DevOps & Infra

holobiomicslab Skill

Use 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.

holobiomicslab Skill

Use 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.

holobiomicslab Skill

Use 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).

holobiomicslab Skill

Use 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.

holobiomicslab Skill

Use when before launching the DaDIA pipeline or any multi-package R workflow that has strict version constraints. Use this skill when you have access to an R environment and need to confirm that R ≥4.0, XCMS ≥3.11.4, and metaMS ≥1.25.

holobiomicslab Skill

Use when before launching the DaDIA metabolomics pipeline or any multi-package workflow, when you have an R environment with potentially mixed or unknown package versions and need to confirm that R ≥4.0, XCMS ≥3.11.4, metaMS ≥1.25.

holobiomicslab Skill

Use when you need to deploy CloMet for the first time on a new system, or when you want to ensure reproducible execution of metabolomics data harmonization tasks without manual dependency management.

holobiomicslab Skill

Use when after completing Docker installation and container build steps for CloMet, before attempting substantive data analysis or pipeline execution.

holobiomicslab Skill

Use when you have raw Hi-C FASTQ files from a public repository (NCBI SRA, GEO, or ENCODE-deposited accession) and need to reproduce or validate Hi-C map generation following the ENCODE uniform processing standard, or you need to verify that your pipeline output conforms to reference format and.

holobiomicslab Skill

Use when when you have downloaded a released version of a structured dataset (e.g., LOTUS from Zenodo) and need to confirm it matches the documented headline statistics before downstream analysis, or when auditing data integrity after ingestion into a processing pipeline.

holobiomicslab Skill

Use when you have a pre-trained Keras model and need to deploy it via a Docker-based TensorFlow Serving API (e.g., for molecular classification via SMILES), but the model's layer naming or format does not yet match the target runtime's expectations (e.

holobiomicslab Skill

Use when you have one or more .msp spectral library files (NIST format) that need to be ingested for metadata curation, enrichment via web services, or export after transformation. Use this skill as the entry and exit point for any .msp-based annotation or analysis pipeline.

holobiomicslab Skill

Use when after executing a reproducible simulation pipeline (particularly for Over-representation Analysis in metabolomics), compare the newly generated outputs against reference results to confirm that the simulation was correctly implemented and that the computational environment did not.

holobiomicslab Skill

Use when after retrieving a JSON or tabular response from a web service endpoint (such as CANOPUS), validate the result before parsing or integrating it into your analysis pipeline.

holobiomicslab Skill

Use when you have multiple CDF files containing mass spectrometry imaging data (spectra, m/z arrays, and spatial coordinates) that need to be ingested into MATLAB for the DIMPLE pipeline or similar linear-axis mass spectrometry imaging analysis.

holobiomicslab Skill

Use when you have raw or semi-processed m/z peak lists (positive and negative ion mode) and a sample metadata table, and you need to confirm they meet MetaboShiny's structural and semantic requirements before loading them into the normalization pipeline.

holobiomicslab Skill

Use when preparing Python code for contribution to a project that documents style requirements (black and/or flake8), during pre-commit validation in a CI/CD pipeline, or when reviewing pull requests to enforce uniform code standards across the repository.

holobiomicslab Skill

Use when integrating a new metadata validation step into a conversion pipeline that fetches structured chemical identifiers (SMILES, InChI, CAS numbers, IUPAC names) from external services.

holobiomicslab Skill

Use when you have raw MS2 spectra files (mzML, mgf, msp, mzxml) that may contain multiple MS2 spectra per feature and require reduction or standardization before library matching.

holobiomicslab Skill

Use when when you have obtained a DrugBank release file (requiring access credentials) and need to integrate drug chemical structure, name, and identifier information into a metadata cleanup or chemical enrichment pipeline.

holobiomicslab Skill

Use when you have multiple independent peak-picking algorithms available and need to allow end-users to select among them for the same analytical task (peak detection in untargeted LC-MS data) without coupling the rest of your pipeline to each algorithm's API.

holobiomicslab Skill

Use when your Nextflow metabolomics workflow has been validated with Docker locally, but you need to deploy it on an HPC cluster that mandates Singularity containerization (e.g., Red Hat Enterprise Linux 8.

holobiomicslab Skill

Use when you have raw LC-HRMS metabolomics data in .mzML or .abf format and need to perform peak detection, feature alignment, and metabolite annotation in a reproducible, containerized environment.

holobiomicslab Skill

Use when your analysis target is an HPC environment (e.g., Slurm-managed cluster, university research computing center) where Singularity is available but Docker is restricted or unavailable; your workflow is already packaged in Docker but needs portability to HPC;

holobiomicslab Skill

Use when your LC-HRMS metabolomics analysis must run on a high-performance computing cluster (e.g., HiPerGator, SLURM-managed systems) that lacks Docker support or prefers Singularity for security and portability. You have .mzML or .

holobiomicslab Skill

Use when you have raw Hi-C FASTQ files from a sequencing experiment and need to generate kilobase-resolution Hi-C contact maps conforming to ENCODE reference standards.

holobiomicslab Skill

Use when before invoking any Python module in a multi-step Hi-C processing pipeline, or when a dependency has been freshly installed or reinstalled.

holobiomicslab Skill

Use when you are preparing to run a complex multi-tool bioinformatics pipeline (such as HiC-Pro) on a new system or cluster, and need to confirm that all required binaries exist in the execution environment and meet minimum version thresholds (e.g., samtools >=1.9, Python >3.

holobiomicslab Skill

Use when after completing a multi-stage Docker build targeting a compiled runtime environment (e.g., airdpro:cli produced from a Wine + .NET Framework 4.8 + Ubuntu 22.

holobiomicslab Skill

Use when you need to containerize a C#-based Windows application (like AirdPro CLI) for Linux deployment, require Wine and .NET Framework 4.

holobiomicslab Skill

Use when you have generated or received mzPeak files from a Rust, Python, R, or other implementation and need to verify they comply with the published HUPO-PSI specification before integration into a production workflow, data repository, or downstream analysis pipeline.

holobiomicslab Skill

Use when you need to verify that a GitHub Actions workflow (such as a development build release pipeline) has completed successfully, capture its build artifacts (installers, portable binaries, or packages), and document the workflow run metadata.

holobiomicslab Skill

Use when you need to validate that a repository's automated build, test, or publish pipeline is functioning correctly on a target branch (e.g., release branch); when you want to confirm that workflow status badges in documentation accurately reflect current execution state;

holobiomicslab Skill

Use when when deploying a new Python package in a reproducible analysis environment or continuous integration pipeline, and you need to confirm that all required core modules (e.g., pyteomics.mass, pyteomics.pepxml, pyteomics.mzid, pyteomics.tandem, pyteomics.

holobiomicslab Skill

Use when when building or modifying an asynchronous annotation pipeline that dispatches metadata enrichment requests to multiple heterogeneous web services and must verify that each service's HTTP calls succeed, response parsing is correct, and enriched fields are correctly merged into the output.

holobiomicslab Skill

Use when when deploying an R package from a non-CRAN repository (e.g., r-universe, Bioconductor, GitHub), or when verifying that a package build is reproducible and meets CRAN submission standards prior to integration into a larger analysis pipeline.