Data & Analytics

holobiomicslab Skill

Use when after mzRAPP has exported a benchmark CSV file from centroided mzML files and you need to confirm the benchmark was constructed correctly before using it to evaluate NPP tool performance. Specifically, when you have a reference expectation (e.

holobiomicslab Skill

Use when when you have peak-abundance .csv files with assigned molecular formulas (elemental composition: C, H, O, N, P, S) from FT-ICR MS or high-resolution MS and need to characterize the redox and structural properties of the molecular pool—e.

diegosouzapw Bundle

Polars workflow skill. Use this skill when the user needs Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

diegosouzapw Bundle

LibreOffice Calc workflow skill. Use this skill when the user needs Spreadsheet creation, format conversion (ODS/XLSX/CSV), formulas, data automation with LibreOffice Calc and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

holobiomicslab Skill

Use when when migrating spectral library data from file-based formats (JSON, CSV, binary) into a persistent store and need to support fast filtered queries on metadata and similarity computations against query spectra.

holobiomicslab Skill

Use when when testing associations between metabolic features (from NMR or MS) and a phenotype of interest (e.g., BMI, disease status) in a cohort where age, gender, or clinical confounders are known to correlate with both the metabolite and phenotype.

holobiomicslab Skill

Use when you have uploaded a pre-analytical data table containing sample metadata, processing timestamps (pre- and post-centrifugation), and NMR metabolomic measurements for a cohort of peripheral blood samples (plasma/serum), and you need to quantify the magnitude and direction of metabolite.

holobiomicslab Skill

Use when you have raw mass spectrometry data in mzML or Bruker .d format and need to ingest it into a tabular format (pandas DataFrame) for visualization, statistical analysis, or integration with other Python-based mass spectrometry tools.

holobiomicslab Skill

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

holobiomicslab Skill

Use when when a user has prepared a custom collection of metabolite sets (e.g., from spectral fragmentation clustering, literature curation, or domain-specific grouping) in CSV or JSON format and wants to score their activity levels using PALS without modifying the core PALS codebase.

holobiomicslab Skill

Use when when consolidating entries from multiple heterogeneous source databases into a unified table, and you need to maintain auditable connections between final curated records and their original source entries.

holobiomicslab Skill

Use when after running NOREVA's multi-class or time-course assessment functions (normulticlassqcall, normulticlassnoall, normulticlassisall, nortimecourseqcall, or nortimecoursenoall) that produce an overall ranking CSV file of preprocessed workflows.

holobiomicslab Skill

Use when you have raw metabolomics data in mzML or mzXML format and need to convert it into a normalized feature table (CSV or mzTab) via automated batch processing.

holobiomicslab Skill

Use when you have access to the aalto-ics-kepaco/retention_order_prediction repository, have installed all Python (scipy, numpy, sklearn, joblib, pandas, networkx) and R dependencies, possess molecular feature data (MACCS fingerprints or equivalent), and need to execute a specific evaluation.

holobiomicslab Skill

Use when you have mass spectrometry data in a Pandas DataFrame with columns for m/z and intensity (spectrum), retention time and intensity (chromatogram), or drift time and intensity (mobilogram), and need to render 1D traces as static or interactive plots for exploratory analysis, quality control.

holobiomicslab Skill

Use when you have executed mzExacto() on a preprocessed GC-MS dataset and need to verify that the returned dataframe correctly matches query chemicals to their m/z peaks, retention times, and quantitative measurements (area values).

holobiomicslab Skill

Use when your input is a feature intensity table (CSV or R data frame) with features as columns and samples as rows, and you have accompanying sample metadata (batch identifiers, QC/study sample labels, run order, sample phenotypes, collection dates).

holobiomicslab Skill

Use when after retention-time and m/z-based peak alignment has been completed across a cohort of LC-MS samples, and you need to create a unified quantitative matrix for statistical testing, multivariate analysis, or annotation workflows.

holobiomicslab Skill

Use when you have aligned feature tables (CSV format) with corresponding MS2 spectra data (MGF or mzML files), and need to construct a sample-level vectorization matrix where each row represents a sample and columns encode the occurrence counts of MS2 peaks and neutral losses observed in that.

holobiomicslab Skill

Use when when processing LC-MS metabolomics datasets with 10 or fewer samples and requiring reproducible mass track alignment across the cohort.

diegosouzapw Bundle

Web Scraper workflow skill. Use this skill when the user needs Web scraping inteligente multi-estrategia. Extrai dados estruturados de paginas web (tabelas, listas, precos). Paginacao, monitoramento e export CSV/JSON and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

diegosouzapw Bundle

Polars workflow skill. Use this skill when the user needs Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

diegosouzapw Bundle

Database Workflow Bundle workflow skill. Use this skill when the user needs Database development and operations workflow covering SQL, NoSQL, database design, migrations, optimization, and data engineering and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

holobiomicslab Skill

Use when you have matched multiomics data (CNV, mutations, DNA methylation, histone PTMs, transcriptomics, miRNA, lncRNA, proteomics, phosphoproteomics) and metabolomics measurements across a cell line panel or cohort, and you want to infer which molecular features (genes, regulatory marks, splice.

holobiomicslab Skill

Use when you have normalized peak intensity tables from FT-ICR MS data (or MetaboDirect pre-processed .csv output) with samples grouped by experimental treatments (e.

holobiomicslab Skill

Use when when you have a pre-trained GNN model checkpoint and need to apply transfer learning to a new chromatography or molecular property prediction dataset (e.g., Eawag_XBridgeC18_364.xlsx) by fine-tuning the model weights on domain-specific examples without retraining from scratch.

holobiomicslab Skill

Use when when you have extracted a peak feature table (CSV or tabular format) with mass-to-charge ratios, retention times, and intensity values across multiple samples, and you need to distinguish genuine differential metabolic signals from instrumental noise or low-abundance background before.

holobiomicslab Skill

Use when after executing a MassQL query that returns a tabulated results DataFrame (e.g., MS1 or MS2 scan metadata, peak intensities, retention times), and you need to produce visual summaries suitable for publication, presentation, or exploratory analysis.

holobiomicslab Skill

Use when you have feature lists in CSV format originating from different acquisition methods (e.

holobiomicslab Skill

Use when you have multiple CSV feature lists from different acquisition methods (e.g., LC-MS vs LC-IMS-MS) or processing software, each using different naming conventions, retention time scales, or m/z precision;

holobiomicslab Skill

Use when you have multiple feature tables (CSV files) from different LC-MS analytical experiments, each containing mass, retention time, intensity, isotope, and adduct annotations, and you need to merge them into a single aligned feature matrix.

holobiomicslab Skill

Use when your input is a tabular file (CSV or Excel) with column headers annotated using MESSES tagging syntax (#<table_name>.id for record identifiers and #.

holobiomicslab Skill

Use when when you have raw mass spectrometry data (from mzML, Bruker .d, or CSV format) loaded into a Pandas DataFrame and need to ensure it has the correct column structure (m/z, retention time, intensity) before invoking pyOpenMS-Viz plotting functions like .plot(kind='spectrum'), .

holobiomicslab Skill

Use when you have a CSV file with predicted probabilities and true binary labels from a classification model, and you need to evaluate classification performance across decision thresholds and communicate it via a standard diagnostic plot suitable for publication or presentation.

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.

holobiomicslab Skill

Use when when you have molecular structures in one format (e.g., SMILES strings in a spreadsheet or text file) but need to feed them to a tool that accepts a different format (e.g., CypReact requires .sdf or .csv with SMILES).