Data & Analytics

holobiomicslab Skill

Use when your MZmine MGF and CSV input files contain mixed or ambiguous ionization modes, or when your experimental design specifies separate negative (NEG) and positive (POS) ionization mode acquisitions that must be processed independently before merging.

holobiomicslab Skill

Use when after converting a CSV file of molecule definitions into a peak properties dictionary using csv_to_peak_properties, or after loading a serialized peak properties dictionary from pickle/JSON storage, before passing it to SMITER simulation functions (e.g., smiter.synthetic_mzml.write_mzml).

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 raw MRM lipidomics export files in vendor-specific formats (e.g., TSV or CSV from a mass spectrometry instrument) and need to convert them into a standardized tabular format before performing lipid identification, statistical analysis, or visualization.

holobiomicslab Skill

Use when you have a CSV file containing molecule definitions (chemical formula, m/z, intensity, retention time, or other peak properties) and need to prepare it for SMITER's simulation workflow.

holobiomicslab Skill

Use when you have a tabular file (CSV or Excel) that has been manually or semi-automatically tagged with export tags, and you need to verify tag correctness before running the extract command to convert the tagged table into intermediate JSON.

holobiomicslab Skill

Use when you have raw MS data in a supported instrument format (Agilent .d, Thermo .raw, Bruker .d, mzML) and a predefined list of molecular targets (CSV with m/z and/or retention time) that you need to quantify.

holobiomicslab Skill

Use when you have a raw or extracted peak feature table (CSV or tabular format) containing mass-to-charge ratios, retention times, and intensity values across multiple samples from different experimental groups, and you need to identify which peaks show statistically significant differential.

holobiomicslab Skill

Use when when you have mass spectrometry data organized in a Pandas DataFrame with m/z values, retention time (RT), and intensity measurements, and you want to visualize the joint distribution and correlation of these three dimensions to identify peaks, assess separation, and detect patterns across.

holobiomicslab Skill

Use when you have a CSV or Excel file containing chemical structure descriptors for one or more molecules, and you want to obtain binary bitter/not-bitter predictions for each molecule using the BitterPredict classifier.

holobiomicslab Skill

Use when you have collected feature lists in CSV format from two or more MS acquisition methods (e.g., LC-MS vs. LC-IMS-MS), processing software packages (e.g., vendor-specific vs. open-source), or instrument platforms (e.

holobiomicslab Skill

Use when before launching TARDIS peak detection on a new LC–MS dataset or target compound list. Apply this skill when you have raw MS data files in vendor formats (e.g., .raw, .d) and/or a spreadsheet-based target list (.xlsx or .

holobiomicslab Skill

Use when you have MS/MS spectral library data currently stored in multiple file formats (JSON, CSV, or binary) and need to enable fast, filtered queries by metadata (e.g., precursor m/z, retention time, molecular class) without loading entire libraries into memory.

holobiomicslab Skill

Use when you have experimental peaklist data (CSV or mzML-derived tables) from UHPLC-HRMS/MS instruments (Q-Exactive, Agilent/Bruker/SCIEX Q-TOF) with fragment m/z values and want to annotate them to known lipid identities.

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

holobiomicslab Skill

Use when when you have quantitative lipidomics data (either from Skyline CSV export or numerical matrix format) with sample annotations and a biological grouping variable (e.

holobiomicslab Skill

Use when you have raw .msp spectral library files (e.g., from MassBank or custom sources) and need to convert them into a structured CSV library format for use in metabolite annotation.

holobiomicslab Skill

Use when you have loaded mzPeak spectrum or chromatogram metadata and signal data into PyArrow Table structures (via the Python mzPeak reader or equivalent) and need to persist them to disk in Parquet format for downstream analysis, interoperability with other languages (R, .

holobiomicslab Skill

Use when you have a raw peak-picked untargeted LC-MS dataframe with columns containing mass-to-charge (m/z), retention time (rt), feature identifiers, adduct annotations, and sample measurements in non-standard column names or mixed column sets.

holobiomicslab Skill

Use when when you have Parquet-encoded tabular data (e.

holobiomicslab Skill

Use when you have generated one or more MemoMatrix objects (MS2 fingerprint matrices from separate sample sets) and need to combine them for cross-cohort alignment, validate structural consistency after merging, or prepare merged matrices for downstream filtering and visualization.

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 you have a CSV or table-formatted input spectral peak list (chemical shifts, intensities, metadata) destined for NMRformer metabolite identification, and you observe or suspect that the list contains noisy peaks or peaks with chemical shifts that do not match known reference standards.

holobiomicslab Skill

Use when when you have LC-MS/MS data in MZmine-generated MGF and CSV files (for positive and/or negative ionization modes) and need to apply a sequence of deduplication, annotation, and dereplication steps defined in a MolNotator YAML configuration file to predict actual molecules and build.

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 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 you have a mass-spectrometry query string written in MassQL (or similar domain-specific SQL-inspired syntax) that must be converted into structured form for execution. The input is raw, unparsed text containing SQL keywords, MS-specific operators (e.

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 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 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 processing a cohort of centroided mzML LC-MS files with high sample-to-sample retention time and m/z drift, and you need reproducible alignment of detected peaks across all samples before gap-filling and feature consolidation.

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.