Claude Skills by HolobiomicsLab

Use when when working with raw GC-MS data in NetCDF (ANDI) format that requires peak detection, baseline removal, and retention time alignment before spectral matching against reference libraries such as PNNLMetV20191015.MSL.

Use when you have raw GC-MS data (aroma, breath, or other volatile analyte samples) in NetCDF or vendor-native format and need to identify multivariate chemo-/biomarker features without conventional peak picking.

Use when you have raw GC-MS data (in netCDF or vendor format) containing overlapping chromatographic peaks from complex mixtures where individual compound spectra cannot be resolved by simple peak picking.

Use when when you have deconvolved GC-MS spectra (post-deconvolution output compatible with GNPS_GC input specification) and need to group them by chemical similarity to construct a molecular network.

Use when when you have predicted BGC-spectrum IOKR scores or other pairwise linking scores, and need to rank genomic clusters (GCFs from BiG-SCAPE) against metabolomic clusters (MFs from MS/MS spectra grouping), particularly in NPLinker workflows where one GCF may contain multiple BGCs and one MF.

Use when you have paired GCF and MF datasets with strain membership information and need to rank candidate GCF–MF links to identify which biosynthetic gene clusters likely produce detected metabolites.

Use when after BGC detection and clustering (producing GCFs) and metabolomics profiling (producing MFs with MS/MS spectra), when you have paired genomic and metabolomic data from the same microbial strains and need to rank which GCF–MF pairs are likely to represent true biosynthetic relationships.

Use when when you have integrated genomic data (GCFs from AntiSMASH via BigScape clustering) and metabolomic data (spectra and molecular families from GNPS molecular networking) and need to identify and rank which secondary metabolites detected in spectra are likely produced by which biosynthetic.

Use when you have raw GCIMS sample files (from a GC–IMS instrument) and an annotations table (Excel, CSV, or TSV) with sample metadata, and you need to begin the GCIMS preprocessing pipeline.

Use when you have raw GCxGC-MS data in NetCDF format that contains instrumental and chemical noise (baseline drift, high-frequency signal artifacts) and you need to prepare multiple preprocessed chromatogram objects for downstream multiway PCA or biomarker discovery.

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).

Use when when a feature table from LC-MS metabolomic profiling contains QC (quality control) sample annotations and exhibits systematic signal drift correlated with run order or batch number.

Use when when fitting a nonlinear retention time (RT) mapping spline to anchor feature pairs (m/z and RT values) from two LC-MS datasets acquired under different conditions, you need to determine both the optimal B-spline basis dimension and identify which anchor points are outliers.

Use when when running metabologenomic RiPP detection pipelines (MetaMiner) on the same genomic dataset but with different input sequence formats (e.g., contigs.fasta vs. antiSMASH .final.gbk output), or when unexpected null results occur and input format choice is a plausible cause.

Use when you have pre-processed AntiSMASH BGC annotations (optionally clustered via BigScape into GCFs), GNPS molecular networking spectra and molecular families, and you seek to computationally link biosynthetic gene clusters to observed metabolites without manual curation.

Use when a paired omics project JSON document contains genome identifiers (e.g. IMG IDs, NCBI accessions) but lacks corresponding organism names.

Use when a paired omics project record contains a genome identifier field (e.g., from GenBank) but lacks the corresponding organism name, or when you need to validate that genome identifiers in bulk project records can be resolved to authoritative taxonomy.

Use when you have assembled genomic DNA sequences (contigs in FASTA format, not antiSMASH or BOA output) and corresponding LC-MS/MS data (in MGF, mzXML, mzML, or mzData format) from the same organism, and you want to identify novel RiPPs by linking gene cluster predictions to observed mass spectra.

Use when you have paired genomic (BGCs clustered into GCFs via BiG-SCAPE) and metabolomic data (MS2 spectra grouped into MFs), with strain/sample co-occurrence patterns and predicted BGC–spectrum IOKR scores, and you need to prioritise which GCF–MF pairs are most likely to represent true natural.

Use when you have an MS2 spectral file (MGF format) from LC-MS/MS metabolomics analysis and need to discover latent topics across fragmentation patterns for unsupervised characterization. Use it as the prerequisite step before topic visualization in the ms2lda web interface.

Use when after running qc_summary() on a filtered mpactr object and aggregating ion counts by filter status category (passed/failed).

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.

Use when after computing pairwise cosine similarities between all spectra across two LC-MS/MS datasets when you need a single scalar summary of dataset-level resemblance rather than individual spectrum matches.

Use when when you have preprocessed molecular graph data (node and edge tensors representing atoms and bonds) and need to train a regression model to predict a continuous molecular property (e.g., LC retention time).

Use when you have GNPS molecular networking output (from GNPS1 at https://gnps.ucsd.edu or GNPS2 at https://gnps2.org) that must be integrated with antiSMASH BGC data and MIBiG metadata for natural product mining.

Use when after extracting a GNPS molecular networking job archive using GNPSExtractor, before calling npl.load_data().

Use when you have extracted MS1 and MS2 scans (in mzML/mzXML format) from raw chromatogram files and possess user-provided metadata (retention time, m/z, compound name, molecular weight, annotation fields) that must be combined into a single structured library entry suitable for spectral library.

Use when you have mass spectrometry MS/MS spectral data in GNPS-style MGF format and need to feed it into the Mass2SMILES deep learning model for structure and functional group prediction.

Use when you have computed frequent fragmentation patterns from a collection of MS/MS spectra using mineMS2, and you want to focus pattern interpretation on subsets of spectra that form meaningful network components (connected groups, cliques, or high-similarity pairs) in a GNPS molecular network.

Use when you have downloaded a GNPS archive from either GNPS1 (https://gnps.ucsd.edu) or GNPS2 (https://gnps2.org) and need to programmatically load and validate its contents (spectra.mgf, molecular_families.tsv, annotations.tsv, file_mappings.

Use when you have a GNPS molecular network job and need to programmatically load the network structure, merge external annotations (chemical class, MS2LDA substructural motifs), and export a unified annotated network for visualization in Cytoscape or other graph analysis tools.

Use when you have generated a GNPS mass spectral molecular network (in classical or feature-based mode) and want to annotate network nodes with substructural motifs from MS2LDA or chemical class information.

Use when when you have a USI string (e.g., mzspec:GNPS:TASK-d93bdbb5cdda40e48975e6e18a45c3ce-...

Use when you have GNPS library accession IDs (e.g. CCMSLIB00011906190) for a reference compound and a chemically or biologically modified analog, and need to load their full MS/MS spectra and structural annotations to set up a modification-finding analysis.

Use when you have downloaded a GNPS molecular networking job archive and need to extract its contents (spectra.mgf, molecular_families.tsv, annotations.tsv, file_mappings) but do not know which GNPS workflow version produced it, preventing correct file naming and downstream computational analysis.

Use when when you have a GNPS molecular networking task ID and need to fetch the job archive, decompose it into standard metabolomics file formats (spectra.mgf, molecular_families.tsv, annotations.tsv, file_mappings), and prepare them for integration with genomics data.

Use when when you have run a spectral networking job on GNPS (e.g. ProteoSAFe-METABOLOMICS-SNETS-V2) and need to reuse the network output files locally with MetaMiner or another tool that accepts spectral network input directories.

Use when when processing large-scale mass spectrometry datasets (>1 million spectra) where CPU-based clustering runtime would exceed minutes to hours, and when the analysis pipeline includes: (1) encoding raw spectra into high-dimensional binary vectors, (2) computing pairwise distance matrices.

Use when you have a large collection of tandem mass spectra (≥100k spectra) in MGF format and need to group spectra by similarity (precursor m/z, charge, and fragment ion patterns) for spectral library construction, peptide identification, or quality control.

Use when when clustering or encoding large MS/MS spectra datasets (>1 million spectra) where CPU-only runtime exceeds practical thresholds (hours to days).

Use when after implementing a composite loss function that combines multiple loss terms (e.g., InfoNCE contrastive loss and MSE reconstruction loss) in a PyTorch module, and before running full-scale training on MS/MS spectra data.

Use when when you have extracted retention times from the top detected MS1 features in a LC-MS run and need to evaluate whether the gradient spreads those compounds efficiently across the available chromatographic time window—particularly during iterative gradient optimization where you need a.

Use when after fitting a Gaussian Process regression model to prior LC-MS gradient runs (retention times, separation efficiency scores, or compound identification counts).

Use when after dereplication and cosine similarity clustering have been completed on merged LC-MS/MS data, when you need to construct the final molecular network output with predicted molecules as nodes and their parent ions as a second node class, connected by edges that preserve the fragmentation.

Use when you have a GNPS molecular network (in GML or GraphML format) and corresponding MS2LDA substructural feature assignments or chemical class predictions, and you want to systematically propagate these annotations to individual network nodes to enable feature-aware visualization and.

Use when when you have spectral library matches (seed identities with high confidence scores) mapped to initial candidate structures from in silico fragmentation, and you want to propagate those identities to related structures in the fragmentation candidate graph to improve annotation coverage.

Use when annotating metabolites in untargeted metabolomics experiments where both established biochemical pathways and experimental MS2 similarity patterns must be simultaneously leveraged.

Use when you have a collection of MS/MS spectra (stored as Spectrum2 objects in an ms2Lib class) and need to identify which spectra share identical fragmentation patterns—particularly when coupled to a GNPS molecular network to focus on explaining network components (connected components, cliques.

Use when you have a GNPS mass spectral molecular network and wish to annotate its nodes with both chemical class assignments (from GNPS public library matches) and MS2LDA-derived substructural motifs (from classical or feature-based LDA experiments) in a single integrated operation, typically for.

Use when when you have: (1) a collection of molecules represented as molecular graphs (nodes=atoms, edges=bonds with chirality/order attributes); (2) structured metadata describing experimental conditions (e.