Claude Skills by HolobiomicsLab

Use when you have downloaded a Jupyter notebook from a published metabolomics workflow repository (e.

Use when after aligning fragment ions between two tandem mass spectra (query and reference) using maximum weight matching and you need to assign confidence scores to the matched ion pairs.

Use when after standardizing link scores (strain correlation and IOKR) across all potential GCF-MF pairs in a metabologenomics dataset, perform significance testing to determine whether validated links show statistically distinguishable score distributions from the background.

Use when when you have loaded search results from two or more DIA-MS analysis tools (e.

Use when you have LC-IM-MS/MS raw data from sterol-containing tissue samples and need to assign detected peaks to specific structural isomers (e.g., distinct double bond positions or saturation patterns in C27–C29 sterols).

Use when you have LC-IM-MS/MS experimental data (raw mzML or vendor format) containing signals from N-Me derived unsaturated sterol lipids and need to assign double-bond positions and stereochemistry to individual sterol isomers rather than sum compositions.

Use when when you have a collection of N-Me derivatized unsaturated sterol structures from tissue samples or standards that must be fed into MS/MS fragmentation prediction or collision cross section (CCS) prediction workflows.

Use when use STOCSY when you have preprocessed 1H NMR spectral data with an unidentified peak of interest (driver signal at a specific δ ppm value) and need to determine its metabolite identity by finding correlated signals across the spectrum.

Use when when preparing multiomics datasets for NPLinker that combine GNPS metabolomics, AntiSMASH genomics, and MIBiG reference data, and you need to establish which strain IDs or sample identifiers appear in both genomic and metabolomic experiments.

Use when when you have downloaded and extracted a GNPS archive (from GNPS1 or GNPS2 workflows) and need to establish which spectral records (from spectra.mgf) were generated from which bacterial strains or samples. The file_mappings.tsv or file_mappings.

Use when you have structural candidates from in silico tools (SIRIUS/CANOPUS) and library spectral matches from GNPS, but need to resolve conflicting or incomplete chemical classifications into a unified consensus.

Use when after identifying statistically significant LC-MS features (e.g., via MB-VIP with p < 0.01 and permutation testing), when you need to consolidate multiple ionization and isotopic forms of the same metabolite into structural groups for annotation and pathway mapping.

Use when after you have identified statistically significant LC-MS features and run MamsiStructSearch to generate structural clusters (isotopologue groups, adduct groups, cross-assay links) and computed correlation cluster assignments.

Use when you have trained or loaded a deep learning model that produces high-dimensional spectral embeddings (e.g., 200-dimensional vectors from MS2DeepScore base network) and need to validate that these embeddings capture chemical structure information rather than learning spurious patterns.

Use when when you have paired MS/MS spectra from unknown metabolites and a reference database of known metabolites, and you want to rank candidate structures for unknown compounds by their predicted structural similarity rather than exact spectral matching.

Use when when you have a collection of mass spectra with annotated chemical structures (SMILES/InChI) and need to generate structural similarity labels to train or validate a model that predicts molecular similarity from spectral pairs.

Use when you have an unknown tandem MS spectrum and seek to assign a chemical structure by matching against a curated reference database (e.g., NIST, GNPS, or custom metabolite libraries) without requiring an exact spectral match.

Use when you have GNPS-style MGF spectral files from MS/MS experiments and need to predict the molecular structure (as SMILES) of unknown compounds.

Use when you have experimental mass spectrometry spectra (LC–QTOF or similar format) from unknown compounds and need to predict their molecular structures.

Use when when you have an unknown metabolite's predicted structural similarity scores (from a deep learning model such as DeepMASS) against all known metabolites in a reference database, and need to identify which known metabolites are most likely structurally related to the unknown to guide.

Use when when you have tandem mass spectra (MSMS) from related or candidate molecules and need to determine which similarity metric—cosine, modified cosine, or neutral loss— ranks structurally similar compounds in your dataset. Particularly useful when structural relationships are known a priori (e.

Use when when you have MS/MS spectra with assigned precursor formulas and need to annotate fragment peaks with their chemical subformulas, but want to avoid the computational overhead of generating full SIRIUS fragmentation trees or do not have access to spectrum databases.

Use when when performing chemical denoising of MS/MS spectra: after modifying a master formula based on SMILES and adduct information, enumerate all possible subformulas to establish the set of chemically valid neutral losses.

Use when when performing chemical noise removal on MS/MS spectra and you need to validate whether each fragment ion m/z is consistent with loss of a subformula from the precursor ion.

Use when you have LC-MS normalized intracellular metabolite abundance data from multiple cell lines (or samples) and need to compute reaction-level propensity scores that account for substrate availability as a predictor of metabolic flux.

Use when you have created a GNPS molecular network (classical or feature-based workflow) and separately run an MS2LDA experiment on the corresponding MGF file, and you want to associate each network node with its constituent substructural motifs and visualize which motifs are shared between.

Use when you have (1) a GNPS molecular network (classical or feature-based) with cluster/feature identifiers, (2) MS2LDA output containing Mass2Motif assignments with probability and overlap scores for those same clusters/features, and (3) a goal to annotate network nodes with substructural and.

Use when when you have imported a tab-delimited metabolomics file (via readData or similar) containing columns for compound identifiers, sample/aliquot names, peak areas (primary assay), internal standard areas (secondary assay), and sample type classifications, and you need to organize these into.

Use when when you have clinical survival outcomes (event status and follow-up time) and high-dimensional metabolomic or expression data, and you need to feed them into Cox-PH or Cox-nnet prognosis models.

Use when you have LC-MS peak/feature data, a curated suspect compound database with known m/z, retention time, and fragment ion coordinates, and you observe that traditional peak extraction algorithms have failed to detect ions corresponding to suspected contaminants or chemicals of interest.

Use when you have SWATH-MS raw data (mzML or vendor format) from an untargeted metabolomics experiment and need to identify metabolites.

Use when you need to create defined LC-MS/MS datasets with known molecular composition and fragmentation patterns for algorithm validation, method development, or evaluation of analytical challenges (e.g., co-elution prediction).

Use when when you need to create benchmark LC-MS/MS datasets with controlled, known composition for testing MS analysis algorithms, validating retention-time or fragmentation predictions, or predicting co-elution and co-fragmentation challenges before conducting real experiments.

Use when you need to query or extract data from Thermo Fisher Scientific .raw files or other proprietary binary formats accessible only through a compiled external executable (e.g., RawFileReader .NET assembly). The executable returns text or structured output (e.

Use when you need to execute a complex computational chemistry workflow (QCxMS2) that depends on multiple external semiempirical and ab initio quantum chemistry packages.

Use when when processing multiple LC-MS samples in a cohort study and MassGrid construction reveals that anchor mass tracks (13C/12C isotope or Na/H adduct pairs) in non-reference samples deviate systematically from the reference sample's m/z values by >1 ppm.

Use when when you have raw metabolomics measurements in tab-delimited text format (e.g., from Sciex OS exports) and need to load them into R for quality control analysis.

Use when you have a feature quantification table output from MZmine3 feature detection (rows = features, columns = sample abundance values) and a separate sample metadata file (rows = samples, columns = sample attributes) that need to be unified before downstream statistical analysis, data cleanup.

Use when you have extracted MS1 or MS2 peak lists and scan headers from Thermo Fisher RAW files using MetaXtract and need to load them into pandas, NumPy, or external analysis tools.

Use when after generating a preliminary feature table from LC-MS data (e.g., via Asari), when experimental design includes blank samples or negative controls and you need to filter out features that are likely instrumental or chemical background rather than true biological signal.

Use when you have a txt or tabular export file from a liquid chromatography–mass spectrometry (LC-MS) instrument (e.g., Sciex MultiQuant > v3.0.

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.

Use when you have tandem mass spectra (in .msp or compatible format) from instruments like Orbitrap, and you need to compute library matching scores, cluster spectra by chemical similarity, or embed spectra into a learned vector space for downstream similarity or clustering tasks.

Use when when you have raw tandem mass spectra in mz/intensity format with precursor m/z values, and need to extract all fragmentation features (observed peaks and neutral losses) as a foundation for building interpretable machine learning models.

Use when you have an unknown MS/MS spectrum (tandem mass spectrum) with a measured precursor m/z and fragment peaks, and you need to assign the most likely molecular formula and ionization adduct (e.g., [M+H]+, [M+Na]+, [M+K]+).

Use when you have centroided LC-MS/MS spectra (in MGF, mzXML, mzML, or mzData format) and wish to identify peptidic natural products or ribosomally synthesized and post-translationally modified peptides (RiPPs) against a known structure database or custom RiPP structure database.

Use when you have acquired raw tandem MS data from ProteomeXchange or vendor instruments in proprietary formats (e.g., Thermo .raw files) and need to perform comparative clustering benchmarks or quality assessments across multiple clustering tools.

Use when you have pairs of cleaned tandem mass spectra with known chemical structure annotations (InChIKey, SMILES, or InChI), and you need to predict Tanimoto structural similarity scores directly from spectral data without pre-computing molecular fingerprints.

Use when you have high-resolution MS2 data (.ms2 format) from tandem mass spectrometry analysis of lipid A-containing samples and need to perform automated structure annotation and identification at systems scale.

Use when you have a new fragmentation acquisition strategy (e.g., a weighted exclusion variant, alternative TopN ranking, or dynamic isolation window rule) that you wish to evaluate without access to real mass spectrometry hardware.