Anthropic Skills

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.

Create UpSet plots to visualize set intersections as an alternative to Venn diagrams using UpSetR or upsetplot. Use when comparing overlapping gene sets, peak sets, or sample groups with more than 3 sets.

Create publication-ready volcano plots with custom thresholds, gene labels, and highlighting using ggplot2, EnhancedVolcano, or matplotlib. Use when visualizing differential expression or association results with gene annotations.

Expert patterns for advanced queries, Redis caching, and database scalability.

Exports publication-ready figures in various formats with proper resolution, sizing, and typography. Use when preparing figures for journal submission, creating vector graphics for presentations, or ensuring consistent figure styling across analyses.

Creates reproducible Jupyter notebooks for bioinformatics analysis with parameterization using papermill. Use when generating automated analysis reports, running notebook-based pipelines, or creating shareable computational notebooks.

Generates standardized quality control reports by aggregating metrics from FastQC, alignment, and other tools using MultiQC. Use when summarizing QC metrics across samples, creating shareable quality reports, or building automated QC pipelines.

Applies multiple testing correction methods including FDR, Bonferroni, and q-value for genomics data. Use when filtering differential expression results, setting significance thresholds, or choosing between correction methods for different study designs.

Calculates statistical power and minimum sample sizes for RNA-seq, ATAC-seq, and other sequencing experiments. Use when planning experiments, determining how many replicates are needed, or assessing whether a study is adequately powered to detect expected effect sizes.

Estimates required sample sizes for differential expression, ChIP-seq, methylation, and proteomics studies. Use when budgeting experiments, writing grant proposals, or determining minimum replicates needed to achieve statistical significance for expected effect sizes.

Perform differential expression analysis using DESeq2 in R/Bioconductor. Use for analyzing RNA-seq count data, creating DESeqDataSet objects, running the DESeq workflow, and extracting results with log fold change shrinkage. Use when performing DE analysis with DESeq2.

Detect A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices. Use when identifying A/B compartments from Hi-C data.

Compare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps. Use when comparing Hi-C contacts between conditions.

Create interactive HTML plots with plotly and bokeh for exploratory data analysis and web-based sharing of omics visualizations. Use when building zoomable, hoverable plots for data exploration or web dashboards.

Visualize Hi-C contact matrices, TADs, loops, and genomic features using matplotlib, cooltools, and HiCExplorer. Create triangle plots, virtual 4C, and multi-track figures. Use when visualizing contact matrices or genomic features.

Create publication-quality scientific figures with ggplot2 including scatter plots, boxplots, heatmaps, and multi-panel layouts. Use when creating static figures for papers, presentations, or reports in R.

Create circular genome visualizations with Circos and pyCircos. Display multi-track data including ideograms, genes, variants, CNVs, and interaction arcs. Use when creating circular genome visualizations.

Extract, filter, annotate, and export differential expression results from DESeq2 or edgeR. Use for identifying significant genes, applying multiple testing corrections, adding gene annotations, and preparing results for downstream analysis. Use when filtering and exporting DE analysis results.

Visualize differential expression results using DESeq2/edgeR built-in functions. Covers plotMA, plotDispEsts, plotCounts, plotBCV, sample distance heatmaps, and p-value histograms. Use when visualizing differential expression results.

Perform differential expression analysis using edgeR in R/Bioconductor. Use for analyzing RNA-seq count data with the quasi-likelihood F-test framework, creating DGEList objects, normalization, dispersion estimation, and statistical testing. Use when performing DE analysis with edgeR.

Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.

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.

Compute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.

Statistical testing for differentially abundant proteins between conditions. Covers limma and MSstats workflows with multiple testing correction. Use when identifying proteins with significant abundance changes between experimental groups.

Create clustered heatmaps with row/column annotations using ComplexHeatmap, pheatmap, and seaborn for gene expression and omics data visualization. Use when visualizing expression patterns across samples or identifying co-expressed gene clusters.

Create reproducible bioinformatics analysis reports with R Markdown including code, results, and visualizations in HTML, PDF, or Word format. Use when generating analysis reports with RMarkdown.

Visualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expression patterns.

Create publication-quality visualizations of immune repertoire data including circos plots, clone tracking, diversity plots, and network graphs. Use when generating figures for repertoire comparisons, clonal dynamics, or V(D)J gene usage.

Perform differential expression analysis of miRNAs between conditions using DESeq2 or edgeR with small RNA-specific considerations. Use when identifying miRNAs that change between treatment groups, disease states, or developmental stages.

Map metabolites to biological pathways using KEGG, Reactome, and MetaboAnalyst. Perform pathway enrichment and topology analysis. Use when interpreting metabolomics results in the context of biochemical pathways.

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.

Visualize enrichment results using enrichplot package functions. Use when creating publication-quality figures from clusterProfiler results. Covers dotplot, barplot, cnetplot, emapplot, gseaplot2, ridgeplot, and treeplot.

Visualize ChIP-seq data using deepTools, Gviz, and ChIPseeker. Create heatmaps, profile plots, and genome browser tracks. Visualize signal around peaks, TSS, or custom regions. Use when visualizing ChIP-seq signal and peaks.

Gene Set Enrichment Analysis using clusterProfiler gseGO and gseKEGG. Use when analyzing ranked gene lists to find coordinated expression changes in gene sets without arbitrary significance cutoffs. Detects subtle but coordinated expression changes.

Visualize copy number profiles, segments, and compare across samples. Create publication-quality plots of CNV data from CNVkit, GATK, or other callers. Use when creating genome-wide CNV plots, sample heatmaps, or chromosome-level visualizations.

Visualize metagenomic profiles using R (phyloseq, microbiome) and Python (matplotlib, seaborn). Create stacked bar plots, heatmaps, PCA plots, and diversity analyses. Use when creating publication-quality figures from MetaPhlAn, Bracken, or other taxonomic profiling output.

Use when investigating slow queries, analyzing execution plans, or optimizing database performance. Invoke for index design, query rewrites, configuration tuning, partitioning strategies, lock contention resolution.

Use when building Apache Spark applications, distributed data processing pipelines, or optimizing big data workloads. Invoke for DataFrame API, Spark SQL, RDD operations, performance tuning, streaming analytics.

Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation, missing value handling, groupby operations, or performance optimization.

Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.