Data & Analytics

holobiomicslab Skill

Use when you have raw tabular experimental data (CSV or Excel) with column headers annotated using MESSES tag syntax (#<table_name>.id for record identifiers, #.

holobiomicslab Skill

Use when you have tabular omics data (expression matrices, p-values, fold changes, functional annotations, or clinical outcomes) in CSV format and need to generate publication-ready visualizations that reveal distributional patterns, statistical significance thresholds, group comparisons, or.

holobiomicslab Skill

Use when you have preprocessed MSI data in Cardinal format (post-peakBin) and need to apply mass2adduct's adduct-detection workflow, OR you have exported MSI intensity data as CSV from third-party software (SCiLS, MSiReader) and must convert it into a standardized R object for downstream analysis.

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 you have NMR-based metabolomics measurements from a cohort containing both plasma and serum samples with associated processing delay metadata (pre- and post-centrifugation times), and you need to determine whether metabolic parameter stability differs between the two sample types or to.

holobiomicslab Skill

Use when you have an mzPeak file (uncompressed ZIP archive containing Parquet files) and need to extract and work with spectrum metadata (scan descriptions, precursors, selected ions), spectrum signal data (profile or centroid m/z and intensity arrays), or chromatogram data.

holobiomicslab Skill

Use when when you have multiple lipid structures (from lipidomics data in CSV, XLSX, or mzTab-M format) and need to compute pairwise structural distances, identify lipids responsible for shaping a lipidome, or perform hierarchical clustering of lipidomes.

holobiomicslab Skill

Use when you have received raw lipid identification output from LipidSearch or LIQUID in CSV or TSV format containing lipid species names or identifiers, and you need to extract their structural components and map them to standardized LIPID MAPS categories before building a quantitative data matrix.

holobiomicslab Skill

Use when when you have mass spectrometry data stored in mzPeak format (ZIP archive containing Parquet files) and need to read spectrum metadata, chromatogram metadata, and signal data (m/z and intensity arrays) for analysis. Use this skill if your input is an .

holobiomicslab Skill

Use when you have two or more CSV feature tables from independent metabolomic experiments (each with RT, m/z, intensity, isotope, and adduct columns), and you need to merge them into a single aligned feature matrix for downstream batch effect removal, marker identification, or pathway analysis.

holobiomicslab Skill

Use when when constructing a sample list from an Excel template for LC/GC-MS analysis, you must classify each QC sample by type before proceeding to plate layout and randomization steps.

holobiomicslab Skill

Use when after constructing MetaboSet objects from Excel-formatted LC-MS peak tables and before drift correction or quality flagging.

holobiomicslab Skill

Use when you have feature tables from external metabolomics software (MS-DIAL, XCMS, vendor tools) in CSV format and need to integrate them into JPA for cross-sample alignment and metabolite annotation.

holobiomicslab Skill

Use when you have MS1 data from multiple samples and need to assess whether particular ion targets (e.g., internal standards, biomarkers, or metabolites of interest) maintain consistent quality across the cohort. Trigger on: (1) suspicion of sample-to-sample variability in ion signal;

holobiomicslab Skill

Use when when you have read LC-MS peak table data from Excel (or equivalent) into R and need to organize it into a structured object that tracks feature abundances, sample information (injection order, QC status), and feature metadata (mass, retention time, Feature_ID) simultaneously.

holobiomicslab Skill

Use when you have multiple CSV files containing feature-by-sample matrices from different analytical experiments or batches, each with mass, retention time, intensity, isotope, and adduct information across different samples.

holobiomicslab Skill

Use when after running a comprehensive preprocessing workflow comparison (e.g., via normulticlassqcall or nortimecoursenoall) that produces an overall ranking CSV file of candidate workflows.

holobiomicslab Skill

Use when when you have raw outputs from LipidSearch or LIQUID identification software (CSV or TSV format with vendor-specific column naming and lipid identifiers) and need to construct a structured data matrix suitable for batch normalization, statistical testing, or visualization.

holobiomicslab Skill

Use when you have mass-spectrometry data in a Pandas DataFrame and need to expose plot kinds (spectrum, chromatogram, mobilogram, peakmap) as a `.plot(kind='...

holobiomicslab Skill

Use when you have mzPeak files (Parquet-based archives containing mass spectrometry spectra and chromatogram data) that you want to analyze in R, and you need to convert the Arrow columnar representation into native R objects that can be passed to downstream analysis functions (e.

holobiomicslab Skill

Use when when you have a list of polypeptide sequences (plain text, CSV, or FASTA format) and need to compute their monoisotopic mass and average mass for downstream mass spectrometry interpretation, database matching, or physico-chemical property annotation.

holobiomicslab Skill

Use when when processing MS spectral data from multiple open mass spectra libraries (OMSLs) in mixed formats (MSP, MGF, JSON, CSV), especially when source data exhibits missing fields, malformed entries, inconsistent adduct representations, or non-standard format variants that may cause silent.

holobiomicslab Skill

Use when you have an experimental MS/MS spectrum (e.g., from MassBank or local data) and need to identify significant fragment ions above noise, assign occurrence scores to peaks, and generate a CSV-formatted library entry for use in metabolite feature annotation pipelines.

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 you have precomputed expected contact frequency tables (TSV format with columns like dist_bp, contact_frequency, n_valid) and need to apply log-binning and smoothing to group distance values into log-spaced bins, aggregate statistics within each bin, and export a cleaned, annotated output.

holobiomicslab Skill

Use when after loading a raw metabolomics data matrix (samples × features in CSV format, log2-normalized with batch labels in the first column) and before selecting a batch correction model.

holobiomicslab Skill

Use when when ingesting spectra from multiple open mass spectrometry libraries (OMSLs) in .mgf, .msp, .json, or .csv format and you observe mixed experimental protocols, inconsistent adduct annotations, or partial metadata.

holobiomicslab Skill

Use when after molecular structures have been standardized (e.g., via PubChem standardization) and you need to enrich them with chemical taxonomy labels for cohort stratification, chemical space analysis, or retention time prediction model development.

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 you have molecular descriptor data in CSV or EXCEL format and need to pass it to BitterPredict.m or another descriptor-based classifier.

holobiomicslab Skill

Use when when loading lipidomics data (from Skyline CSV, numerical matrix, or Metabolomics Workbench) and encountering parsing warnings or molecules with names that do not follow standard lipid nomenclature patterns (e.

holobiomicslab Skill

Use when you have raw compound or natural-product metadata in spreadsheet or tabular form with inconsistent column names, missing structure information, or when you need to query against the LOTUS natural-product database and suspect the local copy is stale or absent.

holobiomicslab Skill

Use when ingesting SMILES strings from CSV or other bulk molecular input files before passing them to structure-based prediction tools.

holobiomicslab Skill

Use when you have pre-trained MSGO model weights (PFAS or lipid variant) and a set of experimental mass spectra (either from reference evaluation sets or real wastewater/environmental samples) in CSV format with m/z and intensity pairs, and you need to obtain candidate molecular structures ranked.