Productivity

holobiomicslab Skill

Use when after applying one or more mpactr filters (mispicked, group, CV, or insource) to a peak table in a chained filtering workflow.

holobiomicslab Skill

Use when you have a GNPS task ID from a completed molecular networking workflow (METABOLOMICS-SNETS, METABOLOMICS-SNETS-V2, FEATURE-BASED-MOLECULAR-NETWORKING on GNPS1, or classical_networking_workflow / feature_based_molecular_networking_workflow on GNPS2) and need to access the resulting spectral.

holobiomicslab Skill

Use when after completing feature annotation and reaction assignment in an untargeted metabolomics workflow, specifically when you have a feature-by-sample intensity matrix aligned with metabolite identities and want to communicate cluster structure, reaction pathway groupings, and feature.

holobiomicslab Skill

Use when you have a GNPS-generated molecular network (classical or feature-based workflow) and corresponding MS2LDA LDA experiment results (Mass2Motif assignments and/or chemical class predictions), and you need to propagate those annotations to individual network nodes to support visual and.

holobiomicslab Skill

Use when after generating a peak table from XCMS peakTable() output in an untargeted LC-MS metabolomics workflow, if your experimental design includes quality control (QC) samples (SampleType='LQC') and you want to exclude noisy or unstable EICs before building a peak quality classifier.

holobiomicslab Skill

Use when after parent chemical suspects have been identified in a non-target screening workflow, use this skill when you need to screen for downstream products formed by chemical or biological transformation.

holobiomicslab Skill

Use when when training an object detection network that must predict both discrete labels (e.g., true peak vs. false peak) and continuous coordinates (e.

holobiomicslab Skill

Use when after ISF features have been identified in Part 4 of the ISFrag workflow and you need to export or visualize the hierarchical structure of detected in-source fragments relative to their parent ions.

holobiomicslab Skill

Use when you have loaded DIA mass spectrometry search results containing feature identification data with associated Q-value scores, and you need to restrict the analytes available in selection drop-downs to those meeting a quality cutoff (typically 1% FDR equivalent) before visualization or manual.

holobiomicslab Skill

Use when when you have processed LC-MS/MS data with precursor m/z, ionization mode, collision energy (if available), and fragment peak lists (m/z and intensity pairs), and need to query CSI:FingerID for molecular fingerprint predictions as part of an automated metabolite identification workflow.

holobiomicslab Skill

Use when you are evaluating or selecting FT-ICR MS software for a specific metabolomics workflow and need to assess which tools support your required analytical dimensions (e.g., Van Krevelen diagrams, PERMANOVA, thermodynamic indices, chemodiversity metrics, transformation networks).

holobiomicslab Skill

Use when when you have prepared metabolomics data (e.g., covid_data) with a binary outcome variable and need to select the most appropriate predictive algorithm for your classification task.

holobiomicslab Skill

Use when when evaluating whether an MS data processing platform (such as mzmine) supports the full range of separation/ionization techniques your laboratory uses, or when assessing whether gaps exist in the software architecture that would require external pre- or post-processing for specific.

holobiomicslab Skill

Use when you have a molecular geometry (XYZ format) and need to explore its conformational space prior to quantum mechanical property calculation or when QCxMS2 workflow requires an ensemble of low-energy structures to enable systematic fragmentation pathways for EI mass spectrum simulation.

holobiomicslab Skill

Use when immediately after executing the MetaboAnalystR 4.0 unified LC-MS workflow (feature detection and quantification module) on raw mzML or netCDF data.

holobiomicslab Skill

Use when when you have a GNPS molecular networking task ID (from GNPS1 or GNPS2 workflows: METABOLOMICS-SNETS, METABOLOMICS-SNETS-V2, FEATURE-BASED-MOLECULAR-NETWORKING, classical_networking_workflow, or feature_based_molecular_networking_workflow) and need to prepare the job archive for NPLinker.

holobiomicslab Skill

Use when you have raw or partially processed metabolomics data (mzML/mzXML format) from LC-MS or GC-MS runs and need to apply standardized feature detection, alignment, and intensity normalization as part of a reproducible workflow.

holobiomicslab Skill

Use when when you need to confirm that a Java project's GitHub Actions workflow (e.g., 'dev_build_release.yml') has completed successfully and generated usable build artifacts;

holobiomicslab Skill

Use when after executing an MZmine batch processing workflow on raw metabolomics data (mzML/mzXML format), when you need to convert the in-memory feature detection and alignment results into a shareable, schema-compliant tabular format suitable for downstream statistical analysis, figure.

holobiomicslab Skill

Use when you have raw MS data files from Thermo Orbitrap or other vendor instruments that must be uploaded into a centralized platform for automated processing. Use it at the start of a multi-stage omics workflow where data collection, management, processing, and visualization are integrated;

holobiomicslab Skill

Use when you need to supply candidate metabolite structures for mass spectrometry annotation when working within an integrative metabolomics data analysis workflow (such as MAGMa).

holobiomicslab Bundle

Use when you have LC-MS data from a stable-isotope (e.g. 13C / 15N) tracing experiment and want labelling / flux information — detect features, extract per-feature isotopologue distributions, correct for natural isotope abundance, and compute mass-isotopomer distributions and fractional labelling enrichment across conditions or timepoints.

holobiomicslab Skill

Use when you have raw FASTQ sequencing reads (single-end or paired-end) and a reference transcriptome FASTA file, and you need to determine which transcript(s) each read aligns to in order to quantify transcript abundance. This is the core mapping stage in a salmon quant workflow;

holobiomicslab Skill

Use when when you have cloned or downloaded a Python project repository and need to install all declared dependencies to make the package importable and functional. Use this skill at the start of any local setup workflow when a requirements.

holobiomicslab Skill

Use when you have a raw or partially processed multi-class or time-series metabolomic peak table (in standardized or software-specific format) and need to determine which combination of imputation, QC sample normalization, transformation, and metabolite/sample-based normalization methods will.

holobiomicslab Skill

Use when you have raw lipidomic and metabolomic data files generated by the Multi-ABLE barocycler-based concurrent multiomics method and need to perform integrative preprocessing (spectral normalization and alignment across samples) followed by multivariate analysis to identify differential lipids.

holobiomicslab Skill

Use when you have a machine learning training workflow (e.g., k-fold cross-validation) where each fold is independent, can run in parallel, and requires exactly one GPU per fold.

holobiomicslab Bundle

Use when you have ion-mobility LC-IMS-MS/MS data (e.g. timsTOF / PASEF) and want CCS-aware annotations — 4D feature extraction with collision cross section, CCS calibration and filtering, CCS-aware library matching, and (optional) networking.

holobiomicslab Bundle

Use when you have untargeted lipidomics LC-MS/MS data (mzML) and want a class- and species-level annotated lipid feature table — preprocessing, normalization, lipid identification by MS/MS, retention/adduct rule validation, differential analysis, and a fused master table.

holobiomicslab Bundle

Use when you have NMR metabolomics data (1D/2D spectra or FIDs) and want a quantified, identified metabolite profile — spectral preprocessing (phase/baseline/referencing, binning), metabolite identification by chemical shift, quantification, and group statistics.

holobiomicslab Skill

Use when you have a feature table generated from LC-MS/MS non-targeted metabolomics data that spans multiple sample preparation batches, instrumental runs, or experimental conditions.

holobiomicslab Skill

Use when after running Paramounter's peak-height optimization on XCMS CentWave-extracted metabolomic features, if the downstream analysis or feature validation reveals an unacceptable rate of false positives, or if the extraction workflow is experiencing software crashes or timeout failures due to.

holobiomicslab Skill

Use when after peak picking and sample alignment when you have an aligned feature table containing m/z and retention time coordinates. Use it when your untargeted LC-MS workflow needs to reduce feature redundancy caused by naturally occurring stable isotope patterns and common adduct formation.

holobiomicslab Skill

Use when you need to execute a complex computational chemistry workflow (QCxMS2) that depends on multiple external semiempirical and ab initio quantum chemistry packages.

holobiomicslab Skill

Use when xCMS has produced aligned LC-MS features but alignment quality is suspected to be poor—especially when analyzing hundreds of samples, data acquired over extended periods (>1 week), or when individual m/z bins show inconsistent RT shifts.

holobiomicslab Skill

Use when you have a pretrained spectrum encoder (e.g., TCN on mass spectrometry data) that has learned useful representations, and you need to train new components (e.g., a rescoring module) for a related but distinct task (e.