Claude Skills by HolobiomicsLab

Use when you have preprocessed GCF-MF link pairs from paired genomics–metabolomics datasets (antiSMASH-detected BGCs clustered into GCFs, and GNPS spectra grouped into MFs) and need to rank them by likelihood of representing true natural product–biosynthetic gene associations.

Use when when you have a collection of microbial genomes with predicted BGCs (via antiSMASH), a set of MS/MS spectra (e.g. from GNPS), and you want to score potential BGC-spectrum associations based on the presence of conserved molecular substructures inferred from the BGC's closest MIBiG homolog.

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

Use when constructing HPLC column feature vectors from raw metadata that includes additive composition flags (e.g., presence/absence or concentration of formic acid, acetic acid, TFA, or phosphoric acid in mobile phase eluents A and B).

Use when you have raw LC-MS data in .mzML (XML-based) or Thermo .raw (proprietary binary) format and need to load it into memory for visualization, querying, or downstream analysis.

Use when you have curated a labeled dataset of MS/MS spectra annotated as 'relevant' (compounds of interest obtained from reference standards) or 'other' (reference standards or non-target compounds), and you need to build a classifier to automatically distinguish these two classes on unknown.

Use when when you have a pretrained spectrum encoder (TCN) and need to train a formula rescoring module that ranks candidate molecular formulas against MS/MS spectra.

Use when when you have both (1) a molecular network graph from GNPS with aligned MS/MS features (nodes and edges) and (2) a bioassay-guided fractionation activity matrix (fractions × bioactivity measurements), and you need to determine which molecular features are responsible for observed.

Use when when you have (1) a molecular network graph from GNPS with node identifiers and edges, (2) LC-MS/MS features quantified across fractions in a feature table, and (3) bioassay measurements (e.

Use when when you have LC/MS feature data (m/z, retention time, intensity) that must be annotated against a metabolic database and you have access to (or can construct) knowledge about biochemical reaction networks, expected isotope patterns, and instrument-specific adduct formation rules.

Use when you are building a new mass spectrometry data backend or storage layer and need to integrate it with the Spectra ecosystem. Triggers include: (1) you have a novel data source (raw files, databases, web APIs) that should be accessible through Spectra objects;

Use when after constructing a SummarizedExperiment object from raw metabolomics data via buildExperiment, or after batch correction and ratio computation steps, inspect rowData, colData, and assays slots to verify that compound identities, sample annotations, and computed assay values (primary.

Use when after successfully reading and validating a tab-delimited metabolomics file (containing mandatory columns: aliquot, compound, area, type, injection_time, batch) using readData, and before applying batch correction, outlier detection, or other QC filters.

Use when constructing or enriching a chemical formula database that must capture not just structural similarity (DBEdges) but also biological co-occurrence patterns.

Use when you have LC-MS/MS spectra (MGF, mzXML, mzML, or mzData format) from a bacterial or fungal strain and corresponding genomic sequence data (FASTA, antiSMASH .final.gbk, or BOA .annotated.

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.

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.

Use when you have untargeted metabolomics data with unknown or ambiguous molecular identities, anchor metabolites (known structures in SMILES or MOL format), and a curated database of biotransformation rules (e.g., from KEGG, RetroRules, or domain-specific repositories).

Use when you have two MS/MS fragmentation spectra with fragment ion lists and computed pairwise mass differences (m/z deltas) between them, and you need to find the alignment of ion pairs that: (1) does not reuse any ion from either spectrum, (2) maximizes total matching quality (e.

Use when after completing dereplication and cosine similarity clustering in the MolNotator workflow, when you have merged, dereplicated molecular and ion data (output from mode_merger and dereplicator steps) and need to assemble the final molecular network representation connecting predicted.

Use when your peak table includes features flagged in blank control samples (e.g., solvent blanks, media blanks) at relative abundance above a project-specific threshold.

Use when apply this filter after feature detection and before downstream statistical analysis when your experimental design includes blank samples (e.

Use when after MS1 feature detection and accurate mass annotation, when you have identified a set of blank injections (negative controls) run in the same analytical sequence segment as your biological or study samples.

Use when you have a feature quantification table exported from MZmine3 processing of non-targeted LC-MS/MS data and your experimental design includes blank (negative control) samples.

Use when when you have tandem mass spectra from ribosomally synthesized peptides (RiPPs) and suspect the presence of unknown or non-standard post-translational modifications that would be missed by standard database search modes constrained to known modification classes.

Use when you have raw LC-MS feature-intensity tables (rows: samples, columns: metabolite features, values: raw intensities) with non-normal, skewed distributions and need to normalize them prior to statistical analysis.

Use when when you need to read proprietary or binary data formats (e.g., Thermo Fisher .raw files) from R but the native implementation is in .NET/C#, and direct language bindings are unavailable or impractical. Use this when the target assembly requires Windows/.

Use when after applying deimos.calibration.tunemix() to positive-mode or negative-mode tune mix reference data (containing known CCS values across m/z range 118–1522), assess whether the single-field calibration model's r-squared coefficient meets the expected precision (typically ≥0.

Use when after querying a formula database (KEGG, PubChem, or user-supplied) with neutral mass values derived from observed m/z peaks and adduct transformations, when multiple candidate formulae fall within the configured mass tolerance window (ppm or Da) and you need to rank them by likelihood.

Use when you have an observed m/z value from mass spectrometry imaging and need to assign a chemical formula with high confidence.

Use when when you have a query MS/MS spectrum (m/z and intensity pairs) and need to find potential structural analogues or exact matches in a large spectral library. Apply this skill after preprocessing your spectra (e.

Use when you have an unknown compound's mass spectrum (m/z peaks and intensities) in positive or negative ion mode and need to identify candidate metabolites from a structure database.

Use when when you have generated a set of predicted metabolite structures from BioTransformer's metabolism prediction engine and need to assign identity to observed compounds from LC-MS/MS, spectral, or chromatographic experiments.

Use when after computing InChIKey and neighbourhood scores for library match candidates, you need to write results to a persistent format (CSV, JSON, or database) for storage, sharing, and interpretation.

Use when you have a query MS/MS spectrum matched against a library and need to re-rank the top 2000 candidate spectra by combining spectral similarity (MS2Deepscore) with structural neighbourhood information.

Use when after compound database dereplication has generated per-spectrum candidate lists (e.

Use when you have a query mass spectrum and a set of candidate molecular structures, and you need to prioritize candidates by their likelihood of matching the query. Typical triggers include: (1) you have computed or extracted spectral features (e.

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

Use when after molecular formula assignment from FT-ICR MS peak data, when you need to classify metabolites by their redox state to predict bioavailability or lability, or when generating thermodynamic indices for chemodiversity analysis and environmental metabolomic interpretation.

Use when when you have loaded an unprocessed Cardinal object from MS imaging data (e.g., from Zenodo or native formats) containing thousands of m/z features across many spectra, and you need to produce a curated peak list with known expected peak count (e.g., 687 cleaned peaks from PIGII_206).

Use when use this strategy when analyzing tandem mass spectrometry data where you expect both unmodified and modified peptide identifications and require high confidence assignments with controlled false discovery rates.

Use when after running qc_summary() on a filtered mpactr object when you need to understand the distribution of ions across filter status categories (passed vs. failed filters) and want to visualize or report the count and percentage composition of each status group.

Use when when preparing heterogeneous column-metadata inputs for a graph transformer model that operates on molecular graphs. Specifically: (1) you have both categorical metadata (e.

Use when you have acquired tunemix or reference standard data in ion mobility spectrometry with known m/z, drift time, and CCS values, and you need to establish a drift-time-to-CCS mapping for a specific instrument, ionization mode (positive or negative), and buffer gas.

Use when you have positive-mode tune mix reference data (e.g., example_tune_pos.h5) with known CCS values spanning a wide m/z range (e.g., 118.086–1522 m/z) and need to establish a CCS calibration model to convert experimental drift times or collision cross sections for downstream analysis.

Use when you have structural input data (SMILES or molecular geometry files) for N-Me derived unsaturated sterol lipids and need to generate a predicted CCS dataset indexed by lipid identifier and structural isomer class.

Use when apply CLR normalization when you have count-based microbiome or metabolome compositional data (e.g., 16S rRNA gene abundances, LC-MS/MS metabolite abundances) that will be used as input to multivariate predictive models (neural networks, regression, correlation analysis).

Use when you have vendor-independent centroided mzML files from data-dependent acquisition (ddMS2) HRMS experiments and need to extract a reproducible feature list with mass, chromatographic, and intensity dimensions as input to PFAS prioritization, suspect screening, or other MS-based analyses.

Use when you have centroided data-dependent acquisition (DDA/ddMS2) mzML files from LC- or GC-HRMS measurements and need to systematically detect and characterize MS1 features across the mass-to-charge and retention-time dimensions prior to compound prioritization or suspect screening.

Use when you have added or modified user-facing parameters to a model class (such as L1/L2 regularization in SiameseModel), written unit tests to verify the new functionality, and need to communicate these changes to users and maintain a historical record.