Data & Analytics

holobiomicslab Skill

Use when after feature extraction from mzML/mzXML breath analysis data when you have a numerical array or dataframe of feature intensities across retention time or m/z dimensions and need to identify which features represent genuine volatile organic compound (VOC) signals rather than noise or.

holobiomicslab Skill

Use when you have generated or received a mass spectrometry data file in a structured format (e.g., mzPeak, Parquet-based archive) and need to verify it conforms to the published specification before use in analysis pipelines, sharing with collaborators, or publishing.

holobiomicslab Skill

Use when importing a new delimited data file (CSV, semicolon-separated, or other formats) into Punc'data that contains high-resolution mass spectrometry results.

holobiomicslab Skill

Use when when processing a multi-sample LC-MS metabolomics project after mass-track extraction and retention-time calibration have been applied to all individual samples, and you need to detect peaks across the entire cohort.

holobiomicslab Skill

Use when a Python library exposes functionality that depends on external packages (like sqlalchemy, pandas, or lxml) that are not required for core operations.

holobiomicslab Skill

Use when when designing or optimizing an MsBackend implementation (or similar columnar data structure) you must decide whether to pre-allocate all known columns in the backing DataFrame at initialization or defer column creation until first access.

holobiomicslab Skill

Use when you have LC-MS data from isotope labeling experiments where FAM measurements must be transformed to MDV values. Specifically, apply this when: (1) you have measured fractional abundances of isotopologues (FAM) in XLSX format from a high-resolution instrument (e.g., Orbitrap);

holobiomicslab Skill

Use when when you have MS1 mass spectrometry data from multiple samples (a cohort) acquired across an instrument run or batch, and you need to identify which samples deviate from cohort norms or which ion targets show anomalous behavior.

holobiomicslab Skill

Use when when migrating an existing file-based spectral library (stored as JSON, CSV, or binary formats) into a production system that requires frequent subset queries by metadata filters, similarity scoring across large spectral collections, or integration into downstream tools like MS2Query that.

holobiomicslab Skill

Use when you have a CSV-formatted target list with m/z, retention time, or ion mobility identifiers and need to locate and extract peak abundances from raw MS data files (Agilent .d, Thermo .raw, Bruker .d, mzML) acquired across LC-MS, LC-IMS-MS, DDA, DIA, or direct infusion modes.

holobiomicslab Skill

Use when after duplicate filtering of MZmine-exported MGF and CSV files, when you have combined spectra from multiple samples in a single MGF and need to segregate them by sample identifier before fragment annotation or adduct assignment.

holobiomicslab Skill

Use when you have a feature table (CSV with m/z and retention time columns) and corresponding .mzXML or .mzML mass spectrometry files from an LC-MS metabolomics experiment, and you need to filter out false or low-quality chromatographic peaks before downstream analysis.

holobiomicslab Skill

Use when you have extracted an MS1 feature table (from XCMS or custom CSV with m/z, retention time, and intensity columns) and one or more DDA mzXML files from the same or related LC-MS runs, and you need to annotate features with MS2 spectral data and match them against a standard spectral library.

holobiomicslab Skill

Use when after retention-time correction has been completed on centroided LC-MS data (mzML or mzXML format) and you need to identify all detected peaks as a unified feature table across a cohort of samples.

holobiomicslab Skill

Use when when you have raw LC-MS/MS data files (.mzML, .raw, or vendor formats) from multiple samples and need to identify reproducible molecular features across the cohort before annotation or statistical analysis.

holobiomicslab Skill

Use when you have loaded a numeric column (e.g., H/C ratio, O/C ratio, m/z value, or intensity) from a CSV file into Punc'data and need to render a histogram with appropriate bar spacing. The skill is triggered when the range of the column is small enough that default bin widths (1.

holobiomicslab Skill

Use when after mass track extraction from individual LC-MS samples, when you need to align mass tracks across a cohort to produce a unified feature matrix. Specifically: when study size is ≤10 samples, use pairwise anchor-prioritized alignment;

holobiomicslab Skill

Use when you have raw mzML files and feature tables (CSV from mzMine or XCMS) for LCMS data, have generated training/validation/test batches with known class imbalance, and need to train a CNN model from scratch to achieve AUC ROC > 0.9 for distinguishing true from false positive MS1 peaks.

holobiomicslab Skill

Use when you have peak/feature tables from one or more of MZmine, XCMS, MS-DIAL, or Compound Discoverer and need to ingest them into LipidMatch for lipid identification. The input files are in tabular format (CSV, TSV, or Excel) and their upstream tool origin may be unknown or mixed.

holobiomicslab Skill

Use when you have raw MS data in vendor formats (Agilent .d, Thermo .raw, Bruker .

holobiomicslab Skill

Use when you have a CSV file containing nucleoside or peptide molecular data (formulas, identifiers, retention times, intensities) that you want to simulate as LC-MS/MS runs. Use this skill as the mandatory first step before selecting a fragmentation model and noise injector in SMITER.

holobiomicslab Skill

Use when after loading spatial metabolomics data (from CSV, imzML, or merged positive/negative ion modes) into an AnnData object, and before filtering or alignment steps.

holobiomicslab Skill

Use when you have LC- or GC-HRMS data in mzML format and a feature list (CSV/TSL/Excel) from external feature detection software (e.

holobiomicslab Skill

Use when you have experimental fragment m/z values from HRMS/MS instruments (Q-Exactive orbitrap, Q-TOF) in CSV or mzML-derived peaklist format, and need to match them against a library of 500,000+ in-silico fragmented lipid species.

holobiomicslab Skill

Use when you have experimental fragment m/z peaklists from Q-Exactive orbitrap, Agilent Q-TOF, Bruker Q-TOF, or SCIEX Q-TOF UHPLC-HRMS/MS instruments (in CSV or mzML-derived table formats) and need to assign lipid identities using untargeted or targeted tandem MS data.

holobiomicslab Skill

Use when when you have feature lists (in CSV format) from two or more different MS acquisition methods (e.g., LC-MS vs. LC-IMS-MS), different processing software (e.

holobiomicslab Skill

Use when when initializing a SmartPeak session from a sequence file, you need to load and validate the workflow.csv and sequence.csv files that are co-located in the session directory.

holobiomicslab Skill

Use when you have a multi-polarity compound target list (e.g., a .xlsx file with a polarity or ionization mode column indicating positive or negative ESI mode) and you are about to perform targeted peak detection in a single LC–MS acquisition mode (e.g., positive-ion mode only).

holobiomicslab Skill

Use when after XCMS feature detection and alignment when you have a CSV-formatted feature table with m/z and retention time annotations and want to deduplicate isotopic peaks, adducts, and in-source fragments into compound-level clusters before molecular weight inference or spectral matching.

holobiomicslab Skill

Use when you have preprocessed MSI data (as a CSV intensity matrix or Cardinal MSProcessedImagingExperiment object) and suspect that observed peaks include both parent ions and their adducts formed with matrix or salt species.

holobiomicslab Skill

Use when when uploading a new mass spectrometry data file to Punc'data in CSV or delimited-text format, before attempting to map columns to their semantic roles (m/z value, intensity, formula).

holobiomicslab Skill

Use when you have a formula-assigned FT-ICR MS dataset (CSV or tab-delimited table with molecular formulas and mass values) and need to quantify the structural diversity, elemental stoichiometry patterns, or complexity landscape of the organic mixture.

holobiomicslab Skill

Use when you have a Pandas DataFrame containing mass spectrometry data (e.

holobiomicslab Skill

Use when you have a feature table and accompanying CSV metadata that includes a 'Sample Type' field (or equivalent) with entries such as 'BLANK', 'QC', 'STD', or 'Unknown'.

holobiomicslab Skill

Use when you have Nightingale Health 1H-NMR metabolomics measurements for a new cohort and wish to compute one or more established metabolic risk scores (mortality, MetaboAge, cardiovascular event, type-2 diabetes, COVID-19 severity) without recalibration.

holobiomicslab Skill

Use when you have received a formula-assigned FT-ICR MS dataset (CSV or tab-delimited table containing molecular formulas and mass values) and need to convert those formula strings into quantified elemental compositions before computing molecular descriptors, diversity indices, or transformation.