Integrations & APIs

holobiomicslab Skill

Use when you need to confirm that a software API or tool correctly implements a boolean control over an optional filter module, particularly in contexts where filter activation state directly affects the set of predicted metabolites.

holobiomicslab Skill

Use when when you have lipid names or abbreviations sourced from multiple databases (HMDB, LIPID MAPS, LipidHome, RefMet, SwissLipids) or software tools (LipidSearch, MS-DIAL, MZmine2, LipidBlast, etc.) and need to normalize them into a single canonical representation to enable data integration.

holobiomicslab Skill

Use when when analyzing rare-variant associations where sample sizes are modest, individual-variant tests lack power, or you need to aggregate signal across multiple rare variants within a biological pathway.

holobiomicslab Skill

Use when when you have raw MS files (e.g., .raw, vendor-specific formats) stored in a centralized repository and need to invoke a third-party proteomics analysis tool—such as a mass spectrometry feature detector or quantification engine—without manually managing file transfers or output.

holobiomicslab Skill

Use when you have a metabolomics feature table (rows=features, columns=samples) generated from LC-MS or GC-MS preprocessing and need to identify which features contain systematic errors from peak integration or alignment.

holobiomicslab Skill

Use when when you have separate quantification data (abundance matrix), sample metadata (phenotypes, treatment groups, experimental conditions), and spectral data (MS/MS fragmentation patterns or other spectral features) that must be combined for mass spectrometry-based metabolite analysis.

holobiomicslab Skill

Use when when you have a GNPS molecular networking job archive (downloaded as a .zip or compressed archive) and need to prepare metabolomics spectra and molecular family data for NPLinker integration.

holobiomicslab Skill

Use when you have raw mass spectrometry data in vendor-specific formats (Thermo RAW, Waters RAW, or open formats like mzML/jcamp) that need to be ingested, validated, and converted to a standardized representation for downstream peak detection, quantification, or integration with other NMR/IR/MS.

holobiomicslab Skill

Use when you have MS data in a new format or storage system (e.g., a custom database, HDF5 file, or proprietary raw file) and need to make it accessible to Spectra-based analysis workflows without forking the Spectra package itself.

holobiomicslab Skill

Use when you have a metadata table with raw, non-standardized taxonomy strings (e.g. misspellings, deprecated nomenclature, or aliases) in separate species, genus, and family columns, and you need to standardize them before performing taxon-dependent scoring (e.

holobiomicslab Skill

Use when when analyzing tandem MS/MS spectra with SIMILE V2 and you want to leverage both fragment ion mass differences and neutral loss patterns to improve spectral similarity scoring.

holobiomicslab Skill

Use when you have computed frequent fragmentation patterns from a collection of MS/MS spectra using mineMS2, and you want to focus pattern interpretation on subsets of spectra that form meaningful network components (connected groups, cliques, or high-similarity pairs) in a GNPS molecular network.

holobiomicslab Skill

Use when when you have a GNPS molecular networking task ID and need to fetch the job archive, decompose it into standard metabolomics file formats (spectra.mgf, molecular_families.tsv, annotations.tsv, file_mappings), and prepare them for integration with genomics data.

holobiomicslab Skill

Use when after cluster-based filtering has produced a set of candidate KEGG compounds for each feature cluster in untargeted LC-MS data, and you need to rank these candidates by their metabolic plausibility using network context rather than mass accuracy alone.

holobiomicslab Skill

Use when designing a metabolite annotation workflow that must simultaneously leverage established biochemical knowledge (pathway databases, reaction networks) and experimental evidence (mass spectrometry feature similarity, co-occurrence patterns).

holobiomicslab Skill

Use when when you have an aligned MemoMatrix (sample-by-feature occurrence matrix where features are MS2 peaks and neutral losses) and corresponding sample annotations (especially blank/control sample labels), and you need to exclude background-derived peaks and losses before applying visualization.

holobiomicslab Skill

Use when you have DIA mass spectrometry raw files from multiple instrument types (timsTOF, TripleTOF, Orbitrap) in their native formats (.raw, .d, .

holobiomicslab Skill

Use when you have an experimental MS/MS spectrum (m/z and intensity pairs with known precursor m/z) and need to identify the compound by searching against public repositories or a local reference library.

holobiomicslab Skill

Use when you have search result files from one or more DIA-MS analysis tools and need to load them into a unified environment for Q-value filtering, cross-tool comparison (upset plots), and interactive visualization of identifications, quantifications, and coefficient of variation metrics across.

holobiomicslab Skill

Use when you have structural candidates from in silico tools (SIRIUS/CANOPUS) and library spectral matches from GNPS, but need to resolve conflicting or incomplete chemical classifications into a unified consensus.

holobiomicslab Skill

Use when you have centroid-mode LC–MS all-ion fragmentation (AIF) data already processed through xcms for feature detection and retention-time correction, and a corresponding RamClustR object that groups co-eluting fragment ions into putative spectral clusters.

holobiomicslab Skill

Use when after MS2LDA has inferred a motifset and you need to visualize and export the relationships between discovered Mass2Motifs for post-processing exploration, comparative annotation, or integration with external tools. Use this skill when you have motifset.json or motifset_optimized.

holobiomicslab Skill

Use when when you have processed Cardinal MSI data (normalized peak intensities, optional SSC segmentation results) and need to transition to Seurat-based workflows for differential expression, pathway analysis, or integration with spatial transcriptomics data.

holobiomicslab Skill

Use when when you have scored a set of potential gene cluster family (GCF)–molecular feature (MF) links using two or more orthogonal methods (e.

holobiomicslab Skill

Use when after batch effect removal and sample integration, when you have a normalized feature-by-sample abundance matrix (finalData) with corresponding sample group labels (finalLabel), and need to identify which metabolites discriminate between biological conditions or phenotypes for focused.

holobiomicslab Skill

Use when you need to construct a reference metabolomics database from scratch or when existing public databases (HMDB, MassBank, METLIN) need to be merged into a single queryable resource for metabolite annotation in untargeted mass spectrometry analysis.

holobiomicslab Skill

Use when you have .msp spectrum files with minimal metadata (e.g., only compound name and mass) and need to augment them with chemical structure descriptors, identifiers, and properties from external databases.

holobiomicslab Skill

Use when you are building or refactoring a scientific Python library and need to decide how to organize and expose utility functions (e.g., adaptive coarse-graining, filtering, analysis routines) so that end users can import and call them reliably.

holobiomicslab Skill

Use when you have mass spectrometry data stored in a non-standard format (SQLite database, custom indexed gzip files, or other database backends) and want to enable pymzML's Reader to access it with both random-access by spectrum ID and sequential iteration capabilities, avoiding the need to.

holobiomicslab Skill

Use when you have multiple mzML or HDF5 feature tables from the same study acquired on the same or similar instruments and need to align feature coordinates across samples to correct for systematic shifts in mass-to-charge, drift time, or retention time caused by instrumental drift, column aging.

holobiomicslab Skill

Use when when you have deposited a collection of JSON project documents in a platform or repository and need to verify that all conform to a published JSON Schema specification before publication, distribution, or integration with downstream systems.

holobiomicslab Skill

Use when you have paired cdf files (raw mass spectrometry imaging data) and Matlab workspace (.mat) files for the same root sample, and you need to reproduce published linear-axis imaging analysis results (e.g., per-root mass spectrometry imaging metrics along a developmental or spatial axis).

holobiomicslab Skill

Use when after peak integration and feature alignment in metabolomic processing, when you have a feature table (rows=features, columns=samples) and need to identify which features are corrupted by processing artifacts (faulty peak integration, feature misalignment).

holobiomicslab Skill

Use when you have completed statistical analysis of omics data (proteomics, metabolomics, transcriptomics, or multi-omic) and possess both abundance/expression measurements and computed statistical metrics (p-values, effect sizes, fold-changes).

holobiomicslab Skill

Use when when you have paired spatial transcriptome and metabolome datasets with spot-based coordinates that need to be aligned for multi-modal integration.

holobiomicslab Skill

Use when after peak detection and clustering have been completed on aligned and baseline-corrected GC-IMS data, and before imputation or statistical analysis.