Claude Skills by HolobiomicsLab

Use when you have draft metabolic reconstructions in SBML or standard format for multiple organisms in a microbial community (e.

Use when you have a generic constraint-based metabolic model (SBML format) and cross-sectional omics data (RNA-seq, intracellular metabolomics, YSI or bioanalyzer extracellular flux measurements) for multiple biological samples and need to create sample-specific models that discriminate whether.

Use when you have generated consensus metabolic reconstructions for multiple members of a microbial community (e.

Use when you have an untargeted metabolomics feature table (with m/z, retention time, and statistical significance values) and want to predict which metabolic pathways and functional modules are active in your sample, but you lack confident metabolite identifications or wish to bypass the.

Use when when you have computed Reaction Activity Scores (RAS) from transcriptomics and GPR rules, Reaction Presence Scores (RPS) from RAS normalized flux predictions, and Flux Fold-change Distributions (FFD) from metabolomic data and mass-action constraints across multiple sample pairs, and you.

Use when you have intracellular metabolomics abundance data (measured metabolite concentrations) from multiple biological replicates collected from two or more cell lines or conditions, and you need to create a normalized, cell-line-level metabolite dataset before computing Reaction Propensity.

Use when you have normalized peak intensities using MetaboDirect's data preprocessing step and are preparing to perform PERMANOVA or NMDS ordination on a bacterium-phage or environmental metabolomics dataset (36+ samples).

Use when you have a peak-abundance matrix from FT-ICR MS (peaks as rows, samples as columns with raw peak intensities) and need to compute abundance-based diversity indices or functional diversity metrics that are sensitive to relative vs. absolute peak heights.

Use when after running the annotateRC function on LC-MS All-ion fragmentation (AIF) features and obtaining a populated annotations object with ranked candidate matches, use this skill when you need to persist results to disk for archival, sharing, or downstream interpretation (e.

Use when you have paired metabolomics data (MS/MS spectra and feature quantification) linked to organismal or tissue taxonomy, and you want to reduce false positive annotations and improve annotation rank by filtering candidate metabolites to those chemically plausible within the given taxon.

Use when when preparing to run Over-representation Analysis (ORA) on metabolomics pathway data, after you have loaded both a metabolomics pathway database (e.g., KEGG, MetExplore) and an experimental detection list (metabolites measured in your study).

Use when after running annotateRC() on LC-MS AIF features, when you need to validate whether a feature's rank-1 annotation is reliable or when you suspect that structurally similar metabolites (e.

Use when you have a query mass spectrum matched to multiple candidate metabolites (by accurate mass, database lookup, or spectral similarity), and you possess or can train a DNN model for retention time prediction on your target chromatographic method.

Use when you have peak-abundance data (after molecular formula assignment, peak filtering by m/z, isotope, ppm error, and sample presence thresholds) and you need to quantify and compare the molecular composition diversity across samples or conditions.

Use when you have intracellular metabolomics abundance data (absolute or relative concentrations) for multiple cell lines or conditions, a constraint-based stoichiometric metabolic model with reaction-metabolite associations, and you need to disentangle how differences in substrate concentration.

Use when designing or validating a metabolomics pathway analysis experiment, especially when you have uncertainty about how many metabolites your detection platform will reliably measure relative to a pathway database. Use it if you want to understand whether your expected metabolite coverage (e.

Use when you have simulated or experimental mzML data from two or more fragmentation controllers (e.

Use when after selecting statistically significant features from multi-assay LC-MS metabolomics datasets (e.g., via MB-VIP and permutation testing with p < 0.01).

Use when you have two independent LC-MS untargeted metabolomic feature tables (e.

Use when immediately after executing the MetaboAnalystR 4.0 unified LC-MS workflow (feature detection and quantification module) on raw mzML or netCDF data.

Use when when you have access to the MAGMa source code and need to understand or audit how in silico metabolite candidates are enumerated from parent structures.

Use when you have extended a metabolite identification tool (such as Met-ID) to support a new derivatizing matrix beyond the default (e.

Use when you have (1) peak-picked LC-MS AIF features in a feature table with m/z and retention time, (2) corresponding xcmsSet and RAMClustR pseudo-MS/MS spectral objects from centroid-mode raw data, and (3) a research goal to identify which features are lipids rather than other metabolite classes.

Use when after training a neural network or regression model to predict metabolomic profiles from microbiome data.

Use when after feature extraction (Asari) has produced a full feature table from mzML data, but before normalization and annotation.

Use when you have executed mzExacto() on a preprocessed GC-MS dataset and need to verify that the returned dataframe correctly matches query chemicals to their m/z peaks, retention times, and quantitative measurements (area values).

Use when your input is a SummarizedExperiment containing multiple batches or injection sequences of metabolomics samples (study samples, QC replicates, calibration lines) with measured ion areas for compounds and assigned internal standards.

Use when you have log2-transformed, standardized peak intensity matrices with metabolite annotations (peak ID → KEGG/ChEBI IDs) and need to test whether groups of peaks co-vary systematically within known pathways or spectral groupings.

Use when you have a metabolite intensity matrix (rows=metabolites or peaks, columns=samples) paired with metabolite-to-pathway or metabolite-to-feature-group annotations, and you want to score activity levels across pathways or metabolite groupings in a way that tolerates missing peaks and.

Use when after constructing a background set for ORA in metabolomics: you have loaded an experimental detection list and a metabolomics pathway database, applied background-set construction logic, and need to confirm that the resulting background set has the correct size, composition, pathway.

Use when you have peak intensity data from metabolomics experiments with annotated metabolites assigned to known groupings (KEGG pathways, Reactome, GNPS Molecular Families, or MS2LDA Mass2Motifs) and need to identify which metabolite sets change significantly across experimental comparisons while.

Use when when a user has prepared a custom collection of metabolite sets (e.g., from spectral fragmentation clustering, literature curation, or domain-specific grouping) in CSV or JSON format and wants to score their activity levels using PALS without modifying the core PALS codebase.

Use when you have two or more mass spectral libraries in different formats (NIST binary exports converted to MSP, MoNA downloads, RIKEN public databases, GNPS MGF, or batches of in-house standards in separate MSP files) and need to combine them with consistent metadata (SMILES, InChIKey, molecular.

Use when after statistical analysis (e.g., MB-PLS with permutation testing) has identified a subset of significant LC-MS features (p < 0.05 or similar threshold) that require structural interpretation.

Use when you have a set of candidate transformed structures generated by biotransformation rules (e.

Use when you have a raw LC–MS compound metadata file (xlsx or csv) with heterogeneous column names and column order, and you need to prepare it for targeted peak detection in TARDIS.

Use when you have genome FASTA or annotated genome files (antiSMASH .gbk, BOA .annotated.txt) and wish to discover ribosomally synthesized and post-translationally modified peptides (RiPPs) by integrating genomic and mass spectrometry data.

Use when after feature detection and peak alignment have produced a feature table with zero or missing values across samples.

Use when when you have preprocessed LC-MS/MS data (MGF file with MS1 and MS2 spectra and a feature abundance table from MZmine2 or similar peak detection tool) and need to perform chemical phylogeny-based diversity analyses or meta-analyses comparing metabolomic profiles across multiple samples or.

Use when when you have raw or GNPS-processed MS2 spectral data from microbial strains and need to organize spectra into molecular families (grouped by spectral similarity) while preserving strain provenance, as a prerequisite for linking metabolomic families to gene cluster families (GCFs) via.

Use when you need to create realistic, diverse chemical populations for simulating LC-MS/MS acquisition strategies in a virtual environment. It is essential when you lack real metabolomics data but want to prototype and compare fragmentation strategies (e.

Use when you have raw LC-MS data in mzML or equivalent binary format from a public repository (MetaboLights, MassIVE) or instrument vendor output, and need to ingest it into MetaboAnalystR 4.0 for unified LC-MS1 feature detection and MS/MS spectra processing.

Use when when importing a tab-delimited or Sciex OS text export metabolomics dataset into mzQuality, before building the SummarizedExperiment object.

Use when you have measured intracellular metabolite concentrations (e.g., via LC–MS/MS) across multiple cell lines or samples and want to predict which metabolic reactions are substrate-limited versus transcriptionally regulated.

Use when after completing feature annotation with the annotateRC function on LC–MS All-ion fragmentation (AIF) datasets, when you need to persist ranked metabolite candidates, matched ion spectra, and global summary tables to disk for archival, manual review, or integration into downstream.

Use when after consolidating aligned LC-MS peaks into a quantitative feature table (with m/z, retention time, and intensity values across all samples), and before proceeding to statistical analysis or functional interpretation.

Use when you have a Sciex Multiquant (≥v3.0.3) txt export containing QCpool sample measurements at multiple timepoints within a sequence, and you need to flag compounds with high technical variability or signal degradation before proceeding to statistical analysis or interpretation of.

Use when when beginning an LC-MS/MS metabolomics analysis pipeline and you have preprocessed xcms result objects (XcmsExperiment or legacy xcmsSet) that need to be loaded into memory, validated for integrity, and prepared for feature grouping (e.

Use when after you have detected LC-MS features, grouped them into empirical compounds via isotope and adduct clustering (using khipu), and have accurate m/z and retention time values.

Use when you have fitted an MB-PLS model on multi-assay LC-MS intensity data (e.g., HPOS, LPOS, LNEG), computed MB-VIP scores for all features, and need to identify which features are statistically significant for your phenotypic outcome.