Product & Planning

holobiomicslab Skill

Use when you have GC–MS data from human breath samples and need to identify marker metabolites for disease diagnosis, phenotyping, or biomarker discovery without a predefined target list. Your data is noisy or conventional peak picking has produced high false-positive rates.

holobiomicslab Skill

Use when after running QC analysis on NMR or MS metabolomic data and obtaining per-feature CV values, use this skill to validate that the dataset meets FDA thresholds (CV < 0.30 for discovery, CV < 0.15 for quantification) and to report the proportion of features meeting each threshold.

holobiomicslab Skill

Use when you have generated candidate peptide-spectrum matches from a spectral library search (especially open modification searches using cascade strategies) and need to assign statistical confidence to those matches. Use it whenever the scoring metric (e.

holobiomicslab Skill

Use when you need to enumerate all supported metadata conversions in a plugin-based architecture without hard-coding converter names or method signatures.

holobiomicslab Skill

Use when using Casanovo for de novo peptide sequencing on high-stakes datasets (immunopeptidomics, paleoproteomics, or monoclonal antibody discovery) where missing the correct sequence in the top-1 prediction is costly.

holobiomicslab Skill

Use when you have a collection of tandem mass spectrometry spectra in mzML or similar format and need to prepare them for LDA-based motif discovery.

holobiomicslab Skill

Use when when you have executed database search pipelines (Dereplicator, VarQuest, or Dereplicator+) on centroided LC-MS/MS spectra in MGF format and obtained match results with associated p-values and false discovery rates.

holobiomicslab Skill

Use when when you have raw untargeted LC-MS metabolomics data and need to detect low-quality or mis-integrated peaks in an XCMS-processed xcmsSet object before performing metabolite annotation, statistical analysis, or biomarker discovery.

holobiomicslab Skill

Use when when building an extensible converter framework where new converter implementations (e.g., WebConverters or ComputeConverters for external chemical services) should be automatically discovered and registered without modifying a central registry.

holobiomicslab Skill

Use when when rescoring PSMs from a search engine with MS²Rescore and you need to (1) constrain computational cost by reducing the number of candidates fed to feature generators and rescoring engines, (2) control false discovery rate correctly by removing lower-ranking PSMs before final statistical.

holobiomicslab Skill

Use when you have selected two organisms whose metabolic networks are available in KEGG and you need to quantitatively assess their structural and functional similarity to identify shared or divergent metabolic capabilities for comparative systems biology, drug target discovery, or evolutionary.

holobiomicslab Skill

Use when you have a large spectral library and many query spectra to search against it, and you need to identify both unmodified and open-modification peptides with strict false discovery rate control.

holobiomicslab Skill

Use when you have KEGG metabolic data for one or more organisms and need to simultaneously analyze network topology and functional pathway organization—for example, when comparing metabolic capabilities between species for drug target discovery or when you need to expose both structural rewiring.

holobiomicslab Skill

Use when you have raw tandem mass spectrometry data in one or more of the vendor formats (MGF, mzML, or msp) and need to apply unsupervised topic modeling (LDA-based motif discovery) or comparative fragmentation analysis.

holobiomicslab Skill

Use when you have LC–MS/MS data processed through MZmine2 or MZmine3 and need to construct a feature quantification table for natural product discovery pipelines (e.g., INVENTA prioritization, GNPS networking, or metabolite annotation).

holobiomicslab Skill

Use when after LDA-based Mass2Motif discovery has generated a set of recurring fragmentation patterns (motifset_optimized.

holobiomicslab Skill

Use when you have a count matrix (genes × samples) from HTSeq, featureCounts, or transcript abundance quantification (Salmon, kallisto), a sample metadata table with experimental design, and you need to test for differential expression while controlling false discovery rate via independent.

holobiomicslab Skill

Use when when you have LC-MS data (mzML or netCDF format) and a pre-defined list of target metabolites (m/z, retention time, and identifiers) that you wish to extract and quantify across multiple samples, rather than performing untargeted feature discovery.

holobiomicslab Skill

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.

holobiomicslab Skill

Use when you have completed NMR data quality control analysis and possess per-feature CV values, and you need to formally assess whether the metabolomic dataset meets FDA regulatory standards for downstream biomarker discovery or quantitative assays.

holobiomicslab Skill

Use when you have raw GCxGC-MS data in NetCDF format that contains instrumental and chemical noise (baseline drift, high-frequency signal artifacts) and you need to prepare multiple preprocessed chromatogram objects for downstream multiway PCA or biomarker discovery.

holobiomicslab Skill

Use when processing IM-MS data files (Agilent .d or UIMF format) that contain high-abundance ions suspected of signal saturation, particularly in untargeted or discovery proteomics/metabolomics workflows where dynamic range compression would obscure quantitative relationships.

holobiomicslab Skill

Use when after obtaining in silico annotations from SIRIUS (Zodiac/Cosmic scores) or ISDB (cosine/shared peaks metrics), before using the annotation table for Feature Component calculation, chemical class assignment, or metabolite discovery prioritization.

holobiomicslab Skill

Use when when performing untargeted metabolomics annotation at scale and you need to estimate false discovery rates for candidate metabolite identifications.

holobiomicslab Bundle

Use when you want to discover shared substructures (Mass2Motifs) across a corpus of MS2 spectra via LDA topic modeling — convert MS/MS spectra into a bag-of-fragments document corpus, run MS2LDA (Latent Dirichlet Allocation) to infer recurring fragmentation motifs, annotate motifs against MotifDB / Spec2Vec embeddings, and map motifs back onto molecular-network features for substructure-level annotation of an untargeted LC-MS/MS dataset.

holobiomicslab Skill

Use when when you have preprocessed mass spectral data (normalized peak intensities or binned m/z representations) and need to discover latent spectral patterns to enhance neural network predictors without labeled spectral classes.

holobiomicslab Skill

Use when you have preprocessed single-cell ATAC-seq fragment files or count matrices and need to identify open chromatin regions (peaks) to support downstream differential accessibility analysis, motif discovery, or regulatory network inference.

holobiomicslab Skill

Use when when you have aligned and quantified mass spectrometry features from multiple natural extracts (via MZmine2/3), paired with in silico annotation results (ISDB or SIRIUS), and you need to prioritize samples for chemical discovery based on the proportion of unannotated, extract-specific.

holobiomicslab Skill

Use when when performing large-scale untargeted metabolomics annotations where you need to assess the false discovery rate of metabolite identifications but lack sufficient negative control samples or decoy compound databases.

holobiomicslab Skill

Use when you have preprocessed multi-class or time-course metabolomic peak tables (with or without quality control samples and/or internal standards) and need to compare multiple preprocessing workflows to identify which performs for biomarker discovery or differential profiling.

holobiomicslab Bundle

Use when you have a metabolomics feature/quant table and want a statistically rigorous comparison and candidate biomarkers — cleaning and normalization, multivariate analysis, differential features, enrichment/pathway analysis, and biomarker selection with ROC.

holobiomicslab Skill

Use when when building an automated converter discovery and job enumeration system where converter classes are dynamically loaded from package directories and you need to extract and validate their internal conversion method signatures without prior knowledge of which converters will be available.

holobiomicslab Skill

Use when you have raw MS/MS spectra in supported formats (.mgf, .mzML, or .msp) containing both chemical signal and experimental noise, prior to structural motif discovery via topic modeling.

holobiomicslab Skill

Use when you have raw FIA-MS full-scan data in mzML format and need to prepare it for untargeted metabolite discovery. Apply this skill when your goal is to detect and annotate unknown metabolites across a wide m/z range (e.

holobiomicslab Skill

Use when when you have preprocessed mass spectrometry ion images (single-channel 2D arrays or multi-channel spectral images) and need to train a self-supervised encoder to learn low-dimensional representations for downstream tasks such as co-localized ion discovery (COL mode) or isotope ion.

holobiomicslab Skill

Use when you have normalized and imputed metabolite abundance measurements (as a MultiAssayExperiment object or similar matrix) and need to construct a correlation network for co-expression module discovery. Use it specifically when outlier-robust similarity is required—i.