HolobiomicsLab
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- ▌ Arrival Time To Ccs Conversion · holobiomicslabUse when when you have raw TWIM-MS arrival-time data and need to transform it into absolute CCS values for downstream biomolecular class assignment or comparative analysis.
- ▌ Artifact Checksum Verification · holobiomicslabUse when when reproducing a prior software release (especially one generated by automated versioning tools like Semantic Release), you need to confirm that the artifacts produced in your environment match the original release byte-for-byte.
- ▌ Asynchronous API Orchestration · holobiomicslabUse when you have a batch of mass spectra records in .msp format that lack standardized metadata fields (SMILES, InChI, CAS numbers, molecular formula, IUPAC names) and need to populate them by querying multiple independent web APIs in parallel.
- ▌ Asynchronous Event Aggregation · holobiomicslabUse when your workflow fetches data from multiple external web services (e.g., CIR, CTS, PubChem, IDSM, BridgeDb) asynchronously and you need to track which services are available, how often they fail, and their response latencies during a long-running annotation job.
- ▌ Biomolecular Class Ccs Mapping · holobiomicslabUse when after biomolecular class labels have been assigned to features in a TWIM-MS dataset and you have raw ion mobility arrival time measurements. Use it when you need to convert arrival times to standardized CCS values where calibration accuracy depends critically on the biomolecular class (e.
- ▌ Class Specific Ccs Calculation · holobiomicslabUse when when you have multi-omic TWIM-MS data (raw or processed arrival-time records) and have already assigned features or detected ion features to biomolecular classes (e.
- ▌ Compressed File Header Parsing · holobiomicslabUse when you have a large indexed gzip file (igz format) with metadata encoded in the gzip header comment field, and you need to retrieve specific blocks or spectra by integer index without decompressing the entire file.
- ▌ Cross Dataset Feature Matching · holobiomicslabUse when you have two or more feature tables in HDF5 format with detected features characterized by m/z, drift time, retention time, and intensity, and you need to match corresponding features across samples to account for systematic shifts caused by instrument variation or tuning differences.
- ▌ Docker Container Orchestration · holobiomicslabUse when when you have .mzML or .abf LC-HRMS raw data files that require MS-DIAL-based feature detection, chromatogram alignment, and metabolite identification, and you need to ensure reproducibility across local machines, cloud, and HPC systems without manual tool installation and dependency.
- ▌ False Positive Rate Assessment · holobiomicslabUse when you have run ORA on simulated metabolite sets with known null conditions (no true pathway enrichment) and need to measure how detection coverage, pathway database size, or other experimental parameters inflate Type I error rates.
- ▌ Frequency Distribution Binning · holobiomicslabUse when you have loaded a table of entity–attribute pairs (e.
- ▌ Github Actions API Integration · holobiomicslabUse when when you need to verify that a GitHub Actions workflow (such as a development build or release pipeline) executes without fatal errors and produces expected artifacts. Use this skill when the workflow is already configured in a repository (e.g., a .yml file in .
- ▌ Hdf5 Format Support Validation · holobiomicslabUse when when you need to work with mzMLb (HDF5-based) proteomics data in pyteomics and want to confirm that the required h5py and hdf5plugin libraries are installed and accessible, or when troubleshooting ImportError or missing format handler issues related to mzMLb modules.
- ▌ HTTP Connectivity Verification · holobiomicslabUse when you need to confirm that a documented web service URL is live and reachable before attempting to submit analysis jobs, download results, or integrate the service into an automated pipeline. Use it as a prerequisite check when the service documentation claims academic or public availability.
- ▌ Java Build Artifact Validation · holobiomicslabUse when when you need to verify that a Java project's automated build pipeline (GitHub Actions workflow) executes without errors and generates distributable artifacts (e.g., .deb installers, portable binaries, or .jar files).
- ▌ JSON Validation And Formatting · holobiomicslabUse when after enriching a project JSON document with external metadata (e.g., organism names, genome identifiers) or before writing enriched JSON to disk.
- ▌ Keras Model Conversion To Hdf5 · holobiomicslabUse when you have pre-trained Keras models from the NP-Classifier repository that must be deployed via TensorFlow Serving and need to expose standardized input/output layer names ('input_2048', 'input_4096', 'output') for integration with the classification API.
- ▌ Mass Fragment Pattern Querying · holobiomicslabUse when after LDA modeling has produced an inferred motifset (JSON format) containing Mass2Motifs with fragment and neutral-loss patterns.
- ▌ Metabolite Coverage Simulation · holobiomicslabUse when designing or validating a metabolomics pathway analysis experiment, especially when you have uncertainty about how many metabolites your detection platform will reliably measure relative to a pathway database. Use it if you want to understand whether your expected metabolite coverage (e.
- ▌ Ms Data Constraint Enforcement · holobiomicslabUse when implementing or validating a new MsBackend class that stores m/z and intensity values, or when assigning peak data to an existing backend. Triggers include: (1) implementing a replacement method (e.g., mz<- or intensity<-) for a backend class;
- ▌ Organism Dictionary Enrichment · holobiomicslabUse when after organism names have been cleaned and standardized (e.g., via 1_cleaningOriginal.R and 4_cleaningTaxonomy.R) but before final integration of organism, structure, and reference data. Use it when you have a cleaned organism table (interim/tables/2_cleaned/organism/cleaned.tsv.
- ▌ Pathway Level Activity Scoring · holobiomicslabUse when you have a metabolite intensity matrix (samples × metabolites) with assigned annotations (peak IDs mapped to KEGG or ChEBI compound IDs), a metabolic pathway database, and need to rank pathways by activity level within experimental groups or comparisons.
- ▌ Python Async Method Definition · holobiomicslabUse when when extending MSMetaEnhancer with a new local chemical transformation (e.g., SMILES to InChI) that should execute non-blockingly within an asynchronous annotation workflow.
- ▌ Quality Overview Visualization · holobiomicslabUse when you have sequential QCpool (pooled quality control) samples analyzed with Sciex Multiquant (≥v3.0.
- ▌ R Internal Function Invocation · holobiomicslabUse when when you need to verify or retrieve package-internal metadata about compiled .NET assembly location and version before processing raw mass spectrometry files, or when testing the R↔C# dispatch mechanism in isolation without loading actual Orbitrap .raw data files.
- ▌ Repository Metadata Extraction · holobiomicslabUse when when you need to inventory a collection of related web applications or tools distributed across multiple repositories, discover their live deployment URLs, trace their associated publications, and verify accessibility and metadata completeness.
- ▌ Semantic Versioning Validation · holobiomicslabUse when when you need to confirm that a generated or retrieved release artifact from a version control system (e.g., git tag v1.0.0) produces byte-for-byte or functionally equivalent outputs to the official release published on a platform (e.g., GitHub Releases) on a specific date.
- ▌ Simulation Parameter Variation · holobiomicslabUse when when you have a computational simulation framework (e.
- ▌ Spectral Data Table Conversion · holobiomicslabUse when when you have mzPeak format spectrum files and need to work with spectrum metadata, intensity/m/z arrays, or precursor information in a tabular, columnar, or vectorized computing environment.
- ▌ Spectrum Feature Vectorization · holobiomicslabUse when you have raw mass spectrometry spectra (peak lists or intensity arrays) that must be fed into a pre-trained deep learning model for substance classification (e.g., PS²MS for NPS detection).
- ▌ Structural Type Categorization · holobiomicslabUse when when you have a set of query chemical compounds (by name or SMILES) and a reference library organized into named groups (e.
- ▌ Taxonomic Metadata Integration · holobiomicslabUse when after organism name cleaning and standardization (via 1_cleaningOriginal.R and 4_cleaningTaxonomy.R) has produced a cleaned organism table (interim/tables/2_cleaned/organism/cleaned.tsv.gz), apply this skill to map those cleaned names to Open Tree of Life (OTL) identifiers.
- ▌ Test Suite Pass Fail Reporting · holobiomicslabUse when when you need to verify that a research software package (e.g., MassQL) maintains functional correctness over time, assess the reliability of a tool before integration into a workflow, or document test coverage and failure modes for reproducibility.
- ▌ Time Domain Signal Apodization · holobiomicslabUse when working with raw FT-ICR transient data (e.g., ESI_NEG_SRFA.d format) prior to noise thresholding and mass-domain calibration.
- ▌ Url Format Constraint Checking · holobiomicslabUse when ingesting or validating project JSON documents against a schema (such as app/public/schema.json in the Pairing Omics Data Platform) that designates certain fields as URL type (e.
- ▌ Whitespace Character Detection · holobiomicslabUse when validating project JSON documents against the platform's schema (app/public/schema.json) and you need to ensure all URL-type fields conform to URL syntax rules. Specifically, use it when the schema designates certain fields as URL type (e.
- ▌ Adduct Mass Difference Ranking · holobiomicslabUse when you have computed a histogram of mass differences from all pairwise mass comparisons in your MALDI-MS imaging dataset and need to prioritize which mass differences are most frequent and likely represent genuine molecular adducts (e.g., metabolite + matrix ions) rather than noise.
- ▌ Backend Performance Comparison · holobiomicslabUse when you have execution-time metrics (from a benchmark table or profiling logs) across multiple visualization backends for the same set of plots (e.
- ▌ Batch Effect Visualization Pca · holobiomicslabUse when after applying CordBat batch correction to a log2-transformed metabolite matrix from multi-batch metabolomics data, you want to quantitatively and visually assess whether the correction successfully reduced batch effects.
- ▌ Batch Level Quality Inspection · holobiomicslabUse 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.
- ▌ Batch Spectrum Quality Control · holobiomicslabUse 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.
- ▌ Biochemical Annotation Mapping · holobiomicslabUse when you have loaded MSI data into napari, defined one or more ROIs of biological interest (e.g., tumor margin, specific tissue layer), extracted mean or summed intensity spectra from those regions, and need to identify the putative biochemical compounds corresponding to detected m/z peaks.
- ▌ Calibration Quality Assessment · holobiomicslabUse when after applying polynomial m/z domain recalibration using a reference peak list (e.g., SRFA.ref) to a Bruker FT-ICR dataset. Use this skill to verify that calibration has converged and that mass error statistics support reliable downstream annotation.
- ▌ Chemical Descriptor Extraction · holobiomicslabUse when you have standardized molecular structures (SMILES or SDF format) and need to generate a uniform feature matrix for machine learning models (e.g., retention time prediction).
- ▌ Classyfire Taxonomy Assignment · holobiomicslabUse 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.
- ▌ Column Header Keyword Matching · holobiomicslabUse when importing a new delimited data file (CSV, semicolon-separated, or other formats) into Punc'data that contains high-resolution mass spectrometry results.
- ▌ Command Line Interface Testing · holobiomicslabUse when after installing a Python package or before running a computational workflow for the first time, to verify that the CLI entry point is properly configured, the Python environment is correctly set up, and to discover available command-line arguments and their defaults (e.
- ▌ Command Line Tool Verification · holobiomicslabUse when after installing a command-line bioinformatics tool (e.g., via pip, conda, or package manager) and before attempting to use it on experimental data.
- ▌ Compound Identifier Extraction · holobiomicslabUse when when you receive a raw query string destined for the ClassyFire API and need to distinguish between a user-supplied compound identifier (e.g., a database accession or common name) and the actual chemical structure representation (SMILES, InChI, IAPNIC name, or FASTA sequence).
- ▌ Container Runtime Verification · holobiomicslabUse when when deploying a containerized application (e.g., ipbhalle/metfragweb) with injected configuration files via Docker volume mounts, and you need to confirm that the container accepted the mounted file and applied its settings before proceeding with downstream analysis or services.
- ▌ Converter Output Schema Design · holobiomicslabUse when when integrating multiple heterogeneous metadata services (e.g., CIR, CTS, PubChem, IDSM, BridgeDb) that return unstructured or variably-formatted responses, and you need to enforce uniform output contracts before committing fetched values to mass spectra files (.msp format).
- ▌ Converter Registry Enumeration · holobiomicslabUse when you need to expose all supported metadata conversion options in a tool interface (e.g., Galaxy tool form, CLI argument parser, or API endpoint) and want to avoid hard-coding conversion paths.
- ▌ Cross Modal Alignment Training · holobiomicslabUse when after completing pretraining and fine-tuning stages when you have a checkpoint with task-specific performance but need to improve cross-modal consistency.
- ▌ Cross Platform Build Targeting · holobiomicslabUse when you have a Windows-only .NET Framework application that must run on non-Windows hosts (macOS or Linux), and you need reproducible, isolated execution with support for both interactive GUI and batch CLI workflows.
- ▌ Data Ingestion Pipeline Design · holobiomicslabUse when when building a platform that must accept raw MS data files (e.g., .raw, .mzML, .mzXML) from instrument runs or external sources as the first stage of an automated omics workflow.
- ▌ Deep Learning Layer Inspection · holobiomicslabUse when when you have downloaded pre-trained Keras models and need to confirm their layer naming and structure conform to requirements for TensorFlow Serving or other deployment pipelines—specifically before converting to HDF5 format or integrating into a production API that expects fixed.
- ▌ Dependency Management With Pip · holobiomicslabUse when when you have cloned a Python package repository and need to verify that the package and its test suite can be installed and executed locally, or when preparing to contribute code changes that must pass the project's test suite before submission.
- ▌ Dependency Manifest Extraction · holobiomicslabUse when a bioinformatics package claims to install a large number of tools (e.g., ~89 for untargeted metabolomics pipelines) but the article or main documentation does not enumerate them explicitly.
- ▌ Directory Structure Validation · holobiomicslabUse when after editing core R scripts in the Core-Match repository and before running LipidMatch-4.2 analysis, to verify that developer modifications have been correctly distributed across the Flow version (LipidMatch_Distribution) and Modular version (FluoroMatch_Modular) directory trees.
- ▌ Embedding Space Representation · holobiomicslabUse when you have pairs or triplets of MS/MS spectra with associated metadata (compound structural information, Tanimoto similarity scores) and want to learn embeddings that simultaneously preserve spectral similarity relationships and reconstruct peak intensities.
- ▌ Execution Time Metric Analysis · holobiomicslabUse when you have computation-time metrics from a gallery or benchmark suite comparing multiple plotting backends on the same data types (e.
- ▌ Feature Alignment Metabolomics · holobiomicslabUse when you have detected multiple ion peaks from replicate injections of the same sample in untargeted metabolomics and need to consolidate them into a single feature table.
- ▌ Feature Intensity Thresholding · holobiomicslabUse when after feature extraction from mzML/mzXML files when you have identified candidate peaks in breath spectrometry data but need to separate true VOC signals from instrument noise.
- ▌ File Compression And Archiving · holobiomicslabUse when when a spectral processing operation produces multiple output artifacts in different formats (e.
- ▌ File Format Handling CSV Excel · holobiomicslabUse when you have molecular descriptor data in CSV or EXCEL format and need to pass it to BitterPredict.m or another descriptor-based classifier.
- ▌ Fixed Modification Propagation · holobiomicslabUse when your search engine output (PSM file) omits fixed modifications that were configured during the search (e.
- ▌ Format Agnostic Data Ingestion · holobiomicslabUse when when you have mass spectrometry data in multiple formats (mzML, mzXML) that must be fed into a compression, analysis, or transformation pipeline that operates on a single canonical spectral data model.
- ▌ Formula Parsing And Evaluation · holobiomicslabUse when you have multiple open elemental images (e.g., P31, Zn66) from LA-ICP-MS and need to create a new derived image by evaluating a per-pixel mathematical expression. Typical triggers include: combining isotope ratios (e.
- ▌ Ftms Mass Calibration Workflow · holobiomicslabUse when when you have a processed Bruker Solarix FT-ICR mass spectrum object (from ReadBrukerSolarix) and a reference peak list file (SRFA.
- ▌ Generic Hdf5 Library API Usage · holobiomicslabUse when when you have converted multidimensional MS data to MZA HDF5 format and need to programmatically read spectra, metadata, and ion mobility or mass-to-charge arrays in Python or R without vendor-specific dependencies.
- ▌ Git Branch Workflow Validation · holobiomicslabUse when when preparing to create a release branch in a Maven-based multi-module project (e.g., NMRFx), use this skill to verify that all pom.xml files in the repository tree declare identical version strings. This is a prerequisite for safe release-branch naming (e.g., release/X.Y.
- ▌ Hash Function Parameterization · holobiomicslabUse when when you have normalized or vectorized mass spectrometry spectral data (m/z and intensity pairs converted to feature vectors) and need to construct an LSH index for fast nearest-neighbor retrieval of similar spectra.
- ▌ HTTP Post Request Construction · holobiomicslabUse when you need to submit structured chemical compound data (identifiers and structural representations) to a remote REST API that accepts POST requests and returns JSON responses, particularly when the API requires tab-separated compound inputs separated by newlines and a query label as the.
- ▌ Image Visualization Formatting · holobiomicslabUse when you have loaded a normalized or raw pixel array (NumPy format) with associated metadata from MSI line-scan data, and need to generate publication-quality ion images with controlled intensity scaling, smoothing, and optional ratio or fractional abundance comparisons across multiple m/z.
- ▌ Intensity Color Representation · holobiomicslabUse when when visualizing 2D peak maps (x=m/z, y=retention time or ion mobility, z=intensity) using pyOpenMS-viz with any plotting backend (matplotlib, bokeh, plotly).
- ▌ Java Diagnostic Tool Operation · holobiomicslabUse when when you have deployed a JVM application in a Docker container with JAVA_OPTS heap size parameters and need to verify that the maximum and initial heap sizes are correctly configured before running memory-intensive workflows like metabolite fragmentation analysis.
- ▌ Java Maven Build Orchestration · holobiomicslabUse when when you have access to a Java project with a Maven pom.xml defining multiple interdependent modules (e.
- ▌ Lexer Tokenizer Implementation · holobiomicslabUse when when you need to parse a domain-specific query language (like MassQL) into an abstract syntax tree (AST), and the query strings must be decomposed into discrete lexical units before syntactic analysis.
- ▌ Lipid Class Feature Annotation · holobiomicslabUse 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.
- ▌ Lipid Identifier Normalization · holobiomicslabUse when when you have a list of lipids identified by different database identifiers (e.
- ▌ Log Transformation Application · holobiomicslabUse when apply log-transformation immediately after loading a raw metabolomics featuredata matrix (metabolite peak intensities as rows=samples, columns=metabolites) and before normalization.
- ▌ Lotus Metadata Standardization · holobiomicslabUse 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.
- ▌ Low Energy Structure Selection · holobiomicslabUse when after generating an ensemble of 3D conformers via RDKit conformation sampling, when you need to reduce the conformer set size before expensive quantum-chemical calculations (e.g., QUICK).
- ▌ M Z Value Ordering Enforcement · holobiomicslabUse when when implementing or modifying data replacement methods (e.g., `mz<-`, `peaksData<-`) in a writable MsBackend subclass, or when accepting user-supplied m/z vectors destined for storage in a Spectra backend.
- ▌ Mass Error Tolerance Filtering · holobiomicslabUse when when annotating full-scan MS or MS imaging data against a metabolite database (e.g., LipidMaps, HMDB) and you need to control the stringency of m/z matching. Use this filter to balance annotation sensitivity against specificity: tighter ppm tolerances (e.
- ▌ Mass Spectrum Peak Aggregation · holobiomicslabUse when you have a processed mass spectrum object with assigned molecular formulas for individual peaks and need to summarize peaks by their heteroatom composition—e.
- ▌ Mass Spectrum Query Processing · holobiomicslabUse when you have an unknown mass spectrum (as m/z and intensity arrays) and need to identify candidate compounds by matching against a mass spectral library.
- ▌ Matrix Data Structure Handling · holobiomicslabUse when after feature filtering has removed low-abundance or highly sparse metabolites (e.g., >80% missingness), and before applying missing value imputation or normalization.
- ▌ Matrix Directive Field Mapping · holobiomicslabUse when you have extracted tabular data in intermediate JSON form and need to convert a table of records into a list of dictionaries (array of objects) where each row becomes a dictionary entry, optionally removing sensitive or irrelevant fields and normalizing all values to strings for downstream.
- ▌ Matrix Multiplication Pipeline · holobiomicslabUse when after completing 10-fold cross-validated training of MiMeNet neural networks on paired microbiome-metabolome datasets and identifying well-predicted metabolites (those with Spearman correlation coefficient above the 95th percentile of background distribution).
- ▌ Metabolomic Feature Imputation · holobiomicslabUse when when a preprocessed metabolomic feature table (e.g., MS-Dial output) retains features and samples that passed filtering for missingness thresholds and m/z validity, but still contain scattered missing values (NA). This skill is appropriate after sample-level filtering (e.
- ▌ Metaboprep Object Manipulation · holobiomicslabUse when you have imported raw (un)targeted metabolite data (from Metabolon, Nightingale, Olink, or SomaLogic platforms, or custom tab-delimited tables) and need to apply a uniform, reproducible QC and normalization pipeline.
- ▌ Model Checkpoint Serialization · holobiomicslabUse when after successfully training a spectrum prediction model (FFN encoder, GNN encoder, intensity predictor, or fragment generator) to completion or at intermediate milestones, and before using that model for inference on test sets, structural elucidation queries, or transfer learning.
- ▌ Module Assignment Biclustering · holobiomicslabUse when you have a trained neural network model of microbiome-metabolome associations and have derived a feature attribution score matrix (microbes × metabolites) quantifying the strength of each microbe-metabolite interaction.
- ▌ Molecular Embedding Generation · holobiomicslabUse when when you have molecular structures (SMILES or explicit graph representations) and need fixed-size vector representations to feed into a prediction head or transfer learning task.
- ▌ Molecular Structure Validation · holobiomicslabUse when ingesting SMILES strings from CSV or other bulk molecular input files before passing them to structure-based prediction tools.
- ▌ Multiple Comparison Correction · holobiomicslabUse when after running ANOVA or G-test statistical analysis across multiple biomolecules in an omics dataset (e.
- ▌ Mzml File Format Understanding · holobiomicslabUse when you have mass spectrometry raw data in mzML format (including compressed variants like mzML.
- ▌ Network Node Attribute Mapping · holobiomicslabUse when you have a GLASSO-generated network graph and corresponding PCA scores from a prior dimensionality-reduction step, and you need to identify which nodes or sub-networks are associated with high or significant PCA scores.
- ▌ Neural Network Model Inference · holobiomicslabUse 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.