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Found 42 Skills
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.
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.
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.
Use when implementing data analysis pipelines, statistical tests, or bioinformatics workflows in code (Python/R), particularly for genomics, transcriptomics, proteomics, or other -omics data.
Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology
Patterns for building, maintaining, and scaling bioinformatics workflows. Covers Nextflow, Snakemake, WDL/Cromwell, container orchestration, and best practices for reproducible computational biology. Use when ", " mentioned.
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.
Use when designing software architecture for bioinformatics pipelines, defining data structures, planning scalability, or making technical design decisions for complex systems.
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.
Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.
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.