Claude Skills by HolobiomicsLab

Use when when you have high-resolution tandem mass spectrometry (MS2) data in .ms2 format and need to identify and annotate lipid A structures at scale.

Use when constructing a de novo or expanded lipid spectral library that must cover all theoretically possible chain compositions and double-bond positional isomers for one or more lipid classes.

Use when you have a lipid identification or library-generation task that requires you to define a target chemical space bounded by lipid classes (e.g., phosphatidylcholine, triglyceride) and fatty acid composition ranges (e.g., C14–C22 with 0–6 degrees of unsaturation).

Use when after hierarchical fragmentation library matching has produced candidate lipid annotations for a multi-species LC-MS/MS dataset, and you have applied retention time–based filtering rules (e.

Use when you have N-methyl-derivatized unsaturated sterol lipid structures (as SMILES or molecular formula) and need to predict their MS/MS fragmentation behavior before experimental acquisition, or to build a reference spectral library for isomer-level sterol identification in tissue samples.

Use when after raw lipidomic and metabolomic data files have been generated by the Multi-ABLE method and loaded into the R environment, but before performing multivariate statistical analysis to identify differential lipids and metabolites.

Use when you have MS/MS spectra with initial lipid annotations from spectral library matching (e.g., from XCMS + CAMERA or LipidIN's Expeditious Querying module) and seek to improve recall, precision, and annotation coverage.

Use when you have an enumerated list of lipid species (identified by class, fatty acid composition, and chain length) and need to generate theoretical precursor m/z values, fragment ion masses, and relative intensities for targeted or untargeted lipidomics experiments.

Use when you have defined lipid species (class, chain composition, and adducts) and need to generate precursor–fragment transition pairs for targeted lipidomics experiments.

Use when you have experimental tandem MS (MS/MS) spectra from lipid samples (in mzML format) and need to assign molecular identities and lipid classes.

Use when you have MS-DIAL lipid identification results (alignment exports in msp/txt format) and need to distinguish correct from incorrect lipid IDs before downstream analysis.

Use when after peak picking (MZmine, XCMS, MS-DIAL, or Compound Discoverer output) and candidate retrieval, when you have experimental fragment m/z values and multiple candidate lipid species from the in-silico library.

Use when you have experimental peaklist data (CSV or mzML-derived tables) from UHPLC-HRMS/MS instruments (Q-Exactive, Agilent/Bruker/SCIEX Q-TOF) with fragment m/z values and want to annotate them to known lipid identities.

Use when you have identified lipid species unique to your experimental system (e.

Use when when you have curated or synthesized a set of custom lipid species (e.g., rare or organism-specific lipids, modified lipids, or synthetic standards) and need to integrate them into LipidMatch for candidate matching against your experimental MS/MS datasets.

Use when when you have a parsed lipid species table (output from LipidSearch or LIQUID) containing lipid names or identifiers, and you need to annotate each entry with its standardized LIPID MAPS category (e.g., Glycerophospholipids, Sphingolipids) and subcategory (e.

Use when you need to generate a comprehensive, non-redundant inventory of lipid species that span a defined lipid class (e.g., phosphatidylcholine, triacylglycerol) and a range of fatty acid compositions (e.g., C14:0 to C22:6).

Use when you have a list of detected lipids (e.g., from LC-MS/MS lipidomics data) with associated statistical measures (p-values, fold-changes), and you need to test whether specific lipid ontology categories (e.

Use when you have candidate lipid annotations from spectral library matching (e.g., XCMS + CAMERA output) with MS/MS scores, and you need to reduce false positives and predict previously unannotated lipids.

Use when after running MetaboAnnotatoR's annotateRC function when you need to (1) verify that the top-ranked annotation for a feature is correct, (2) understand what alternative lipid structures (e.

Use when you have centroid-mode LC–MS AIF chromatograms processed by xcms and RamClustR (or equivalent), a feature table with m/z and retention time coordinates, and you need to resolve individual lipid species identities with confidence scores and fragmentation evidence.

Use when after generating a complete lipid spectral library with adduct-specific fragmentation patterns and retention time metadata, when you need to deploy the library for targeted or data-dependent acquisition on specific mass spectrometry instruments—either Excalibur-controlled orbitrap.

Use when you have identified lipid species unique to your sample type (e.

Use when after chromatographic peak detection, fill-in of missing peaks, and retention-time-based grouping in LC-MS metabolomics workflows, apply log2 transformation when refining feature groups using correlation of abundances across samples.

Use when you have extracted mass tracks (EICs) from multiple LC-MS samples at 0.001 amu resolution and need to construct a sample-agnostic m/z reference frame.

Use when when processing multiple centroided mzML LC-MS files from the same study and you need to identify which mass tracks represent the same metabolite across samples.

Use when after retention-time clustering has grouped features from multiple LC-MS samples, when you need to refine feature assignments by enforcing m/z consistency and eliminate duplicate or near-duplicate features with the same mass but potentially misaligned retention times.

Use when you have a set of observed m/z values extracted from a Cardinal MSImagingExperiment object, raw LC-MS data, or similar high-throughput MS dataset, and you need to assign them to known metabolites in a reference database (HMDB, Lipidmaps, etc.) with control over mass accuracy tolerance and.

Use when when you have a spatial metabolomics or LC-MS dataset with detected m/z features (as a feature matrix or SpaMTP Seurat object) and need to assign metabolite identities. Specifically: you have observed m/z values, you know the ionization polarity and expected adduct form (e.

Use when you have two peak-picked, conventionally aligned untargeted LC-MS metabolomics datasets (as metabData objects) acquired under different conditions or at different times, and you need to determine which features in dataset X correspond to which features in dataset Y so their sample.

Use when you have extracted peaks from multiple LC/HRMS batches (n > 1) with their m/z and RT values, and you need to identify and align peaks representing the same compound across batches to build a consensus feature matrix.

Use when when you have detected features with m/z, drift time, and retention time dimensions and need to associate peaks into isotopic groups (e.g., monoisotopes with C13 substitutions) or align features across multiple LC-IMS-MS/MS samples.

Use when you have MS-DIAL lipid identifications from an Orbitrap or TOF mass spectrometer and need to remove spurious or low-confidence assignments before downstream metabolomics analysis.

Use when you have a trained NeatMS neural network model and a labelled validation dataset of MS1 peaks (annotated as 'High_quality' or 'Low_quality'), and you need to identify the scalar probability threshold that separates true positive from false positive peak classifications in your specific.

Use when you have molecular descriptors or fingerprints for a set of compounds (e.g., from LC-MS metabolomics) and need to predict a continuous property—such as HPLC retention time—to support compound identification or filter out false positive annotations.

Use when when you have a labeled peak quality matrix (with known pass/fail labels), need to objectively compare performance across multiple classification algorithms (e.

Use when you have a labeled dataset of DIA raw files (.raw, .d, .wiff) with known quality annotations and have extracted the 15 iDIA-QC metrics (raw file characteristics from timsTOF, TripleTOF, or Orbitrap instruments).

Use when after training a binary MS/MS spectral classifier on labeled data, apply this skill to quantify classifier performance before deployment.

Use when after data normalization (Box-Cox transformation) and before hypothesis testing in Step 9 of untargeted metabolomic workflows.

Use when when processing GC–MS or LC–MS data as m/z vs retention time chromatograms and you need to identify biomarker or chemical marker features without conventional peak picking, particularly when false positive detection rates from peak detection algorithms are problematic.

Use when when processing raw chromatography–mass spectrometry data (GC–MS or LC–MS) as a 2D m/z vs retention time map and you need to identify and visualize marker features for analyte discrimination without relying on conventional peak picking.

Use when when training a transformer encoder on tandem mass spectra (MS/MS) and you need to generate positive sample pairs for contrastive learning without access to labeled chemical or spectral analogues.

Use when after parsing MRM transition tables (m/z values, retention times, transition parameters) from mzML data, before statistical analysis or visualization. Use this skill when you have detected but unannotated transitions and need to map them to lipid species with quantified confidence.

Use when you have defined a set of lipid targets (species, adducts, chain compositions) for PRM or MRM analysis and need to generate precursor-to-fragment transition rules that will maximize signal intensity and specificity on your mass spectrometer (Thermo QExactive HF, Agilent QTOF, or.

Use when when you have a set of candidate molecular formulae for a measured m/z value and need to rank them by how closely their theoretical m/z matches the observed value.

Use when you have experimental fragment m/z values from HRMS/MS instruments (Q-Exactive orbitrap, Q-TOF) in CSV or mzML-derived peaklist format, and need to match them against a library of 500,000+ in-silico fragmented lipid species.

Use when when you have a peaklist from IDSL.IPA or similar peak-picking tools (containing observed m/z and intensity values) and need to assign molecular formulas from a prioritized chemical space.

Use when you have intracellular metabolomics measurements (absolute metabolite abundances) for multiple biological samples and want to identify which metabolic reactions are controlled by substrate availability rather than gene expression.

Use when when you have quantified intracellular metabolite abundances (LC-MS normalized values) from multiple samples and need to predict how differences in substrate availability translate into differences in metabolic flux for specific reactions in a constraint-based metabolic model.

Use when you have extracted and intensity-normalized fragment ion masses and neutral loss values from MS/MS spectra and need to prepare them for unsupervised topic modeling to discover recurring fragmentation motifs.