bio-proteomics-proteomics-qc
No SKILL.md available for this skill.
View on GitHubMore from gptomics/bioskills
bio-single-cell-batch-integration
Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets.
5bio-data-visualization-multipanel-figures
Combine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, or matplotlib GridSpec with shared legends and panel labels. Use when combining multiple plots into publication figures.
5bio-data-visualization-specialized-omics-plots
Reusable plotting functions for common omics visualizations. Custom ggplot2/matplotlib implementations of volcano, MA, PCA, enrichment dotplots, boxplots, and survival curves. Use when creating volcano, MA, or enrichment plots.
5bio-data-visualization-genome-tracks
Create genome browser-style visualizations showing multiple data tracks (coverage, peaks, genes) using pyGenomeTracks, Gviz, and IGV. Use when visualizing genomic data at specific loci with multiple aligned tracks.
5bio-metagenomics-kraken
Taxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database. Use when performing initial taxonomic classification of shotgun metagenomic reads before abundance estimation with Bracken.
5bio-pathway-go-enrichment
Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and customizable statistical thresholds.
4