Claude Skills by HolobiomicsLab

Use when you have received raw CE-MS or LC-MS output files in vendor-specific formats from a mass spectrometry instrument and need to process them through an untargeted metabolomics workflow (e.g., AriumMS) that requires standardized, interoperable file formats.

Use when you have a cleaned and clustered set of LC-MS features (m/z, retention time, MS/MS spectra) from MS-CleanR output and need to assign putative compound identities by matching observed MS/MS fragmentation patterns against reference spectral libraries using HRR-based scoring.

Use when you have raw LC-MS/MS data in mzML or mzXML format and need to: (1) identify the top-abundance MS1 signals in an LC run, (2) compute a single scalar metric (separation efficiency) that summarizes how well compounds are resolved across the chromatogram, and (3) feed that metric into a.

Use when you have high-resolution MS1 data (mzXML, mzML, or netCDF format) from LC/HRMS analysis and need to deconvolve composite spectra into individual fragmentation patterns without DDA or DIA acquisition.

Use when after GNPS spectral library search has returned matched chemical annotations (with m/z values and cosine similarity scores) for MS/MS spectra.

Use when you have raw MS2 spectra files (mzML, mgf, msp, mzxml) that may contain multiple MS2 spectra per feature and require reduction or standardization before library matching.

Use when after frequency-based denoising of MS/MS spectra, when you need to validate that denoising improves metabolite identifications and quantify the trade-off between signal retention and annotation confidence. Specifically: (1) you have denoised MS/MS spectra from replicate measurements;

Use when you have DDA LC-MS/MS raw data (mzML format) with detected chromatographic peaks at a target m/z value and retention time window, and you need to build a high-confidence MS2 consensus spectrum for that peak to match against reference spectra (e.g., Metlin, GNPS).

Use when when you have DDA LC-MS/MS data (mzML format) with identified chromatographic peaks at a specific m/z (e.g., 304.1131) and multiple MS2 spectra fragmented from that precursor, and you need to produce a single high-confidence MS2 spectrum for comparison against reference databases (e.

Use when you have vendor-specific raw mass spectrometry data files (e.g., from Orbitrap, Q-TOF, or other MS instruments) and need to feed them into IsoFusion or other tools that require MS1 format as input.

Use when after MS-DIAL has completed feature detection and peak alignment on .mzML LC-HRMS data, producing an aligned feature table. Use MSFLO when you need to assign metabolite identities to detected features and filter results by significance criteria before downstream interpretation.

Use when you are converting a processed Cardinal MSImagingExperiment object (containing normalized peaks, optional spatial shrunken centroids segmentation, and feature m/z annotations) into a Seurat object for downstream pathway analysis, differential metabolite expression, or integrative.

Use when you are parsing mass spectrometry spectral library files in MSP format and need to guarantee that all spectrum records are either successfully integrated into the final dataset or explicitly logged with a reason for exclusion.

Use when you have untargeted metabolomics data from multiple MS instruments (e.

Use when you have a collection of tandem MS/MS samples (stored in MassIVE) with GNPS spectral library annotations (m/z, retention time, compound identity), and you want to explore whether samples cluster by shared chemical features without predefined class labels.

Use when when tabulating chemical annotation enrichment (e.g., GNPS spectral library matches) across sample groups stratified by metadata category (e.g., sample type, extraction method, ionization source), and the groups contain different numbers of files or samples.

Use when after implementing or modifying a numerical compression codec (such as MSNumpressCoder for m/z and intensity arrays in mass-spectrometry workflows) to verify that round-trip encode–decode cycles preserve numerical values within expected tolerance.

Use when you have raw CE-MS data and need to (1) transform migration time values to effective mobility using two calibration markers (e.

Use when when working with Bruker .d/.baf mass spectrometry imaging data and needing to feed it into MSIGen or other open-source MSI processing pipelines.

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 a feature-by-sample matrix (rows = annotated chemical features such as m/z, retention time, GNPS spectral library matches;

Use when when you have CE-MS raw data (mzML or netCDF format) with extracted ion traces for target compounds and need to identify peak boundaries and extract quantitative peak properties (retention time on µeff scale, peak intensity, peak area) within a specified mobility window.

Use when analyzing raw 2D MS data (m/z vs. retention time maps) where conventional peak picking introduces unacceptable error rates, particularly in untargeted metabolomics or chemometrics studies requiring sensitive marker identification at trace levels (e.g., parts per billion).

Use when when reconstructing a metabolite fragment library entry from raw MS/MS spectral data (e.g., from MassBank or local acquisition), you need to define peak-picking thresholds to separate true fragment ions from baseline noise and assign occurrence scores.

Use when after elution peaks have been detected on composite mass tracks using local maxima and prominence thresholds, and before mapping detected features back to individual samples or performing pre-annotation.

Use when you have extracted m/z and retention time (m/z-RT) information for peaks from untargeted LC/HRMS data (using tools like IDSL.IPA) and need to assign molecular formula identities to those peaks.

Use when rapid QC-MS is actively monitoring LC-MS data acquisition and a QC check result (e.g., internal standard retention time drift, m/z deviation, or intensity threshold breach) returns a fail status.

Use when you have preprocessed MSI data in Cardinal format (post-peakBin) and need to apply mass2adduct's adduct-detection workflow, OR you have exported MSI intensity data as CSV from third-party software (SCiLS, MSiReader) and must convert it into a standardized R object for downstream analysis.

Use when you have raw CE-MS or LC-MS instrument files (stored as OnDiskMSnExp objects or similar Bioconductor containers) and need to extract quantitative features (migration times, m/z values, peak intensities) by orchestrating multiple R packages in a controlled, documented sequence.

Use when you have detected and clustered unknown MS features from untargeted xenobiotic metabolomics data, computed fragmentation pattern similarity scores between features and reference spectra, and now need to systematically assign individual features to specific biotransformation reactions (e.

Use when when you have raw or preprocessed mass spectrometry data (feature matrices or transient files) acquired at lower mass resolution or with signal degradation, and you possess high-resolution reference MSI data or simulated ground truth to train a reconstruction model.

Use when when you have constructed a two-layer metabolite annotation network (knowledge-driven and data-driven) and need to propagate initial seed annotations (e.

Use when when processing Data Dependent Acquisition (DDA) raw mass spectrometry data (mzML or mzXML format) and you need to deconvolute fragmentation spectra by matching precursor ions to their corresponding fragment ions.

Use when you have a set of small-molecule compounds (e.g., from MS/MS library matching or database annotation) that require retention time validation or ranking to resolve ambiguous identifications.

Use when when you have generated embeddings for query and reference MS/MS spectra, computed a cosine similarity matrix between them, and need to evaluate how often the correct compound appears in the top-1, top-5, or top-10 retrieved candidates.

Use when you have raw or converted mass spectrometry data (CE-MS or LC-MS in mzXML or mzML format) and need to identify candidate metabolite regions before feature extraction.

Use when when you have a validated ReDU sample-information metadata file (gnps_metadata.

Use when you have imported raw MSI spectral data in imzML format and need to improve signal-to-noise ratio before performing mean intensity calculations, ROI analysis, or database annotation.

Use when you have a Nextflow workflow (e.g., Nextflow4MS-DIAL) that currently runs under Docker or bare metal, and you need to execute it on an HPC cluster that lacks Docker support or enforces Singularity as the container runtime.

Use when analyzing MALDI-mass spectrometry imaging data in which sodium or other alkali metal contamination is suspected, or when peak lists show unexplained mass differences in the range of ~20–25 Da (characteristic of Na adducts).

Use when after loading and preprocessing a Cardinal MSImagingExperiment object (with normalized peaks and optional spatial segmentation results), and before conducting spatial statistical tests or co-localization analyses.

Use when you have raw MS/MS feature data with m/z, retention time, and fragmentation spectra from an untargeted metabolomics experiment, and you need to annotate reaction-derived metabolites of xenobiotics without relying on a priori targeted methods.

Use when you have completed a ViMMS simulation run or processed real LC-MS/MS data and need to quantitatively assess whether one DDA controller (e.g., WeightedDEWController with exclusion) outperforms another (e.g., TopNController) in terms of spectral coverage and signal recovery.

Use when when you have MS2 .mzML format data files from untargeted metabolomics or proteomics experiments and need to perform an initial annotation step by matching experimental spectra against known reference libraries (GNPS, HMDB, MassBank) with a defined precursor mass tolerance (e.g., 15 ppm).

Use when you have received chemical annotations from GNPS spectral library matching and need to (1) assess annotation confidence and validity for downstream analysis, (2) understand why the same chemical may appear under multiple GNPS annotation IDs, or (3) decide whether to collapse or deduplicate.

Use when you have MS2 product-ion spectra in open formats (.mzML or .mzXML) from public mass spectrometry datasets (e.g., from MassIVE with a valid accession) and need to identify chemical compounds by comparing fragmentation patterns against the GNPS reference spectral library.

Use when when comparing two or more MSMS spectra and you need to emphasize structural relationships revealed by neutral losses (mass differences between precursor and fragment ions) rather than absolute m/z values.

Use when when you have pre-computed dense embeddings for query spectra (unknown compounds) and reference spectra (spectral library), and you need to rank library entries by similarity to each query for compound identification or structural similarity retrieval.

Use when after extracting and optionally combining MS2 spectra from a chromatographic peak (e.g., at a known m/z value like 304.1131), you need to determine which compound(s) in a reference library match the experimental spectrum.

Use when when you have extracted MS2 spectra from DDA chromatographic peaks and need to identify the originating compound by comparing against reference MS2 spectra (e.g., from Metlin or GNPS).