Claude Skills by HolobiomicsLab

Use when when you have mass spectrometry data in R's Spectra format but need to leverage Python libraries (matchms, spectrum_utils) for specialized operations like spectral similarity scoring, filtering, or normalization that lack native R implementations or perform better in Python.

Use when you have MS/MS spectral library data currently stored in multiple file formats (JSON, CSV, or binary) and need to enable fast, filtered queries by metadata (e.g., precursor m/z, retention time, molecular class) without loading entire libraries into memory.

Use when you have raw LC/MS data in mzML format and need to execute a complete non-targeted screening workflow to extract and annotate chemical features.

Use when you have raw mass spectrometry spectral data in common formats (MGF, MSP, mzML, mzXML, JSON) that requires standardized metadata cleaning, validation, and peak filtering before comparative analysis. Use this skill when you need to encode data quality constraints (e.

Use when you have preprocessed MS/MS spectral data (m/z and intensity arrays) and need to map it to a fixed-size latent vector for use in an encoder–decoder architecture. Specifically applicable when the downstream task requires a molecular structure reconstruction (e.

Use when when you have preprocessed molecular graph representations (from Preprocess.py or equivalent) and need to train a regression model to predict continuous retention time targets.

Use when when constructing a composite loss function for contrastive learning on structured data (e.

Use when when training a multi-component deep learning model where some components (e.g., a pretrained TCN spectrum encoder) should remain frozen while others (e.

Use when after implementing a transformer encoder backbone with masking mechanisms in PyTorch, before training on mass spectra data.

Use when when building a graph-based molecular property prediction model that must process both molecular structures (as heterogeneous graphs) and tabular metadata (chromatographic column parameters).

Use when after loading feature identification results (e.g., from OpenSwath or other DIA search engines) when you need to display only confident peptide precursors and their chromatograms.

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 processed LC-MS peak detection output or feature tables containing internal standard identifications with retention times, m/z values, and intensity measurements across multiple samples, and you need to visually monitor whether internal standards are within acceptable QC.

Use when when processing a batch of LC-MS samples in mzML or mzXML format where at least one file has been designated as a quality control (QC) file, extract its TIC or BPC before performing retention time correction.

Use when you have a QC-annotated LC-MS feature table (CSV or data frame format with sample metadata) and need to isolate QC replicate measurements prior to computing quality metrics such as D-Ratio or performing signal drift correction.

Use when you have a QC-annotated feature table (samples × features with QC sample identifiers) from LC-MS untargeted metabolomic profiling and observe systematic signal drift across the run sequence or between batch blocks.

Use when when loading multiple LC-MS runs (mzML files) into an MsExperiment object and the injection sequence contains an interleaved or documented pattern of QC and sample runs (e.g., two QC, four sample, two QC, four sample, two QC).

Use when after drift correction and before imputation when you have LC-MS data with designated QC samples and you need to remove features with poor reproducibility across QC replicates.

Use when after drift correction has been applied to your LC-MS peak table and you need to identify low-quality metabolic features that exhibit high internal spread (RSD, RSD*) or excessive QC-versus-biological variation (D-ratio) before imputation and batch correction.

Use when after applying one or more mpactr filters (filter_mispicked_ions, filter_group, filter_cv, filter_insource_ions) to a feature table, use qc_summary() to extract the pass/fail status of each ion across all applied filters.

Use when after applying one or more mpactr filters (filter_mispicked_ions, filter_group, filter_cv, filter_insource_ions) to an mpactr object, call qc_summary() to obtain a structured report of which ions passed or failed each filter.

Use when you have raw mass-spectrometry files (MGF, BIOM, mzXML, mzML) or feature abundance tables from external tools (MZmine2, peak detection software) and need to convert them into QIIME 2's typed artifact format before running downstream plugins like q2-qemistree, diversity analyses, or.

Use when you need to verify that a QIIME 2 artifact (e.g., a Chemical Feature Tree from q2-qemistree, a FeatureTable[Frequency], or a Phylogeny[Rooted] object) has been correctly produced, before using it as input to downstream analyses.

Use when you need to publish metabolomics spectra in static media (PDF, print, supplementary tables) and want readers or automated systems to access the corresponding interactive spectrum visualization without manual lookup.

Use when you have a feature quantification table exported from MZmine3 processing of non-targeted LC-MS/MS data that includes both biological samples and blank/control samples, and you need to identify and exclude features whose intensity is driven by contamination in blanks rather than true.

Use when after performing an inner or left join operation to combine a feature quantification table (from MZmine3) with sample metadata, and before proceeding to data cleanup, blank removal, batch correction, or statistical analyses.

Use when after feature integration and imputation when you have QC-annotated LC-MS feature intensity data (CSV or data frame format) with replicate QC samples.

Use when after composite-map peak detection has produced an unfiltered peak list with SNR, peakshape (goodness_fitting), peak_height, and prominence values.

Use when after completing doAnalysis on an mzQuality SummarizedExperiment object with outlier detection, batch correction, and compound reliability filtering applied.

Use when when importing a new batch of centroided mzML or mzXML LC-MS files into MetCohort, before any data alignment or feature detection is performed. At least one file must be designated as QC to enable ROA detection and alignment;

Use when when you have XCMS-preprocessed LC-MS metabolomics data with a peak table and an accompanying covariate/metadata file that contains a 'SampleType' column, and you plan to apply QC-based quality filters (such as RSD filtering) or train classifiers for peak quality assessment.

Use when after applying one or more mpactr filters (mispicked, group, CV, or insource) to a peak table in a chained filtering workflow.

Use when after executing Formation formatting on processed feature tables (output from Blueshift or Gravity modules).

Use when you have raw DIA mass spectrometry files (.raw, .d, or .wiff format) from timsTOF, TripleTOF, or Orbitrap instruments and need to assess their quality before downstream proteomics analysis.

Use when you have raw MS data in a supported instrument format (Agilent .d, Thermo .raw, Bruker .d, mzML) and a defined list of m/z, retention time, or other identifiers for which you need to extract and quantify peak abundances across one or more samples.

Use when when you have SMILES strings or molecular formulae for N-Me derivatized unsaturated sterol lipids and need to generate theoretical MS/MS spectra (predicted fragment m/z values and intensities) to compare against experimental LC-IM-MS/MS data before performing CCS prediction or downstream.

Use when when you have a collection of N-Me derived unsaturated sterol lipid identifiers or structures and need to generate predicted collision cross section (CCS) values for LC-IM-MS/MS analysis.

Use when when you have interpolated or optimized molecular geometries (from geodesic_interpolate or CREST conformer ensembles) and need to compute ab initio electronic energies, orbital properties, or transition state characteristics to populate a fragmentation reaction network or validate.

Use when after feature clustering has been applied to co-eluting LC-MS features and mass-to-charge ratio matching to KEGG has produced an annotated table with adduct assignments.

Use when after executing a MassQL query against mzML mass spectrometry files and obtaining a tabulated result DataFrame in memory.

Use when after executing MassQL queries against mzML mass spectrometry files when you need to organize heterogeneous scan-level results (MS1 and MS2 data) into consistent tabular schemas for batch analysis, statistical comparison across files/queries, or visual summary generation.

Use when after loading search result files (e.g., from DIA-NN or OpenSwath) containing feature identification results with associated Q-value scores, apply this filter when you need to select a subset of high-confidence identifications before generating comparison plots or summary statistics across.

Use when when converting raw metabolomics data (tab-delimited text files, Sciex OS exports) into a structured object for batch processing, or when you need to organize compound-level measurements (assays), sample metadata (colData), and feature annotations (rowData) in a single container that.

Use when you have untargeted LC/MS metabolomics data from stable isotope labeling experiments (e.g., 13C-glucose vs. 12C-glucose) already converted to mzXML format, and you need to systematically identify putatively incorporated metabolic features by comparing unlabeled and labeled sample groups.

Use when when you have extracted multiple spectral attributes (e.g., base-peak m/z, intensity, retention time, scan index) from individual MS scans via accessor functions and need to organize them into a single rectangular data frame for batch analysis, filtering, or export to external tools.

Use when when xcms has produced misaligned feature groups and you need to extract raw LC-MS profiles from source files into a structured format acceptable by ncGTW's realignment functions.

Use when after completing Part 4 (Identification of ISF Features) in the ISFrag workflow, when you have a feature table with identified ISF features and their hierarchical fragmentation relationships, and you need to export this relationship structure for interpretation, integration with external.

Use when when you have metabolomics data (tab-delimited text or SummarizedExperiment object) and need to apply batch correction, outlier detection, internal standard recommendation, and quality filtering at scale or in non-interactive workflows.

Use when you have a Spectra object in R and need to apply a specialized Python MS algorithm (e.

Use when when you have obtained an R-based bioinformatic program (such as DNMS2Purifier.