q-value < 0.01, methylation difference > 25%) using calculateDiffMeth(), use this skill to determine what fraction of those bases overlap with specific gene features (promoters, exons, introns) and CpG contexts (islands vs. shores).
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7. ▌
   Binary Stream Seeking And Decompression · holobiomicslab

   Use when you have a large gzip-compressed file (e.g., mzML.gz) with an embedded index structure in the gzip header comment field, and you need to retrieve specific blocks (e.g., mass spectra by scan number, chapters by ID) without decompressing the entire file.
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8. ▌
   Build Artifact Retrieval And Validation · holobiomicslab

   Use when when you need to verify that a GitHub Actions workflow (such as dev_build_release.
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9. ▌
   Domain Specific Language Grammar Design · holobiomicslab

   Use when when you need to enable non-programmers or domain experts to formulate complex, unambiguous queries over specialized data (e.
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10. ▌
    Tanaka Physicochemical Descriptor Handling · holobiomicslab

    Use when you have raw HPLC column metadata arrays containing Tanaka parameter blocks that will be fed into a featurizer for machine learning on retention times. Tanaka parameters are present but may contain string artifacts ('2.7 spp', '2.
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11. ▌
    Tandem Ms Feature Table Import And Parsing · holobiomicslab

    Use when when you have raw feature tables exported from a tandem LC-MS/MS preprocessing tool (e.g., Progenesis QI, MS-DIAL, Bruker Metaboscape) and need to combine them with sample metadata (group assignments, replicate structure) before applying feature filtering or quality control workflows.
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12. ▌
    Tanimoto Similarity Scoring Implementation · holobiomicslab

    Use when when you have paired mass spectrometry spectra (e.g., from GNPS, MoNA, MassBank, or MSnLib) and need to predict continuous structural similarity scores (0–1 range) between them, especially when traditional spectral-distance metrics (e.
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13. ▌
    Topic Modeling Hyperparameter Optimization · holobiomicslab

    Use when when preparing to apply LDA to a new MS/MS spectral dataset in bag-of-fragments format, before running the full modeling pipeline.
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14. ▌
    Training Validation Dataset Stratification · holobiomicslab

    Use when when you have a complete labelled MS/MS spectral dataset annotated as 'relevant' (compounds of interest from reference standards) or 'other' (reference standards or non-target compounds from repositories), and you need to train and evaluate an AnnoMe binary classifier without data leakage.
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15. ▌
    Type Safety And Length Matching Validation · holobiomicslab

    Use when implementing data replacement methods (such as `[<-`, `$<-`, `mz<-`, `intensity<-`, `peaksData<-`) in a writable MsBackend subclass.
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16. ▌
    Vendor Proprietary Format Interoperability · holobiomicslab

    Use when you have mass spectrometry raw data in a proprietary vendor format (Thermo .raw, Agilent .d with or without ion mobility, Bruker ion mobility .d, or mzML) and need to enable reproducible, language-agnostic access to multidimensional spectra (e.
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17. ▌
    Accurate Mass Metabolite Search Against Hmdb · holobiomicslab

    Use when after MS1 feature detection and spectra merging in an untargeted or semi-targeted metabolomics workflow, when you have a list of observed accurate m/z values from high-resolution mass spectrometry (e.
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18. ▌
    Bi Encoder Cross Encoder Architecture Design · holobiomicslab

    Use when you have paired spectrum-compound reference data and need to simultaneously retrieve candidate compounds rapidly (bi-encoder) while also refining relevance scores through joint context modeling (cross-encoder).
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19. ▌
    Blood Sample Processing Parameter Extraction · holobiomicslab

    Use when when you have peripheral blood sample cohorts (plasma/serum) with multiple timestamps (e.
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20. ▌
    Metabolite Concentration To Spectrum Mapping · holobiomicslab

    Use when when you have a list of known metabolite concentrations and their corresponding J-coupling constants (spin systems) and need to generate realistic 1D 1H NMR spectra or 2D correlation spectra (COSY, HSQC, HMQC) for simulation, validation, or educational purposes, without access to actual.
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21. ▌
    Molecular Transformation Frequency Filtering · holobiomicslab

    Use when after clustering pairwise mass differences into transformation groups from FT-ICR MS formula-assigned datasets, apply this skill when you have a catalog of transformation pairs (e.
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22. ▌
    Nmr Network Visualization And Interpretation · holobiomicslab

    Use when you have 2D NMR spectra (heteronuclear: HSQC, HMBC;
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23. ▌
    Transformer Model Inference On Spectral Data · holobiomicslab

    Use when you have a 1D 1H NMR spectrum represented as chemical shift vs. intensity values, a corresponding list of detected peak positions, and you need to assign metabolite identities to those peaks.
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24. ▌
    Bootstrap Confidence Interval Computation · holobiomicslab

    Use when when you have computed z-score deviations for genomic annotations (e.g., motifs) across multiple cells or samples and need to quantify uncertainty in their variability rankings before performing differential or comparative analyses.
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25. ▌
    Checksum Verification For Reproducibility · holobiomicslab

    Use when you have executed a complex multi-step processing pipeline (e.g., ENCODE Hi-C uniform processing pipeline) and need to confirm that the generated output files match a known reference baseline.
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26. ▌
    Conda Environment Creation And Management · holobiomicslab

    Use when you need to deploy a complex multi-language pipeline (e.g., HiC-Pro) that requires Python >3.7 libraries (pysam, bx-python, numpy, scipy), R packages (ggplot2, RColorBrewer), and compiled tool binaries (bowtie2, samtools >=1.
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27. ▌
    Differential Accessibility Interpretation · holobiomicslab

    Use when after identifying differentially accessible peaks (via tl.
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28. ▌
    Leiden Clustering Resolution Optimization · holobiomicslab

    Use when you have performed spectral dimension reduction on single-cell omics count matrices and wish to partition cells into discrete populations.
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29. ▌
    Makefile Based Build System Configuration · holobiomicslab

    Use when when deploying a complex bioinformatics pipeline (e.g., HiC-Pro) that depends on multiple external tools with version constraints (samtools ≥1.9, bowtie2, R packages, Python libraries) and you need to verify their availability and configure their paths before running the analysis.
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30. ▌
    Multiome Data Ingestion Paired Modalities · holobiomicslab

    Use when you have independently generated or received both scATAC-seq peak count matrices and scRNA-seq gene expression matrices from the same set of cells (multiome experiment), and you need to perform joint analysis such as co-clustering, trajectory inference, or regulatory inference that.
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31. ▌
    Rna Seq Expression Alignment Across Cells · holobiomicslab

    Use when you have paired scATAC-seq and scRNA-seq data from the same cells (multiome experiment) and want to perform integrated analysis that leverages both chromatin accessibility and gene expression signals.
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32. ▌
    Activity Score Computation And Reporting · holobiomicslab

    Use when you have preprocessed metabolite intensity data (log2-transformed, zero-mean unit-variance standardized) mapped to compound annotations, and you need to derive activity scores for a set of metabolite groups (pathways, Molecular Families, Mass2Motifs, or custom metabolite sets) to rank them.
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33. ▌
    Comparative Enrichment Method Evaluation · holobiomicslab

    Use when you are selecting a pathway enrichment method for metabolomics peak data and need to assess which method will remain stable when your data contains noise, dropout, or missing identifications.
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34. ▌
    Computational Execution Table Generation · holobiomicslab

    Use when when you have a set of gallery or benchmark scripts (e.
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35. ▌
    Consensus Clustering Algorithm Selection · holobiomicslab

    Use when when you have computed hierarchical clustering dendrograms on your feature matrix (microbes or metabolites) using Euclidean distance and complete linkage, and need to determine how many clusters to cut the dendrogram into.
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36. ▌
    Contrastive Learning Loss Implementation · holobiomicslab

    Use when when you have paired augmented ion images processed through ResNet18 encoders producing 512-dimensional representation vectors, and you need to learn meaningful low-dimensional representations without labeled data by enforcing that augmentations of the same image remain similar while.
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37. ▌
    Custom File Wrapper Class Implementation · holobiomicslab

    Use when when you have mass spectrometry data stored in a database or non-mzML file format and need to integrate it into the pymzML pipeline without converting to mzML. Use this skill when FileInterface does not natively recognize your file extension and you want to avoid data conversion overhead.
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38. ▌
    Deep Neural Network Latent Space Mapping · holobiomicslab

    Use when you have a pre-trained DNN model for retention time prediction and need to adapt it to a new chromatographic method or instrument where you have only 10–20 calibration molecules with known retention times;
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39. ▌
    Documentation And Metadata Modernization · holobiomicslab

    Use when a mature scientific package (e.g., Mummichog 3) is being migrated to a new GitHub organization that enforces standardized project structure, and the current setup.py, pyproject.toml, requirements.txt, .
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40. ▌
    Error Metric Comparison And Benchmarking · holobiomicslab

    Use when you have predicted retention times from one or more machine learning models (DNN, Gaussian Process, or ensemble) applied to small-molecule chromatography data, along with corresponding experimental ground-truth retention times, and need to quantify prediction accuracy and rank competing.
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41. ▌
    File Handler Instantiation And Lifecycle · holobiomicslab

    Use when you need to open an mzML file in pymzML and must automatically select the correct handler based on file extension (.mzML, .mzML.gz, .db) and—for gzip files—indexed vs. non-indexed compression status. Use it whenever FileInterface.
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42. ▌
    Flux Variability Analysis Interpretation · holobiomicslab

    Use when when you have sampled the feasible flux solution space of constraint-based metabolic models (via optGpSampler or equivalent uniform sampling) and need to normalize flux predictions across reactions and cell lines for concordance analysis with transcriptomics and metabolomics data.
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43. ▌
    Hierarchical JSON Structure Construction · holobiomicslab

    Use when your input is a tabular file (CSV or Excel) with column headers annotated using MESSES tagging syntax (#<table_name>.id for record identifiers and #.
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44. ▌
    Internal Standard Selection Optimization · holobiomicslab

    Use when after batch correction of metabolomics QC samples using pooled study quality control (SQC) samples, when you have multiple candidate internal standards and need to determine which one stabilizes the compound/internal standard ratio for each compound.
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45. ▌
    Mass Calibration Coefficient Computation · holobiomicslab

    Use when after successfully matching at least 5 reference m/z points (from a .ref file) to spectrum peaks within a PPM error window (starting at ±1.0 ppm and widened iteratively to ±1.5, ±3, ±5, ±7, or ±10 ppm if needed).
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46. ▌
    Mass Spectrometry Ion Formula Assignment · holobiomicslab

    Use when when you have a metabolite structure (SMILES or molecular graph) and need to predict its ionization behavior in a mass spectrometry experiment using a specific derivatizing matrix or ionization mode. Use this skill when the expected ions are non-standard (i.
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47. ▌
    Metabolomics Study Design Interpretation · holobiomicslab

    Use when when you have received Sciex Multiquant TXT export files from a completed metabolomics or lipidomics analytical run and need to verify that QC pool samples were injected at the designed regular intervals throughout the sequence(s).
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48. ▌
    Model Hyperparameter Transfer And Tuning · holobiomicslab

    Use when you have a trained baseline GNN model with established hyperparameters (dropout rate, learning rate, epochs, optimizer settings) and want to evaluate whether alternative message-passing GNN architectures (Graph Attention Networks, Message-Passing Neural Networks) achieve comparable or.
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49. ▌
    Molecular Graph Construction From Smiles · holobiomicslab

    Use when you have raw SMILES strings from a chemical database (e.g., CCSBase, METLIN, or custom sources) and need to feed them into a graph neural network model.
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50. ▌
    Pandas Dataframe Manipulation Ms Columns · holobiomicslab

    Use when when you have raw mass spectrometry data (from mzML, Bruker .d, or CSV format) loaded into a Pandas DataFrame and need to ensure it has the correct column structure (m/z, retention time, intensity) before invoking pyOpenMS-Viz plotting functions like .plot(kind='spectrum'), .
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51. ▌
    Parallel Factor Generation From Metadata · holobiomicslab

    Use when when you have a Spectra object backed by an on-disk MS data source (e.g., MsBackendMzR reading mzML, mzXML, or CDF files) and need to process large numbers of spectra in parallel or serial chunks.
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52. ▌
    Python Dependency Resolution And Pinning · holobiomicslab

    Use when when setting up a multi-stage bioinformatics workflow (e.g., ENPKG) that calls external tools (MZmine, Sirius, SPARQL engines) and depends on specific Python libraries; when reproducibility across team members or cloud infrastructure is required;
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53. ▌
    Python Package Migration And Refactoring · holobiomicslab

    Use when you have a mature Python package (e.g., Mummichog 2.x) that needs to be relocated to a new GitHub organization (e.
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54. ▌
    Quality Control Sample Outlier Detection · holobiomicslab

    Use when you have a SummarizedExperiment object containing pooled quality control samples with measured compound and internal standard peak areas.
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55. ▌
    Strain Correlation Score Standardisation · holobiomicslab

    Use when you have computed raw strain correlation scores (based on shared strain membership) between genomic and metabolomic objects of heterogeneous sizes, and you need to compare link quality fairly across pairs with different numbers of strains.
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56. ▌
    Structured Logging And Metric Extraction · holobiomicslab

    Use when when executing a multi-converter annotation workflow on mass spectra metadata (.
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57. ▌
    Tanimoto Similarity Pairwise Calculation · holobiomicslab

    Use when you have a collection of molecular fingerprint vectors (such as biosynfoni count fingerprints) and need to measure structural similarity between all pairs of molecules.
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58. ▌
    Uncertainty Quantification Rt Prediction · holobiomicslab

    Use when you have trained a DNN retention time predictor and need to rank candidate metabolites for an unknown compound: the DNN outputs both point estimates and uncertainty bounds for each candidate's RT, and you need to convert these into probabilistic scores that reflect confidence in each.
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59. ▌
    Asynchronous Converter Job Orchestration · holobiomicslab

    Use when you have mass spectrum records in .msp format lacking computed chemical metadata (SMILES, InChI, CAS numbers, formulas, IUPAC names) and want to fetch these properties from multiple heterogeneous web services without blocking on individual API calls.
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60. ▌
    Batch File Processing Across Directories · holobiomicslab

    Use when when you need to systematically extract a specific field or set of fields from multiple files scattered across nested directories—for example, to reconstruct an index of entry statuses from thousands of JSON annotation records, or to audit a repository's content without manually visiting.
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61. ▌
    Benchmark Table Generation And Reporting · holobiomicslab

    Use when when you need to quantify and document the computational cost of a multi-stage analysis pipeline (such as MetaboDirect) across varying input sizes (e.
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62. ▌
    Binary Mask Generation From Thresholding · holobiomicslab

    Use when you have imported a laser ablation ICP-MS image into pew² and need to distinguish tissue-bearing pixels from background noise or non-ablated regions.
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63. ▌
    Ceramide Name Standardization With Regex · holobiomicslab

    Use when when downloading a lipidomics dataset from Metabolomics Workbench (e.
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64. ▌
    Chemical Structure Annotation And Export · holobiomicslab

    Use when after executing BioTransformer's environmental microbial degradation module on a small-molecule input, when you need to capture predicted metabolites alongside the specific transformation rules applied and degradation pathway information in a format suitable for subsequent analysis.
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65. ▌
    Chemical Structure Identifier Conversion · holobiomicslab

    Use when when you have a metabolomic dataset with heterogeneous chemical identifiers (names, InChI strings, InChIKeys, or SMILES) and need a single canonical identifier per metabolite to enable comparison across studies, detect redundant entries, or link to external chemical databases for.
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66. ▌
    Classification Performance Visualization · holobiomicslab

    Use when you have a CSV file with predicted probabilities and true binary labels from a classification model, and you need to evaluate classification performance across decision thresholds and communicate it via a standard diagnostic plot suitable for publication or presentation.
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67. ▌
    Cmake Build Configuration For Extensions · holobiomicslab

    Use when when you have C++ source code that needs to be wrapped as a Python extension module (e.g., pyOpenMS nanobind bindings), and you need to automate the build process via CMake to handle compilation, linking, and module artifact generation across multiple platforms (Windows, macOS, Linux).
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68. ▌
    Comparative Abundance Heatmap Generation · holobiomicslab

    Use when when you have parsed lipid expression data (quantitative abundance measurements) organized as rows (lipid identities) and columns (experimental conditions or samples), and need to simultaneously display relative abundance levels across many lipids and conditions to identify clustering.
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69. ▌
    Consensus Clustering Module Construction · holobiomicslab

    Use when after training a neural network model on paired microbiome-metabolome data and extracting microbe-metabolite feature attribution scores for significant interactions (e.g., at the 97.5th percentile threshold).
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70. ▌
    Deep Learning Signal Inference With Onnx · holobiomicslab

    Use when when you have a TransitionGroup structure containing normalized intensity traces (1D signal data from chromatograms or mobilograms) and you want to automatically detect peak regions with high precision by leveraging a conformer-based deep learning model.
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71. ▌
    Deletion Reason Logging And Traceability · holobiomicslab

    Use when when processing OMSLs (Open Mass Spectra Libraries) with heterogeneous data quality, inconsistent annotations, or mixed ionmode/chromatographic modes (LC/GC), and you need to track which spectra were discarded, why, and potentially recover or reprocess them in future iterations.
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72. ▌
    Descriptor Deduplication Across Toolkits · holobiomicslab

    Use when when computing molecular descriptors from SMILES strings using both RDKit and mordred libraries simultaneously, and you need to combine their outputs into a single feature matrix without redundant features.
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73. ▌
    Developer Extension Point Identification · holobiomicslab

    Use when when you need to onboard developers into a modular, object-oriented codebase (such as CloMet) and want to pinpoint specific classes, abstract interfaces, or factory patterns that serve as official extension points rather than requiring developers to read through entire source trees.
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74. ▌
    Disease Stratified Network Visualization · holobiomicslab

    Use when you have metabolite-disease correlation scores and protein association predictions from a deep learning model (such as DeepMSProfiler) and need to visualize which metabolites and proteins co-associate within specific disease groups.
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75. ▌
    Docker Container Deployment Verification · holobiomicslab

    Use when after building and starting a Dockerized TensorFlow Serving instance (via `make server-compose` or equivalent) to validate that the model has the expected input layer names ('input_2048' and 'input_4096') and output layer name ('output') before routing live inference traffic through the.
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76. ▌
    Enzyme Family Classification And Mapping · holobiomicslab

    Use when you have biotransformation reaction data from a curated source like EAWAG's Biodegradation and Biocatalysis Database and need to map enzyme families, reaction types, and substrate specificities into machine-readable rule formats (SMARTS patterns, condition thresholds) for automated.
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77. ▌
    File Format Dispatch And Handler Routing · holobiomicslab

    Use when you have a parser library that must support multiple file formats (e.g., mzML, SQLite, compressed gzip) and want to avoid conditional logic scattered throughout the parsing code.
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78. ▌
    File Interface Integration And Extension · holobiomicslab

    Use when when you need to parse mzML data stored in a format beyond the built-in mzML, mzML.
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79. ▌
    Filter Subclass Implementation In Python · holobiomicslab

    Use when when you need to apply domain-specific or novel filtering logic to compounds during Pickaxe reaction network expansion—for example, filtering by Tanimoto similarity to targets, mass tolerance to metabolomics peaks, or custom molecular descriptors—and the built-in filters do not meet your.
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80. ▌
    Fingerprint Similarity Computation Rdkit · holobiomicslab

    Use when when implementing a similarity-based filter for Pickaxe compound expansion that must retain or remove candidates based on their structural resemblance to a target compound set.
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81. ▌
    Ft Icr Spectrum Recalibration Validation · holobiomicslab

    Use when after applying mass calibration functions (LedFord, linear, or quadratic equations) to an FT-ICR transient or magnitude-mode dataset, before running SearchMolecularFormulas. Specifically, validate recalibration when: (1) comparing recalibrated spectra against reference calibration files (e.
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82. ▌
    Gradient Flow Backpropagation Validation · holobiomicslab

    Use when after implementing a multi-task fusion module (such as FuseBlock) that combines feature tensors from multiple prediction branches (e.g., isotope, charge, retention-time) and must verify that backpropagation signals flow from the fused representation to each upstream branch.
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83. ▌
    Graph Neural Network Architecture Design · holobiomicslab

    Use when you have molecular structures that need to be represented as both fingerprint vectors (fixed-length chemical descriptors) and graph-structured data, and you need a model that can learn from both representations simultaneously to predict a continuous molecular property (e.
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84. ▌
    Hierarchical Dataset Metadata Extraction · holobiomicslab

    Use when when you have received an MZA-format HDF5 file (or other hierarchical HDF5-based scientific data) and need to understand its internal structure before downstream processing, share it across programming languages (Python, R), or document its schema for reproducibility.
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85. ▌
    High Resolution Spot To Spot Integration · holobiomicslab

    Use when you have two spatial omics datasets (e.g., spatial transcriptome and metabolome spot matrices) collected from the same or adjacent tissue sections, with both feature matrices (X: np.ndarray) and spatial coordinates (D: np.ndarray containing location information in .
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86. ▌
    Hnsw Graph Construction And Optimization · holobiomicslab

    Use when when you have pre-computed Word2vec spectrum embeddings and need to perform fast approximate nearest-neighbor retrieval from a library of millions of spectra (e.g., NIST 2017, MassBank, or in-silico predicted spectra).
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87. ▌
    Infrared Spectral Prediction Task Design · holobiomicslab

    Use when when you have a dataset of molecules with experimentally measured or simulated infrared spectra and want to train a graph neural network to predict spectral features (e.g., absorption peaks, intensities) from molecular structure.
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88. ▌
    Internal Standard Optimization Selection · holobiomicslab

    Use when you have preprocessed metabolomics data with multiple candidate internal standards and QC (Quality Control) sample replicates, and you need to assign a single internal standard per compound that will minimize measurement variability across batches.
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89. ▌
    K Nearest Neighbor Algorithm Application · holobiomicslab

    Use when your raw metabolomics dataset contains missing values scattered across metabolite columns, and you intend to perform statistical analysis (e.g., linear mixed models, ANOVA, or dimensionality reduction) that requires complete observations.
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90. ▌
    Lc Gradient Vector Encoding And Decoding · holobiomicslab

    Use when when preparing LC gradient configurations for Bayesian optimization, or when converting predicted optimal vectors back into actionable LC instrument parameters. Specifically, use this skill when you have a set of gradient parameters to optimize (e.
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91. ▌
    Linear Model Design Matrix Specification · holobiomicslab

    Use when when preparing to perform differential abundance analysis on batch-corrected lipid abundance matrices in designs with multiple factors (e.g., treatment × time, multi-group comparisons), repeated measures, or blocking structures.
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92. ▌
    Local Statistics Median Mean Computation · holobiomicslab

    Use when you have raw LA-ICP-MS image data containing potential spike outliers (e.g., instrumental noise, ablation irregularities) and need to establish a local reference statistic for each pixel.
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93. ▌
    Logical Operator Precedence In Gpr Rules · holobiomicslab

    Use when when computing Reaction Activity Scores (RAS) from transcriptomics data linked to a metabolic model via GPR associations, and the model contains reactions governed by mixed AND/OR logical rules (e.g., '(gene_A AND gene_B) OR gene_C').
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94. ▌
    M Z And Retention Time Window Validation · holobiomicslab

    Use when before committing to full-scale targeted peak integration across all LC–MS runs in a metabolomics or lipidomics study. Apply this skill when you have a curated list of 5–50+ target compounds with theoretical m/z values and expected retention times, centroided .
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95. ▌
    Mass Spectrometry Spectral Preprocessing · holobiomicslab

    Use when you have raw mass spectrometry spectra from an unknown analyte or a synthetic compound library and need to feed them into PS2MS or similar deep learning classifiers for NPS detection.
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96. ▌
    Mass Spectrometry Spectrum Preprocessing · holobiomicslab

    Use when when you have raw LC–QTOF wastewater spectra (or other real experimental mass spectrometry data) and need to feed them into a pretrained MSGO model for molecular structure prediction.
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97. ▌
    Metabolism Prediction Result Aggregation · holobiomicslab

    Use when when you have run CypReact predictions on a molecular dataset against multiple CYP isoforms (e.g., 1A2, 2A6, 2B6) and need to consolidate the per-isoform output files into a single combined result file for cross-isoform comparison, visualization, or downstream metabolite identification.
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98. ▌
    Metabolite Network Diffusion Propagation · holobiomicslab

    Use when after cluster-based filtering of KEGG candidates has produced a set of candidate metabolites with assigned scores, but before final annotation ranking.
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99. ▌
    Metabolite Ranking By Network Centrality · holobiomicslab

    Use when when you have a directed metabolic network (digraph) and want to identify which metabolites are most central to observed perturbations; specifically when you have (1) a global network as an edge list, (2) seed nodes with known or hypothesized perturbation (e.
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100. ▌
     Metabolomic Data Subsetting By Phenotype · holobiomicslab

     Use when you have a preprocessed MultiAssayExperiment with metabolite measurements across multiple phenotypic or disease subgroups, and you want to isolate one or more groups for comparison—for example, to compare AKT1-high versus MYC-high prostate tumor samples before applying diff_test for.
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