Claude Skills by HolobiomicsLab

Use when after retrieving a molecular network file (GraphML or JSON format) from GNPS_GC following submission of deconvolved GC-MS spectra. Use this skill to confirm the network structure is sound before performing chemical similarity searches, community detection, or annotation.

Use when you have LC-MS feature tables (m/z and retention time columns) and corresponding .mzXML or .mzML files, and you need to automatically classify whether extracted ion chromatograms represent genuine metabolomic features or false positives.

Use when when building Word2Vec or embedding-based spectral similarity models where you need to capture fragmentation patterns beyond individual peak positions.

Use when when converting MS/MS spectra into spectral documents for Spec2Vec embedding, and you want to capture chemical relationships implicit in the fragmentation pattern (e.g., loss of water, ammonia, or CO2) that may correlate with structural similarity. Use this when the neutral loss range (5.

Use when you have a collection of annotated MS/MS spectra with precursor m/z values and fragment peak lists, and you aim to train or apply a spectral similarity model (such as Spec2Vec Word2Vec) that exploits fragmentation chemistry.

Use when you have uploaded a pre-analytical data table containing sample metadata, processing delay timestamps (pre- and post-centrifugation), and NMR metabolomic measurements for a cohort of plasma or serum samples, and you need to assess how processing delays affect metabolite concentrations and.

Use when you have imported a raw GCxGC-MS chromatogram (NetCDF format folded into 2D-TIC) that exhibits chemical noise, instrumental artifacts, or baseline drift—conditions that obscure true metabolite signals and impede between-group differentiation in downstream multivariate analysis.

Use when when you have an aligned GCIMS dataset and need to configure the findPeaks function with CWT algorithm to detect peaks across retention time and drift time dimensions.

Use when your input is a raw two-dimensional MS map (m/z vs retention time) derived from chromatography–mass spectrometry data with poor signal-to-noise characteristics, and you need to discriminate individual analytes and identify marker features reliably.

Use when you have an unknown mass spectrum from a suspicious analyte and need to determine whether it matches a known NPS or a derivative thereof. The analyte's mass spectrum is available in MSP or equivalent format, and you have a core drug structure to enumerate derivatives from.

Use when you have overlapped peak regions in GC-MS chromatography data (multiple components eluting within the same retention time window) and need to recover the pure mass spectra of each component and their relative concentrations.

Use when after solving for the concentration distribution matrix C using least squares optimization (minimize ||overlapped_peaks - S·C||²), validate that the solution is physically meaningful and numerically accurate by checking whether S·C faithfully reconstructs the input overlapped peak.

Use when when GC-MS chromatograms contain overlapped or co-eluting peaks in a retention time region and you need to recover the individual pure mass spectra and relative abundances of each component without manual peak picking or external standards.

Use when you have trained a GCMSFormer Transformer model on augmented simulated overlapped peaks and need to measure its generalization performance on unseen test data.

Use when your input consists of multiple large MSP files (hundreds of megabytes) with associated structure folders containing hundreds of thousands of MOL or SDF files that require sequential processing steps (structure extraction, SMILES matching, RI assignment).

Use when when compiling EI or MS/MS spectral libraries from multiple gigabyte-scale sources (e.

Use when you have peak intensity vectors from LC/GC-MS experiments with corresponding QC (quality control) sample measurements, and you need to correct for batch effects—especially when QC samples show systematic shifts in peak heights across analytical batches.

Use when when you have a pre-aligned GCIMSDataset and need to systematically identify and annotate chromatographic peaks across multiple samples.

Use when traditional peak extraction algorithms have produced a feature table that you suspect is incomplete or missing known suspects from your target database (e.g., T3DB).

Use when you have preprocessed (smoothed and baseline-corrected) 2D-GCxGC-MS chromatograms from multiple samples and need to align their peak positions to a common reference chromatogram before multivariate analysis.

Use when you have raw LC-MS/MS spectra from vendor instruments (mzML, mzXML, MGF, or MSP format) with variable peak quality and intensity distributions, and you plan to perform library matching, molecular networking, or spectral similarity comparison.

Use when after peak detection and clustering have been completed on aligned and baseline-corrected GC-IMS data, and before imputation or statistical analysis.

Use when when training Word2Vec embeddings on mass spectra represented as peak-word documents, and you need to preserve the quantitative intensity relationships between fragments without allowing a single dominant peak to overwhelm the learned word associations.

Use when you have a peak intensity matrix from LC/GC-MS analysis with known QC sample indices and suspect batch-related systematic variation in feature intensities. Use it as a preprocessing step before downstream analysis (e.

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

Use when you have claims in a paper or tool documentation that one peak picking method outperforms others (e.g., 'IDSL.IPA outperforms MZmine 2 and xcms'), but the specific comparison metrics, numerical results, and source tables are not provided in the abstract or introduction.

Use when you have identified a claim that one tool outperforms another (e.g., 'IDSL.IPA outperforms MZmine 2 and xcms') in a research article's abstract or introduction, but the specific metrics and results table are not included in the introductory text.

Use when after uploading a sample list to InjectionDesign and before performing inter-batch balancing and intra-batch randomization.

Use when you have raw GC–MS or LC–MS data in two-dimensional m/z vs retention time format and need to identify marker features at parts-per-billion sensitivity without relying on conventional peak picking.

Use when you have Nightingale Health 1H-NMR metabolomics measurements for a new cohort and wish to compute one or more established metabolic risk scores (mortality, MetaboAge, cardiovascular event, type-2 diabetes, COVID-19 severity) without recalibration.

Use when analyzing GC-MS data containing overlapped peaks where two or more components co-elute within the same retention time window, making direct spectral assignment impossible.

Use when when you have a pre-trained PyTorch model checkpoint (e.g., a MWFormer transformer) and structured input data (e.g., electron ionization mass spectrum m/z and intensity pairs) that must be converted to tensor format and passed through the model to produce a direct numerical output (e.

Use when you have a QC-annotated feature table (samples × features with QC sample identifiers) from LC-MS untargeted metabolomic profiling and observe systematic signal drift across the run sequence or between batch blocks.

Use when when constructing a sample list from an Excel template for LC/GC-MS analysis, you must classify each QC sample by type before proceeding to plate layout and randomization steps.

Use when configuring a new injection-plate design template in InjectionDesign if you have multiple QC types to position on a plate and need to visually distinguish them in the final worksheet.

Use when after composite-map peak detection has produced an unfiltered peak list with SNR, peakshape (goodness_fitting), peak_height, and prominence values.

Use when you have a feature intensity matrix (peak vector) and a corresponding set of QC sample indices from a multi-batch LC/GC-MS experiment, and you need to establish batch-invariant reference statistics before applying QC-based batch correction methods such as bcpareto(), bccenter(), or.

Use when you have a peak table matrix with NA values that need to be imputed using cluster statistics, or when a GCIMSDataset object requires filtering by retention time (0–1100 s) and drift time (5–16 ms) ranges, or when you need to apply smoothing, decimation, or baseline correction to raw GCIMS.

Use when after performing assignment operations (assign_ri, assign_smiles) or combining multiple library objects (e.

Use when you have constructed or received a SummarizedExperiment object (or similar S4 class) containing MS feature tables, counts matrices, or sample-level metadata, and need to retrieve specific slots (e.

Use when when you have predictions from one or more metabolite annotation models (MLP baseline, GNN, or ESP ensemble) and need to quantify ranking performance on ESI/LC-MS test spectra.

Use when you have multiple pre-trained neural network models (e.g., MLP and GNN) that produce overlapping predictions on the same set of candidates, and your evaluation metric is rank-based (average rank, Rank@K) rather than point-wise accuracy or RMSE.

Use when you have raw GC-MS output files (vendor formats or netCDF) from a chromatography instrument and need to prepare them for automated peak deconvolution and spectral analysis.

Use when when performing peak detection on Gas Chromatography–Ion Mobility Spectrometry samples where the Reactant Ion Peak (a high-intensity background signal from the ion source) would otherwise be misidentified as an analyte peak, causing false positives in the peak list.

Use when after combining multiple EI or MS2 mass spectral libraries and you have access to NIST RI reference files (ri.dat and USER.DBU) and need to assign experimental retention indices to compounds.

Use when processing raw GC-MS data in NetCDF format where peaks have been detected but lack standardized retention indices.

Use when when processing GC-MS data where retention time alone is insufficient for compound identification due to instrument drift or method variation, and you need to match detected peaks against a spectral library (e.

Use when you have a compiled EI or MS2 library object (from read_lib or c() combination of multiple sources) and a local NIST library installation with accessible ri.dat and USER.DBU files in the mssearch/nist_ri directory.

Use when you have extracted peaks from multiple LC/HRMS batches (n > 500 samples across different analytical runs or days) and observe systematic retention time drift or offset between batches, preventing reliable cross-batch peak matching on m/z and RT alone.

Use when you have raw GC-MS output in CSV format (with columns: Component.RT, Base.Peak.MZ, Component.Area, Compound.Name, Match.Factor, File.