Claude Skills by HolobiomicsLab

Use when you have access to a published study that provides a Jupyter notebook (.ipynb) containing executable code for reproducing simulations, analyses, or figures, and you need to verify that the reported results can be regenerated in your own environment or adapt the code for a related analysis.

Use when when you have raw LC-MS/MS spectral data in .

Use when when fitting a multi-block PLS discriminant model on multi-assay LC-MS metabolomics data and you need to determine the number of latent variables to retain without overfitting.

Use when you have pre-trained Keras models from the NP-Classifier repository that must be deployed via TensorFlow Serving and need to expose standardized input/output layer names ('input_2048', 'input_4096', 'output') for integration with the classification API.

Use when when you have a training set of MS2 spectra with known chemical structures (e.

Use when after completing all per-sample annotation steps (molecular networking, ISDB/spectral matching, SIRIUS/CSI:FingerID, and compounds metadata enhancement with Wikidata IDs and NPClassifier ontology).

Use when you have (1) genomic data from a Streptomyces or other RiPP-producing organism in raw FASTA format or annotated GenBank format, (2) high-resolution LC-MS/MS spectra in centroided MGF, mzML, mzXML, or mzData format, and (3) a known or predicted lantibiotic core peptide sequence you wish to.

Use when you have multiple competing spectral similarity scoring methods (e.

Use when you have preprocessed mass spectra (peak-filtered, metadata-cleaned) in supported formats (mzML, mzXML, msp, MGF, JSON) and need to compare all-pairs or many-to-many spectrum similarity to identify related compounds, build spectral libraries, or perform large-scale library searching.

Use when you have raw LC-HRMS metabolomics data in .mzML or .abf format and need to perform peak detection, feature alignment, and metabolite annotation in a reproducible, containerized environment.

Use when you have LC-HRMS raw data files (.mzML or .abf format) from metabolomics experiments and need to extract, align, and annotate features in a reproducible manner across multiple computational environments.

Use when when you have statistically significant features from multi-assay LC-MS metabolomics data (with m/z and retention time annotations) and need to group features that may represent the same compound ionized as different adducts (e.g., [M+H]⁺ vs. [M+Na]⁺).

Use when you have raw mzML files and corresponding feature tables (CSV format, mzmine-formatted) from untargeted LCMS experiments, and you need to convert them into uniformly-shaped peak matrices (2 × 120 per peak: margin + signal regions) as input for neural network classification of MS1 peak.

Use when when beginning an untargeted LC-MS metabolomics study and need to assemble a cohort of mzML files for processing; particularly when establishing performance baselines across sample counts (10, 50, 100+ samples), validating reproducibility, or preparing data for publication.

Use when you have centroid mzML files from LC-MS experiments (converted from Thermo .raw or other vendor formats) and need to identify and quantify individual chemical features across multiple samples.

Use when after chromatographic peak detection on preprocessed LC-MS data, when you have hundreds or thousands of individual m/z × retention-time peaks and need to associate them into biologically meaningful feature groups.

Use when you have preprocessed LC-MS intensity data (e.

Use when after executing a Nextflow-based LC-HRMS metabolomics workflow with Docker or Singularity containerization on .mzML LC-MS data, before proceeding to downstream statistical or visualization analyses.

Use when after performing peak detection on centroided .mzML LC-MS data with screening_mode=FALSE in TARDIS.

Use when when you have a curated list of chemical compounds (real or virtual), a defined fragmentation strategy (e.g., Top-N, exclusion lists), and need to simulate how that strategy will acquire MS1 and MS2 scans over a defined retention-time window.

Use when you have raw LC-MS/MS spectral data in .mgf format (or vendor-specific raw data that can be converted to .mgf via MZmine or similar tools) and need to prepare it for interactive exploration using the specXplore dashboard.

Use when you have loaded mzML.gz or HDF5-formatted raw LC-IMS-MS/MS data and need to identify discrete peaks before feature alignment. Use it if your goal is to reduce noise, increase signal-to-noise ratio, and prepare multi-dimensional data for cross-sample feature matching and CCS calibration.

Use when you have raw LC-MS data (mzML or equivalent format) from a metabolomics experiment and need to extract a reproducible, quantified feature table with intensity measurements before conducting metabolite identification or statistical analysis.

Use when after loading centroided .mzML LC–MS runs and before executing full peak detection and integration.

Use when starting from raw LC-MS spectral files (mzML or mzXML format) in a global metabolomics study and you need to produce a complete, validated feature table with m/z, retention time, and intensity values across all samples.

Use when you have a preprocessed bag-of-fragments corpus derived from tandem mass spectrometry spectra and need to discover recurring fragmentation motifs without prior compound identification.

Use when you have a mass-spectrometry query string written in MassQL (or similar domain-specific SQL-inspired syntax) that must be converted into structured form for execution. The input is raw, unparsed text containing SQL keywords, MS-specific operators (e.

Use when when you need to reconstruct or validate the control-flow architecture of a spectral search system that must handle both exact-match library lookups and analogue discovery in a single pass, particularly when the system uses pre-computed embeddings for efficiency and machine learning for.

Use when you have raw .msp spectral library files (e.g., from MassBank or custom sources) and need to convert them into a structured CSV library format for use in metabolite annotation.

Use when you have custom lipid entries (e.g., synthetic lipids, rare natural variants, or isotopically labeled standards) not covered by LipidMatch's default in-silico library, and you want to include them as matching candidates in your UHPLC-HRMS/MS fragment m/z matching workflow without modifying.

Use when after applying mspcompiler pipeline transformation steps (e.g., reorganize_mona, assign_smiles, assign_ri, read_multilibs, separate_polarity, complete_mgf) to confirm the operation succeeded without data loss or structural corruption.

Use when your metabolomics experiment includes calibration line samples with known concentrations for spiked compounds, and you have computed batch-corrected compound/internal-standard ratios (ratio_corrected assay) and wish to convert relative ratios into absolute quantitative values for pathway.

Use when you have extracted retention times at peak maxima (rtFittedAPEX) from extracted-ion chromatograms (XICs) of known internal RT calibrants (e.

Use when you have peak-picked MS/MS data (e.g., from MZmine, XCMS, MS-DIAL, or Compound Discoverer) and need to identify lipid species present in your sample.

Use when when you have acquired a CCS reference library (such as DTCCSN2 for U13C labeled lipids) and need to verify that it contains the expected lipid classes, CCS values are physically plausible for ion mobility data, and coverage matches the library's advertised documentation before using it.

Use when you have IM-MS lipidomics data with measured CCS values, samples spiked with U13C-labeled lipid internal standards (e.

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 has generated a peaklist of experimental fragment m/z values (from Q-Exactive orbitrap, Agilent/Bruker/SCIEX Q-TOF UHPLC-HRMS/MS, or direct infusion/imaging experiments) and you need to compare those fragments against the LipidMatch in-silico fragmentation 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 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 you have access to a lipidomics library repository (e.g., LipidMatch .csv files) and need to audit or report the total number of distinct lipid species and lipid-type categories present.

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 when you have downloaded or cloned a lipidomics library repository (such as LipidMatch) and need to audit the breadth of lipid-type coverage to ensure the library meets minimum requirements for your analysis scope (e.g., ≥60 distinct lipid categories).

Use when when processing low-resolution GC-MS data in NetCDF format where you have already performed retention-index calibration and peak deconvolution, and you need to assign compound identities by comparing experimental mass spectra to a curated reference library such as PNNLMetV20191015.MSL.

Use when after mass track construction for individual samples, when you need to establish consensus m/z values across a cohort of LC-MS samples to build a unified feature table.

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 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 after generating mzPeak files from prototype implementations (Rust, Python, R, or .NET) or after format conversion, and before integrating files into a mass spectrometry data repository or sharing them with collaborators. Use it when specification compliance is a hard requirement (e.

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.