Claude Skills by HolobiomicsLab

Use when you have a chromVARDeviations object with precomputed bias-corrected deviations and z-scores for multiple annotation sets (e.

Use when after bias-corrected ATAC-seq signal tracks (bigWig files) have been generated and you need to quantify transcription factor binding strength within open chromatin regions.

Use when you have ATAC-seq BAM files and want to detect transcription factor footprints—characteristic depletion patterns of Tn5 insertions around protein-bound motif sites.

Use when you have corrected ATAC-seq footprint scores (from ATACorrect and ScoreBigwig) at open chromatin regions and a motif database (e.g., JASPAR PWMs), and you need to determine which TF motif matches are actually occupied across one or more experimental conditions.

Use when you have computed or received a precomputed expected contact frequency table (e.

Use when when you have computed deviation and variability scores using chromVAR for two or more discrete parameter configurations (e.g., 6-mer vs 7-mer kmers, or different motif databases) and need to determine which parameter set produces stronger or more discriminative variability signals.

Use when you have a sparse chromatin accessibility matrix (ATAC-seq or DNAse-seq counts per peak per sample), matched peak-annotation assignments (e.

Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.

Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.

Use when adopting a mass spectrometry-based analysis tool (e.

Use when you have preprocessed metabolite intensity data (log2-transformed, zero-mean unit-variance standardized) mapped to compound annotations, and you need to derive activity scores for a set of metabolite groups (pathways, Molecular Families, Mass2Motifs, or custom metabolite sets) to rank them.

Use when after computing PLAGE-derived activity scores for pathways or metabolite sets (Molecular Families, Mass2Motifs) from log2-standardized metabolomics intensity data.

Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).

Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.

Use when when you have binned mass spectrometry imaging peaks and want to understand which detected mass-to-charge ratios represent the same metabolite in different ionization states (parent vs. adduct form).

Use when when you have a metabolite SMILES structure and need to predict which adduct ions will appear in a mass spectrum acquired with a chemical derivatizing matrix.

Use when you have computed a histogram of pairwise mass differences from MS imaging data and need to (1) identify which observed mass differences correspond to biologically relevant or chemically known adducts, or (2) rank the most frequently observed mass differences to discover dominant adduct.

Use when after identifying statistically significant LC-MS features (e.g. via MB-VIP permutation testing) when you need to consolidate redundant measurements of the same metabolite arising from different ionisation adducts (e.g. [M+H]+, [M+Na]+, [M−H]−).

Use when when you have access to annotated MS/MS spectra from a specific ionization mode (e.g., negative ESI) or adduct class (e.

Use when you have vendor mass spectrometry raw files (e.g., .raw, .d, .

Use when when you need to evaluate how a specific algorithm parameter (such as SearchMolecularFormulas first_hit mode) affects the quantity and quality of molecular formula assignments on a given spectrum or dataset.

Use when you are evaluating a new or existing data analysis pipeline (e.g., MetaboDirect) and need to produce a transparent, evidence-based feature matrix showing which analyses it supports—particularly when the pipeline is positioned as an alternative to or improvement over established tools.

Use when after executing an end-to-end structure annotation pipeline (such as BAM) on a validation dataset with known reference annotations.

Use when you have pseudo-MS/MS spectra from LC-MS all-ion fragmentation (AIF) data that have been matched against one or more ion fragment databases (e.g., LipidPos, MassBank), generating multiple candidate annotations per feature.

Use when you have run MSMetaEnhancer's annotate_spectra() method on a .

Use when after completing metabolite annotation of LC-MS AIF features using the annotateRC function, when you need to persist ranked candidate matches, inspect multiple candidate annotations per feature, visualize matched ions in ranked spectra, or export pseudo-MS/MS spectra for external analysis.

Use when integrating with an external API (such as TensorFlow Serving) where changes to the response schema could break dependent code, or when model metadata must be extracted and verified before being used in downstream analysis steps.

Use when you need to obtain all project JSON documents currently deposited in a data platform (such as the Paired Omics Data Platform) to validate their structure against a JSON Schema specification, or when you require a complete snapshot of published records for quality assurance, data migration.

Use when you have fingerprint or spectrum data that requires compound-class annotation but prefer not to run SIRIUS locally, or need to integrate predictions into an automated analysis pipeline.

Use when when annotating .msp files with metadata from multiple external web services and you need to monitor which services are slow or unreliable.

Use when after submitting a POST request to the /api/smart3/search endpoint with peak data as a JSON payload, you receive an HTTP response and need to extract classification predictions and confidence scores.

Use when you have access to the source code of a webservice component (Python, configuration files, route definitions) and need to produce machine-readable API documentation (OpenAPI 3.

Use when when you have raw TWIM-MS arrival-time data and need to transform it into absolute CCS values for downstream biomolecular class assignment or comparative analysis.

Use when you have loaded mzPeak spectrum or chromatogram metadata and signal data into PyArrow Table structures (via the Python mzPeak reader or equivalent) and need to persist them to disk in Parquet format for downstream analysis, interoperability with other languages (R, .

Use when when you have Parquet-encoded tabular data (e.

Use when when reproducing a prior software release (especially one generated by automated versioning tools like Semantic Release), you need to confirm that the artifacts produced in your environment match the original release byte-for-byte.

Use when you have built multiple Docker image variants (e.g., cli, dev, linux, windows) from a multi-stage Dockerfile and need to verify that their uncompressed and compressed storage footprints fall within documented acceptable ranges (e.

Use when after calling buildExperiment on metabolomics data to construct a SummarizedExperiment object, before performing batch correction, outlier detection, or other QC calculations that depend on ratio assay values.

Use when when you need to validate that a .NET assembly (such as ThermoFisher.CommonCore.RawFileReader) is correctly installed and accessible before attempting data reading operations.

Use when you have a batch of mass spectra records in .msp format that lack standardized metadata fields (SMILES, InChI, CAS numbers, molecular formula, IUPAC names) and need to populate them by querying multiple independent web APIs in parallel.

Use when building a metadata enrichment system that must support multiple pluggable converter backends and you need to automatically discover all available converters at runtime, extract their conversion specifications, and generate Job tuples that can be dispatched to an async annotation engine.

Use when your workflow fetches data from multiple external web services (e.g., CIR, CTS, PubChem, IDSM, BridgeDb) asynchronously and you need to track which services are available, how often they fail, and their response latencies during a long-running annotation job.

Use when you have a trained GNN model for molecular property prediction (such as CCS) and need to understand which atomic and bond features are most influential in driving predictions.

Use when you have canonicalized SMILES strings from a chemical database (e.

Use when after instantiating a transformer encoder module for mass spectrometry data processing (e.g., in IDSL_MINT), before training on large MS/MS datasets or running inference on test spectra.

Use when you have mass spectrometry data loaded into a pandas DataFrame with m/z, retention time, and intensity columns, and need to confirm that pyOpenMS-Viz can produce visualizations (spectra, chromatograms, or peak maps) using a specific plotting backend (matplotlib, Bokeh, or Plotly) on real.

Use when when you have execution time data for visualization scripts across multiple backends (matplotlib, Bokeh, Plotly) and need to determine which backend offers the fastest median performance for specific mass spectrometry plot types (chromatogram, mobilogram, peakmap, peakmap-marginals.

Use when you are designing or optimizing an MsBackend implementation and need to decide whether to pre-populate the @spectraVars slot with all core spectra variable columns (mz, intensity, rtime, scanIndex, precursorMz, precursorIntensity, acquisitionNum, msLevel) at initialization time, or defer.

Use when you need to support multiple plotting backends for the same data visualization task, and you want to centralize backend selection logic so that users can specify their preferred rendering engine (matplotlib, bokeh, or plotly) at call time without modifying the core plotting logic.

Use when when predicting one data modality (e.g., metabolite abundances) from another (e.g., microbiome composition) and you need to distinguish genuine microbe–metabolite associations from false positives driven by data compositionality, sample size artifacts, or confounding variation.