Claude Skills by HolobiomicsLab

Use when when you have a Thermo Scientific .raw file from an Orbitrap instrument (e.

Use when you have a Thermo Fisher Orbitrap .raw file and need to retrieve a specific scan's spectral data (m/z and intensity arrays), validate instrument parameters (resolving power, AGC injection time), or assess signal-to-noise characteristics of fragment ions for a known precursor peptide (e.

Use when when your untargeted LC-MS pipeline must support multiple peak-picking backends and you need to let users specify which algorithm to use (via configuration file or parameter) without hard-coding algorithm dependencies.

Use when when a project JSON document contains genome identifiers but lacks corresponding organism name annotations, and you need to link MS/MS mass spectra with genomic context for downstream biosynthetic gene cluster or chemical ecology analysis.

Use when you have observed metabolites (from LC-MS/MS, chromatography, or spectroscopy) whose identities are unknown, and you wish to constrain the candidate pool by leveraging organism-specific metabolism predictions.

Use when when fitting a nonlinear regression (GAM spline) through retention time anchor points derived from feature pair alignments in LC-MS metabolomics, and you suspect some anchor points are measurement errors or misaligned features that could bias the smooth curve.

Use when after building a SummarizedExperiment object from metabolomics LC-MS data with QC and Study Sample types defined, when you need to identify QC samples with aberrant Compound/Internal Standard ratios (indicating instrument drift, matrix effects, or sample degradation) and Study Samples with.

Use when after peak picking by MS-DIAL and import into R, when the feature table contains m/z values with decimal components that fall within the [4, 8] interval (indicating instrumental artifacts or calibration errors).

Use when when training a fresh CNN model from scratch on labeled LCMS data (e.g., MS1 peak classification in NeatMS), particularly when aiming for a specific performance target (e.g., AUC ROC > 0.9) and you need to avoid wasting compute time on redundant epochs or degrading validation performance.

Use when when you have a paired omics project document (JSON format) that combines MS/MS mass spectrometry data with genome identifiers, biosynthetic gene cluster information, sample preparation, extraction method, and instrumentation metadata, and you need to verify it conforms to the platform's.

Use when when processing LC-MS metabolomics datasets with 10 or fewer samples and requiring reproducible mass track alignment across the cohort.

Use when when you have a cleaned MS/MS dataset with chemical structure annotations (SMILES, InChI, or InChIKey) and need to generate ground-truth structural similarity labels for training a deep learning model.

Use when after anchor selection and RT mapping spline fitting, when you have a fitted metabCombiner object with pre-aligned feature pair candidates and need to determine which parameter weights (A for RT, B for m/z, C for similarity score) discriminate true metabolite matches from false positives.

Use when you have a collection of preprocessed mass spectra (in mzML, mzXML, msp, MGF, or JSON format) and need to quantify similarity relationships across all pairs—for instance, to identify redundant spectra in a library, cluster related compounds, or perform spectral library searches.

Use when you have a collection of cleaned mass spectrometry spectra (in mzML, mzXML, msp, MGF, or JSON format) and need to identify spectral similarities, find related compounds, or generate a comprehensive similarity matrix for downstream analysis such as spectral library matching or clustering.

Use when you have converted mass spectrometry data in mzXML or mzML format and need to extract metabolic features via region-of-interest (ROI) search followed by preprocessing and augmentation.

Use when you have raw MS data in instrument-native or mzML format (Agilent .d, Thermo .raw, Bruker .d) and need to isolate specific analyte regions defined by precise m/z windows, RT windows (in seconds or minutes), and/or ion mobility arrival-time windows.

Use when when beginning an untargeted LC-MS analysis and either (1) the dataset characteristics (sample complexity, instrument platform, or polarity) differ from previously optimized cohorts, (2) multiple peak-picking algorithms (Centwave, FeatureFinderMetabo, ADAP) are available and their relative.

Use when after ISF features have been identified in Part 4 of the ISFrag workflow and you need to export or visualize the hierarchical structure of detected in-source fragments relative to their parent ions.

Use when after ISF features have been identified and annotated in Part 4 of the ISFrag workflow, and you need to represent the structural relationships among ISF features and their parent ions hierarchically.

Use when when you have applied biotransformation rules to generate candidate product structures from a set of input molecules (represented as SMILES strings) and need to document which product structures were derived from which parent structures.

Use when you have generated a TP object (from generateTPs) with structural information (SMILES) for predicted or library-derived transformation products, and you need to either (1) construct a MetFrag-compatible database that preserves parent compound records alongside TPs, or (2) componentize.

Use when after feature clustering has grouped LC-MS peaks by MS-DIAL peak character estimation, you need to reduce the cluster to a single representative feature per biological entity.

Use when you have evaluated a parametric denoising strategy (e.g., frequency-based filtering) at multiple threshold values and computed two or more competing metrics (e.g., signal loss and noise reduction) for each threshold.

Use when you have a log2-transformed, standardized peak intensity matrix (rows = metabolite features, columns = samples) with compound annotations mapped to curated pathway databases (KEGG, Reactome, or custom metabolite sets), and you need to rank pathways by their activity level while tolerating.

Use when when you have a metabolite intensity matrix (samples × metabolites) with compound annotations and need to score pathway activity levels for pathway enrichment analysis.

Use when when you have an untargeted metabolomics feature table (m/z values, retention times, intensity measurements, and p-values from statistical testing) and want to predict which metabolic pathways are active, but metabolite identification is incomplete, unreliable, or computationally expensive.

Use when you have a metabolomics dataset with metabolite identifiers in mixed formats (e.g., common names, KEGG accessions, HMDB IDs) and need to assign each metabolite to its canonical pathway(s) before performing pathway-level classification, feature selection, or prognosis modeling.

Use when apply ORA after conducting statistical tests (e.

Use when after peak annotation when you have: (1) a peak intensity matrix (rows=peaks with KEGG/ChEBI/UniProt IDs, columns=samples) with group labels; (2) a pathway database (KEGG, Reactome, or user-defined metabolite sets); (3) a comparative experimental design (case vs. control groups).

Use when you have a metabolite intensity matrix (samples × metabolites) or gene expression matrix (samples × genes) with corresponding pathway definitions (pathway IDs mapped to feature sets), and you need to rank pathways by activity level to identify which biological processes are most affected.

Use when when you have matchms-processed MS2 spectra from multiple samples and need to create comparable sample-level signatures for cross-sample analysis, particularly when samples are chemodiverse, have poor feature overlap, or exhibit strong retention time shifts across LC methods or MS.

Use when you have a tandem mass spectrum (MsmsSpectrum) from a known peptide and need to determine what fraction of observed peaks can be explained by expected fragment ions.

Use when you have two related compounds (a known reference and its structural analog with unknown modification site), baseline peak annotations from cosine alignment, and newly available structural information (SMILES or molecular structure) for the modified compound.

Use when when you have vendor-format GC-CI-MS or LC-MS data from stable isotope labeling experiments, a target list of compounds with known monoisotopic m/z, retention time, and elemental formula, and you need per-isotopologue area and intensity values for quantification or downstream statistical.

Use when after nontargeted peak detection has identified candidate peaks in LC-MS chromatograms, when you need to establish exact peak start/end retention times and extract peak-level metadata (intensity, width, shape) for downstream feature quality evaluation and annotation workflows.

Use when after extracting ion chromatograms and ion mobilograms from raw diaPASEF or DIA data within specified m/z, retention time, and ion mobility windows.

Use when after you have (1) extracted regions of interest (ROIs) around candidate peaks in LC-MS data and (2) run those ROIs through a trained CNN-Transformer peak detection model that outputs both binary peak classifications and bounding box coordinates.

Use when after training or loading a NeatMS neural network model, before applying it to filter false positive MS1 peaks in a new dataset.

Use when apply peak-count capping when preprocessing tandem mass spectrometry (MS/MS) spectra for peptide identification or spectral library matching, particularly when working with high-resolution spectra that may retain numerous low-intensity noise peaks after intensity filtering.

Use when implementing or modifying a writable MsBackend subclass (e.g., MsBackendMemory, MsBackendDataFrame) and need to replace peak data (m/z values, intensity values, or peaksData).

Use when you have raw IM-MS data in Agilent MassHunter (.d) or UIMF format from drift tube (DT) or SLIM instruments, and you intend to perform HRdm demultiplexing or peak deconvolution to resolve co-eluting or structurally similar ions.

Use when after generating simulated mzML output from ViMMS and you need to compare it against real acquisition data. Specifically, use it when you have paired real and simulated mzML files from the same sample (e.g., Beer1pos) and must evaluate whether a fragmentation strategy (e.

Use when when you have raw LC-MS/MS data files (.mzML, .raw, or vendor formats) from multiple samples and need to identify reproducible molecular features across the cohort before annotation or statistical analysis.

Use when after retention-time correction has been completed on centroided LC-MS data (mzML or mzXML format) and you need to identify all detected peaks as a unified feature table across a cohort of samples.

Use when you have raw mass-spectrometry data files (mzML, mzXML, or vendor formats) from untargeted metabolomics experiments and need to extract differential metabolic ion peaks for downstream statistical or annotation analysis.

Use when when processing raw untargeted LC/MS data in mzML or mzXML format and you need to detect peaks across mass-to-charge (m/z) and retention time (rt) dimensions without prior knowledge of optimal signal detection parameters, QC samples, or domain expertise in LC/MS preprocessing.

Use when after feature extraction from mzML/mzXML breath analysis data when you have a numerical array or dataframe of feature intensities across retention time or m/z dimensions and need to identify which features represent genuine volatile organic compound (VOC) signals rather than noise or.

Use when converting raw MS/MS spectra from library files (e.g., .msp format) into structured library entries, or when annotating experimental LC–MS features against fragment databases.

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).