Claude Skills by HolobiomicsLab

Use when when processing raw FT-ICR transient data (e.g., ESI_NEG_SRFA.d) that requires assignment of molecular formulas to experimental m/z peaks. Calibration is necessary before SearchMolecularFormulas because uncalibrated mass error will cause false formula rejections or incorrect assignments.

Use when after loading centroided .mzML LC-MS data and creating a target list with compound ID, name, theoretical or measured m/z, expected RT (in minutes), and polarity designation, perform this validation step to confirm target visibility and refine m/z and RT window parameters before running.

Use when after chromatographic peak detection on preprocessed LC-MS data, when you have detected features (peaks) in multiple samples and need to establish which peaks across samples represent the same molecular species.

Use when processing feature lists from LC- or GC-HRMS data (in mzML format or as custom feature tables with m/z and molecular formula columns) and you need to flag potential PFAS candidates.

Use when after loading an MS-DIAL peak list (feature table with m/z, retention time, intensity, and sample assignments) when you need to remove non-organic or chemically implausible features.

Use when after MS-Dial peak picking and feature table construction, when you observe a high proportion of features with anomalous m/z decimal values that are inconsistent with known metabolite ionization patterns.

Use when when you have parsed two or more MS/MS spectra (precursor m/z and fragment ion lists) and need to quantify all pairwise mass differences between fragment ions before alignment or similarity scoring.

Use when you have centroided data-dependent acquisition (DDA) MS2 spectra from LC- or GC-HRMS and need to annotate detected features with PFAS-specific diagnostic fragments. Use it after feature detection (e.

Use when you have a preprocessed peak list (m/z values and assigned molecular formulas) from direct injection FT-ICR MS of a complex organic mixture (e.

Use when after peak picking and sample alignment when you have an aligned feature table containing m/z and retention time coordinates. Use it when your untargeted LC-MS workflow needs to reduce feature redundancy caused by naturally occurring stable isotope patterns and common adduct formation.

Use when you have raw LC-MS fractional abundances (FAM) data from isotope labeling experiments and need to obtain true mass distribution vectors (MDV) that represent only the isotopic labeling contribution.

Use when when annotating observed mass spectrometry peaks against theoretical fragment ions (b, y, or other ion types) using ProForma 2.0 peptidoforms, compute the m/z deviation for each matched peak to verify that the annotation adheres to your specified mass tolerance (e.g., ±10 ppm or ±0.

Use when when screening LC-HRMS datasets for suspect compounds: you have detected features with measured m/z values and a database of reference compounds with theoretical m/z values, and you need to rank candidate matches by mass accuracy before proceeding to retention time and fragmentation.

Use when you have an untargeted metabolomics feature table with m/z values, retention times, and intensity measurements, a metabolic network representation with compound nodes and chemical formulas, and you want to infer functional pathway activity directly from features without performing.

Use when when you have raw or minimally processed MS/MS spectra (in positive or negative ion mode) and aim to infer recurring fragmentation patterns (Mass2Motifs) using topic modeling.

Use when after mass track extraction from individual LC-MS samples, when you need to align mass tracks across a cohort to produce a unified feature matrix. Specifically: when study size is ≤10 samples, use pairwise anchor-prioritized alignment;

Use when after sample alignment has established consensus retention time and m/z coordinates across all samples, and you need to identify and merge peaks that represent isotopologues (e.g., ¹³C variants) or adducts (e.

Use when when generating a virtual chemical mixture for LC-MS/MS simulation, or when sampling molecular formulas from a metabolite database (such as HMDB), you need to restrict the sample to a specific m/z window that matches your instrument's acquisition range or your analytical focus.

Use when when preparing a chemical database for virtual or real MS/MS acquisition, and you need to focus on a specific m/z window (e.g., 100–1000) that matches your instrument's scan range or your metabolomics study's analytical scope.

Use when you have loaded an MsmsSpectrum object and need to focus analysis on a biologically or chemically relevant mass window.

Use when when you have a feature table from Orbitrap LC-MS containing m/z, retention time, and intensity columns, and you need to group individual mass features into putative metabolites that represent the same chemical entity across different ionization states and isotopic compositions.

Use when you have large-scale MS/MS spectra datasets (hundreds of thousands to millions of spectra) in MGF format that need to be grouped by similarity, and you have access to NVIDIA GPU hardware (GTX 1080Ti or GTX 3090).

Use when you have tandem mass spectra (MS/MS) in .msp format and need dense, chemically meaningful vector representations for library matching, similarity computation, or structural clustering. Apply this when comparing spectra across large reference databases (e.

Use when you have a collection of MS/MS spectra (in mzML or MGF format) from a proteomics experiment and need to group or retrieve spectra derived from the same peptide without prior peptide identification.

Use when when you have raw mass spectral data in .mgf, .msp, .mzML, or .lbm2 file formats and need to search against a spectral library using entropy similarity or Flash Entropy Search. Also apply this skill before building spectral library indices or computing entropy-based compound identification.

Use when you have raw LC-MS/MS spectral data in vendor formats or unvalidated .mgf files before feeding them into the specXplore importing pipeline.

Use when when you have separate LC-MS peak tables for unlabeled (C12) and labeled (C13) isotope tracer experiments and need to identify which features correspond to the same metabolite across the two labeling conditions.

Use when you have m/z values from spatially-resolved mass spectrometry imaging (e.g., MALDI-MSI, DESI-MSI) and need to assign molecular formulae to thousands of features with higher precision than traditional LC-MS approaches.

Use when you have untargeted metabolomics MS/MS spectra from multiple features and need to identify which features belong to the same molecular family or are related by biotransformation.

Use when you have unaligned MS2 spectra from one or more samples (in formats like .mgf, .mzML, or .mzXML) and need to compare them in a retention-time-agnostic manner.

Use when after you have (1) identified putative labelled features with intensity and m/z measurements from LC/MS data (e.g., via basepeak_finder output in geoRge), (2) defined a list of expected ionization adducts (e.

Use when you have acquired EI or MS/MS spectral libraries from multiple public sources (NIST, RIKEN, MoNA, SWGDRUG, GNPS) with inconsistent metadata field layouts, missing or misplaced SMILES entries, undocumented retention indices, or mixed polarity modes, and you need to merge them into a single.

Use when when applying a pre-trained Spec2Vec Word2Vec model to new mass spectra (particularly those outside the model's training distribution), you need to assess whether peaks and neutral losses in query spectra have been seen during model training.

Use when you have a GNPS molecular network (classical or feature-based) and MS2LDA LDA experiment output (Mass2Motif assignments with probability and overlap scores) from the same experiment, and you want to annotate network nodes with structural motifs and chemical classes to infer molecular.

Use when you have centroided MS2 spectra (in mzML format from data-dependent acquisition) and a list of known or suspect PFAS diagnostic fragment masses, and you need to systematically flag which detected features contain fragments characteristic of PFAS compounds (e.

Use when you have one or more individual MS/MS spectra (in mzML, mzXML, or JSON format) and need to identify the compound(s) and their biological source by searching against a domain-specific spectral library.

Use when after peak detection and feature table generation when you have a collection of m/z, retention time, and intensity values and need to identify which features are related variants (isotopes, adducts, or fragments) of the same parent compound.

Use when you have predicted structural similarity scores (e.g., Tanimoto or Dice scores) for a large set of spectrum pairs and need to assess prediction accuracy across the full range of possible similarities. Critical when evaluating whether uncertainty filtering (e.

Use when after XCMS feature detection, grouping, and retention time correction when you have aligned features with consistent retention times and intensity patterns across samples.

Use when when you have a list of chemical compounds (with m/z values, retention times, and intensities) and need to simulate their acquisition behavior under a specific ionization polarity and mass spectrometer configuration.

Use when you have a neutral molecular formula (e.g., C3H8O2) and need to compute the adducted formula that will actually be observed in MS data;

Use when when you have baseline MS/MS peak annotations from a known compound but need to refine them using newly available structural information (e.

Use when after PuInc_seeker has identified putative incorporations in XCMS-processed LC/MS data, when you have paired unlabeled and labeled sample groups (e.

Use when you have implemented or modified a tandem mass spectrometry formula inference model and need to measure whether a specific architectural change (e.

Use when you have raw profile LC-MS data in .mzML format and need to prepare candidate peak regions for classification by a neural network detector (e.g., QuanFormer).

Use when after MS2 annotation and sample alignment have been completed in JPA, when you need to visualize ion chromatograms for quality control, validate feature identities, or export chromatographic evidence for specific metabolic features across multiple samples.

Use when you have 32-dimensional GLEAMS embeddings (output from the `gleams embed` step) and need to group spectra by their underlying peptide identity.

Use when after generating transformation products using generateTPs() with structural information (SMILES), when you need to screen for predicted TP compounds in environmental MS/MS data via MetFrag's in-silico fragmentation annotation.

Use when you have two LC-MS feature tables (each containing m/z, retention time, and intensity columns) from the same or related biological samples and need to identify which features in dataset A correspond to which features in dataset B.

Use when implementing replacement methods ($<-, [<-, spectraData<-, mz<-, intensity<-, peaksData<-) for a writable MsBackend subclass, or when modifying peak data in an existing backend.