Skill UI

Provides programmatic access to the CZ CELLxGENE Census (61M+ cells) for querying standardized single-cell expression, metadata, embeddings, and stats across tissues and diseases, supporting scanpy and ML workflows.

Programmatic access to the COSMIC cancer mutation database for downloading mutations, Cancer Gene Census, mutational signatures, fusions, copy number, expression, and resistance data to fuel oncology research or bioinformatics pipelines after authentication.

PyDESeq2 brings the DESeq2 differential expression workflow to Python, letting users load bulk RNA-seq counts, specify design formulas, fit DESeq2 models, run Wald tests with FDR correction, optionally apply apeGLM shrinkage, and export gene-level statistics within pandas/AnnData pipelines.

Query the Reactome Content and Analysis Services (or the reactome2py client) to fetch pathway hierarchies, map genes, run overrepresentation/expression analyses, and explore disease interactions for systems biology research.

Probabilistic deep-learning framework built on PyTorch for single-cell omics; handles batch correction, integration, multimodal and spatial data, differential expression, and transfer learning with unified APIs.

Query the GTEx portal and API to retrieve tissue-specific gene expression, eQTL, sQTL, and variant-level regulatory data for interpreting GWAS loci and exploring gene regulation across 54 human tissues.

This skill assists developers in understanding, writing, and validating EAS workflow YAML files for Expo projects. It provides comprehensive guidance on CI/CD pipelines, job definitions, triggers, and expression syntax, ensuring configurations are accurate and up-to-date by referencing official Expo schemas. Use this when automating builds or deployments via EAS.

Infers candidate gene regulatory networks from bulk or single-cell expression data using GRNBoost2 and GENIE3 algorithms. Ranks transcription factor-target associations with Dask-compatible parallel execution, sparse expression support, and network stability validation for biological research applications.

Programmatically query the CZ CELLxGENE Census for versioned single-cell and spatial transcriptomics data, including population-scale cell metadata, gene expression slices, summary counts, source H5AD URIs, embeddings, and reference atlas comparisons.

gget is a command-line and Python package providing unified access to over 20 genomic databases and analysis methods. It supports querying gene information, sequence analysis, protein structures, viral sequences, expression data, and disease associations. Suitable for interactive exploration and simple queries, offering both CLI and Python function interfaces for bioinformatics workflows.

Perform differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.

scvi-tools is a comprehensive Python framework for probabilistic deep generative models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it enables batch correction, transfer learning, differential expression analysis with uncertainty, and multi-modal data integration. Best for advanced modeling of single-cell RNA-seq, ATAC-seq, CITE-seq, spatial transcriptomics, and specialized modalities like methylation and cytometry.