Data & Analytics

holobiomicslab Skill

Use when when you have annotated representative LCMS samples (raw mzML files + labeled feature tables in mzmine CSV format) and need to convert them into balanced or unbalanced peak matrix batches with fixed dimensions for neural network training.

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 a list of polypeptide sequences (one per line or CSV format) and need to compute average mass (weighted by natural isotope abundances) to compare against experimental LC-MS or MS/MS data where the full isotopic distribution—not just the most abundant peak—is relevant for peptide.

holobiomicslab Skill

Use when you have an m/z-resolved feature list from LC- or GC-HRMS analysis (either detected by pyOpenMS or provided as a custom Excel table) and need to prioritize potential PFAS compounds by identifying clusters of homologous structures.

holobiomicslab Skill

Use when you have raw 1D ¹H NMR spectroscopy output consisting of (1) a CSV file with chemical shift and intensity columns and (2) a TXT file listing detected peak chemical shifts, and you need to load and validate these into memory before passing them to a metabolite identification model like.

holobiomicslab Skill

Use when you have access to a lipidomics library repository (e.g., LipidMatch .csv files) and need to audit or report the total number of distinct lipid species and lipid-type categories present.

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 you have a set of molecular structures (in .sdf or .csv SMILES format) and need to predict which CYP isoforms will metabolize them.

holobiomicslab Skill

Use when you have a collection of molecular structures intended for CypReact-based CYP isoform metabolism prediction and need to verify the input file conforms to CypReact's expected format (comma-separated SMILES in .csv or SDF molecular structure files) before submission to the tool.

holobiomicslab Skill

Use when when you have SQL-inspired query strings that encode domain-specific assumptions and need to validate, transform, or execute them against data repositories. Specifically: input is human-readable DSL text containing mass spectrometry-specific clauses (e.

holobiomicslab Skill

Use when you have formula-assigned FT-ICR MS data (CSV or tab-delimited table with molecular formulas and mass values) and need to quantify elemental stoichiometry, compute diversity indices, or assess molecular complexity across environmental organic mixtures.

holobiomicslab Skill

Use when when you have mass spectrometry data in a Pandas DataFrame with columns for m/z, retention time (or ion mobility), and intensity, and you want to generate interactive (rather than static) visualizations for exploratory analysis, interactive drill-down, or deployment in web applications or.

holobiomicslab Skill

Use when you have matched multiomics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics) across a cohort of cell lines or samples and want to infer which molecular features (genes, transcripts, proteins, methylation sites) regulate metabolite abundance.

holobiomicslab Skill

Use when your input is a raw or prepared multi-class metabolomic peak table (in ExpressionSet format or CSV) where samples belong to distinct biological classes, and you have either QC samples (quality control replicates) or internal standards (IS) to anchor batch correction.

holobiomicslab Skill

Use when you have mass spectrometry data loaded into a Pandas DataFrame with columns for m/z, retention time (or ion mobility), and intensity, and you need to visualize the complete 2D peak map landscape to identify co-eluting features, assess data quality, or explore retention time and.

holobiomicslab Skill

Use when you have one or more polypeptide sequences (from FASTA, CSV, or direct input) and need to compute monoisotopic mass, average mass, isotopic distribution, charge state, or isoelectric point for downstream proteomics analysis such as MS database matching, retention time prediction, or.

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 when you have an existing tool or library with file-based storage (JSON, CSV, binary formats) that needs to support efficient querying across large datasets, and you want to migrate to a relational database without breaking existing functionality or performance expectations.

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 raw feature table (TSV/CSV) derived from LC-MS peak detection (e.

holobiomicslab Skill

Use when when initializing a SmartPeak session and you have a workflow.csv file co-located with sequence.csv in the session directory, and you need to determine the precise order and validity of peak detection, calibration, and QC operations before execution.

holobiomicslab Skill

Use when when you have a retention-time dataset (e.g., SMRT or Eawag_XBridgeC18_364) in .xlsx format and need to train or adapt a graph neural network model to predict chromatographic retention times for new compounds.

holobiomicslab Skill

Use when you are implementing a new MsBackend subclass and need to store spectra metadata (sample names, retention times, precursor m/z, etc.) separately from peak data (m/z and intensity pairs) while maintaining row-wise alignment.

holobiomicslab Skill

Use when you have completed peak detection and feature alignment in metabolomic LC-MS processing and suspect systematic errors in peak integration or feature misalignment across your sample cohort.

holobiomicslab Skill

Use when when beginning an untargeted LC-MS metabolomics study and need to assemble a cohort of mzML files for processing; particularly when establishing performance baselines across sample counts (10, 50, 100+ samples), validating reproducibility, or preparing data for publication.

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 you have TSV or CSV files containing structure-organism pairs (with columns for structure identifier and organism identifier) and need to count unique pairs, unique structures, unique organisms, or enumerate source database identifiers to validate or report dataset scale.

holobiomicslab Skill

Use when you have mzPeak files stored as Parquet tables within a ZIP archive and need to load spectrum metadata, chromatogram metadata, signal data (profile or centroid), or peaks into memory for analysis in R, Python, or another Arrow-supported language.

holobiomicslab Skill

Use when you have preprocessed MS/MS spectral data (converted to bag-of-fragments format with neutral losses extracted and noise filtered) and seek to identify recurring fragmentation patterns indicative of molecular substructures across a spectral cohort.

holobiomicslab Skill

Use when you have uploaded a pre-analytical data table containing sample metadata, processing delay timestamps (pre- and post-centrifugation), and NMR metabolomic measurements for a cohort of plasma or serum samples, and you need to assess how processing delays affect metabolite concentrations and.

holobiomicslab Skill

Use when you have mass spectrometry spectral data loaded into a Pandas DataFrame with columns representing m/z (mass-to-charge ratio) and intensity values, and you need to visualize the spectrum to inspect peak patterns, identify high-abundance ions, or compare spectral profiles across samples or.

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 molecular chemodiversity, elemental composition diversity, or complexity of environmental organic samples for cross-sample comparison or.

holobiomicslab Skill

Use when when you have prepared raw mass spectrometry transition data (precursor m/z, product m/z, intensity, retention time, compound identifiers) and need to verify it conforms to EISA-EXPOSOME's required schema before database import. Specifically: you are building or validating a custom .xlsx/.

holobiomicslab Skill

Use when after raw mass spectrometry data has been converted to a peak feature table (CSV or tabular format) containing mass-to-charge ratios, retention times, and intensity values across samples, and you need to reduce noise and identify which peaks show meaningful differential patterns between.

holobiomicslab Skill

Use when you have raw omics expression data in CSV format (rows=genes/features, columns=samples) and a separate group annotation file (sample IDs mapped to experimental groups or phenotypes), and you need to prepare them for heatmap or other omics visualizations in an R Shiny environment.

holobiomicslab Skill

Use when you have deposited spatio-molecular matrices (e.g., MORPHnMOL.csv from SpaceM analysis) and need to reproduce or extend the data transformations, feature extractions, and figure generation reported in a metabolomics manuscript.