bioinformatics-workflows

Trigger Boundary

Common bioinformatics workflows across databases, single-cell analysis, genomics I/O, and omics analysis.

Use this aggregate Codex App skill when the task matches one of the source workflows below.

Source Workflows

• bioinformatics-database-retrieval: Use for gene, protein, pathway, variant, expression, disease, and structure database lookups. Prefer workflow-specific skills for scanpy, scvi-tools, pysam, or DICOM work. Reference: _src/db/source.md
• scanpy: Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata. Reference: _src/scanpy/source.md
• scvi-tools: Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy. Reference: _src/scvi/source.md
• anndata: Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census. Reference: _src/anndata/source.md
• pysam: Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines. Reference: _src/pysam/source.md
• pydeseq2: Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis. Reference: _src/deseq/source.md
• biopython: Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices. Reference: _src/biopy/source.md
• scikit-bio: Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis. Reference: _src/skbio/source.md
• polars-bio: High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for BED/VCF/BAM/GFF intervals. Streaming, cloud-native, faster bioframe alternative. Reference: _src/polars/source.md
• tiledbvcf: Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics. Reference: _src/vcf/source.md

Workflow

1. Choose the source workflow whose trigger boundary best matches the user request.
2. Read that source workflow's source.md before acting.
3. Load only the needed files under that workflow's copied references, scripts, assets, or evals.
4. Follow the source workflow unless the current project gives stricter instructions.