Claude Skills by HolobiomicsLab

Use when immediately after executing the MetaboAnalystR 4.0 unified LC-MS workflow (feature detection and quantification module) on raw mzML or netCDF data.

Use when after applying mpactr's filter suite (filter_mispicked_ions, filter_group, filter_cv, filter_insource_ions) to a peak table, use this skill when you need to summarize the overall filtering outcome across all ion categories and present a compact, area-proportional view of which ions passed.

Use when after chaining one or more mpactr filter operations (mispicked, group, cv, insource) on an imported peak table and before generating quality-control reports or interactive visualizations.

Use when you have XCMS-processed LC/MS peak data from dual-labeled (e.g., 13C) and unlabeled (12C) metabolomics samples and need to distinguish features genuinely enriched by stable isotope incorporation from noise or background variation.

Use when you have observed compounds (from LC-MS/MS, GC-MS, NMR, or other analytical techniques) with unknown identity and you want to assign candidate metabolite structures by comparing them to computationally predicted metabolism pathways.

Use when your metabolomics dataset contains metabolite identifiers in multiple formats (e.

Use when you have (1) peak-picked LC-MS AIF features in a feature table with m/z and retention time, (2) corresponding xcmsSet and RAMClustR pseudo-MS/MS spectral objects from centroid-mode raw data, and (3) a research goal to identify which features are lipids rather than other metabolite classes.

Use when after constructing candidate feature pair alignments and retention-time spline mappings in a multi-dataset LC-MS metabolomics integration workflow, when you have access to independent ground-truth annotations (e.

Use when you have raw lipidomic and metabolomic data files generated by the Multi-ABLE method (high-pressure liquid chromatography–mass spectrometry output) and need to prepare them for multivariate analysis to detect differential lipids and metabolites across biological samples (e.

Use when after chromatographic peak detection in LC-MS data, when you have hundreds or thousands of features and need to consolidate ions presumed to originate from the same metabolite.

Use when when you have processed authentic standards with LC-MS in positive and negative ESI modes, converted results to .mzML format, and need to build a validated in-house reference library with MS1 m/z and RT measurements for use in untargeted metabolomics compound identification workflows.

Use when you have an experimental MS/MS spectrum (from MassBank or your own acquisition) and need to create a standardized library entry with ranked fragment ions for use in MetaboAnnotatoR or other fragment-based annotation pipelines.

Use when you have LC–MS All-ion fragmentation chromatograms processed through xcms and RamClustR, a feature table with unknown identities, and you want to recover lipid annotations by matching observed spectra against lipid fragment libraries (e.g., LipidPos).

Use when after features have been grouped into empirical compounds (empCpds) with inferred molecular formulas and adduct assignments by khipu, and you need to assign putative metabolite identities at the formula level.

Use when when beginning an untargeted LC-MS annotation workflow, before attempting to match experimental m/z peaks to metabolite identities.

Use when you have a negative-mode or positive-mode LC-MS feature table with observed m/z values and peak intensities, and you need to identify which metabolites (by KEGG ID) are likely represented.

Use when when you have separate quantification data (abundance matrix), sample metadata (phenotypes, treatment groups, experimental conditions), and spectral data (MS/MS fragmentation patterns or other spectral features) that must be combined for mass spectrometry-based metabolite analysis.

Use when when you have SWATH-MS raw data (mzML or vendor format) containing multiplexed MS/MS spectra from multiple co-eluting precursor ions and need to separate these spectra into individual, annotatable component spectra for metabolite identification.

Use when after downloading GNPS molecular networking results (from GNPS1 or GNPS2 workflows), use this skill to extract and standardize the compressed archive into named, canonicalized files (spectra.mgf, molecular_families.tsv, annotations.tsv, file_mappings.tsv/.

Use when after peak detection and statistical association or classification analysis has identified a set of significant peaks, use this skill when you need to move from individual feature-level results (peak intensities, p-values, importance scores) to functional biological interpretation via.

Use when after generating metabolite-disease correlation data and protein association predictions from a deep learning metabolomics module (e.g., DeepMSProfiler's feature extraction step).

Use when after metabolite annotation has been completed (level-1 confidence via spectral library matching in margheRita or equivalent), and you need to perform pathway enrichment analysis on a subset of significant features (e.

Use when after feature extraction (Asari) has produced a full feature table from mzML data, but before normalization and annotation.

Use when after drift correction has been applied to a MetaboSet object, and before imputation and batch correction.

Use when after training an ensemble model (MLP, GNN, or ESP) on spectral data, use this skill to measure performance on test spectra where ground-truth metabolite identities are known. Essential for comparing model variants (e.

Use when when you have generated a set of candidate metabolites for a given experimental MS/MS spectrum and need to determine which candidate is most likely to be the true metabolite.

Use when your input is a SummarizedExperiment containing multiple batches or injection sequences of metabolomics samples (study samples, QC replicates, calibration lines) with measured ion areas for compounds and assigned internal standards.

Use when you have defined one or more proton NMR spectral regions-of-interest (ROIs) with lower and upper chemical-shift bounds (in ppm) from an experimental NMR spectrum of a biological sample, and you need to identify which metabolites in a reference database (HMDB) have published 1H NMR shifts.

Use when you have Nightingale Health 1H-NMR metabolomics assay output (metabolite concentrations in a samples × features matrix) and you want to compute a published metabolic risk score or surrogate biomarker (mortality risk, metabolic age, cardiovascular event risk, type-2 diabetes risk, COVID-19.

Use when when you have log2-normalized, standardized peak intensity data (rows=peaks, columns=samples) with compound annotations (peak-to-metabolite mappings via KEGG/ChEBI IDs) and need to collapse individual peak signals into pathway-level summary scores for statistical comparison across.

Use when you have a metabolite intensity matrix (rows=metabolites or peaks, columns=samples) paired with metabolite-to-pathway or metabolite-to-feature-group annotations, and you want to score activity levels across pathways or metabolite groupings in a way that tolerates missing peaks and.

Use when you have a metabolomics peak intensity matrix with feature IDs (m/z, retention time, or arbitrary peak identifiers) and need to assign these peaks to standardized metabolite databases or spectral groupings (KEGG compounds, ChEBI IDs, GNPS Molecular Families, or MS2LDA Mass2Motifs) before.

Use when you have peak intensity data from metabolomics experiments with annotated metabolites assigned to known groupings (KEGG pathways, Reactome, GNPS Molecular Families, or MS2LDA Mass2Motifs) and need to identify which metabolite sets change significantly across experimental comparisons while.

Use when when you have multi-batch metabolomics data (SummarizedExperiment object with raw or log-transformed assays) and need to assess whether specific metabolites exhibit systematic signal drift across experimental run order or strong batch effects that would justify hierarchical normalisation.

Use when you have raw untargeted LC/HRMS data (mzXML, mzML, or netCDF format) from population-scale studies (n > 500 samples) and need to extract a comprehensive peaklist with aligned features across all samples.

Use when you have an experimental mass spectrum (or a set of spectra from LC-MS/MS data) and need to identify the underlying metabolite(s) by comparing against known reference spectra in GNPS or a local indexed repository.

Use when when you have quantified intracellular metabolite abundances (LC-MS normalized values) for multiple cell lines or samples, a metabolic network model with reaction stoichiometry, and you need to predict how substrate availability translates into metabolic flux differences.

Use when after statistical analysis (e.g., MB-PLS with permutation testing) has identified a subset of significant LC-MS features (p < 0.05 or similar threshold) that require structural interpretation.

Use when after MamsiStructSearch has completed structural clustering of statistically significant LC-MS features (p < 0.

Use when you have a set of candidate transformed structures generated by biotransformation rules (e.

Use when when importing candidate metabolite structures from public chemical databases (PubChem, ChEBI, etc.) for use in MAGMa-based annotation workflows, or when integrating external structure datasets that may use divergent molecular representation formats or contain non-standard chemical.

Use when you have a parent compound (or set of compounds) in SMILES, MOL, or SDF format and need to predict plausible metabolite structures and pathways in a specific biological context (mammalian Phase I/II metabolism, human gut microbiota, or soil/aquatic microbial degradation).

Use when you have multiple tandem MS/MS libraries in different formats (msp, mgf) from different providers (NIST, RIKEN, MoNA, GNPS) with incomplete or inconsistent structural annotations (missing SMILES or molecular formula fields) and need to combine them into unified, polarity-specific msp files.

Use when when you have MS/MS-annotated features from a natural extract (via SIRIUS, CANOPUS, or ISDB) and need to compute the Literature Component or Class Component of a priority rank—i.

Use when you have metabolomic data (e.g., from LC-MS or GC-MS comparing patient to controls) showing differential abundant metabolites (DAMs), candidate genes from exome sequencing or variant calling, and access to a protein–protein or gene–gene interaction network (e.g., STRING).

Use when you have genome FASTA or annotated genome files (antiSMASH .gbk, BOA .annotated.txt) and wish to discover ribosomally synthesized and post-translationally modified peptides (RiPPs) by integrating genomic and mass spectrometry data.

Use when you have raw peak table data from liquid chromatography–mass spectrometry (LC-MS) or related metabolomic instruments, generated by one of 12 supported software tools (or already in NOREVA's standardized format), and need to prepare it for preprocessing method evaluation or biomarker.

Use when after peak detection in MZmine2 has produced an MGF file (containing MS1 and MS2 spectra) and a feature abundance table (CSV or BIOM), but before running q2-qemistree tree construction or any QIIME 2-based metabolomic analysis.

Use when you have two or more CSV feature tables from independent metabolomic experiments (each with RT, m/z, intensity, isotope, and adduct columns), and you need to merge them into a single aligned feature matrix for downstream batch effect removal, marker identification, or pathway analysis.

Use when you have centroid mzML files from LC-MS acquisitions and need to detect, group, and quantify metabolomic features for a PCPFM experiment. Use it as the first feature-level processing step after file format conversion from raw instrument files (e.g., .raw to mzML via ThermoRawFileParser).