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Found 69 Skills
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
Use when designing software architecture for bioinformatics pipelines, defining data structures, planning scalability, or making technical design decisions for complex systems.
Library for bioinformatics and community ecology statistics. Provides data structures and algorithms for sequences, alignments, phylogenetics, and diversity analysis. Essential for microbiome research and ecological data science. Use for alpha/beta diversity metrics, ordination (PCoA), phylogenetic trees, sequence manipulation (DNA/RNA/Protein), distance matrices, PERMANOVA, and community ecology analysis.
Use when implementing production-quality bioinformatics software with proper error handling, logging, testing, and documentation, following software engineering best practices.
Publication-quality bioinformatics figures - phylogenetic trees, genome browsers, iTOL datasets, and data presentation
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology
Patterns for building robust, reproducible genomics analysis pipelines. Covers workflow managers, NGS data processing, variant calling, RNA-seq, and common bioinformatics pitfalls. Use when ", " mentioned.
Use when implementing data analysis pipelines, statistical tests, or bioinformatics workflows in code (Python/R), particularly for genomics, transcriptomics, proteomics, or other -omics data.
Installs 425 bioinformatics skills covering sequence analysis, RNA-seq, single-cell, variant calling, metagenomics, structural biology, and 56 more categories. Use when setting up bioinformatics capabilities or when a bioinformatics task requires specialized skills not yet installed.
Core bioinformatics concepts including SAM/BAM format, AGP genome assembly format, sequencing technologies (Hi-C, HiFi, Illumina), quality metrics, and common data processing patterns. Essential for debugging alignment, filtering, pairing issues, and AGP coordinate validation.