Data & Analytics

Manual and automated gating for defining cell populations in flow cytometry. Covers rectangular, polygon, and data-driven gates. Use when identifying cell populations through hierarchical gating strategies.

Differential abundance and state analysis for cytometry data. Compare cell populations between conditions using statistical methods. Use when testing for significant changes in cell frequencies or marker expression between groups.

Spillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for analysis.

Unsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.

Bead-based normalization for CyTOF and high-parameter flow cytometry. Covers EQ bead normalization, signal drift correction, and batch normalization. Use when correcting instrument drift in CyTOF or harmonizing data across batches.

--> --- name: bio-expression-matrix-metadata-joins description: Merge sample metadata with count matrices and add gene annotations. Use when preparing data for differential expression analysis or visualization. tool_type: mixed primary_tool: pandas measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-experimental-design-sample-size description: 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. tool_type: r primary_tool: ssizeRNA measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_she...

--> --- name: bio-epitranscriptomics-m6a-differential description: Identify differential m6A methylation between conditions from MeRIP-seq. Use when comparing epitranscriptomic changes between treatment groups or cell states. tool_type: r primary_tool: exomePeak2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

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.

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.

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.

--> --- name: bio-data-visualization-upset-plots description: 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. tool_type: mixed primary_tool: UpSetR measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-data-visualization-interactive-visualization description: 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. tool_type: mixed primary_tool: plotly measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-data-visualization-circos-plots description: 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. tool_type: mixed primary_tool: Circos measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Circular genome visualizations for displaying m...

JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens) for modeling sgRNA efficacy and gene essentiality. Use when analyzing multiple CRISPR screens simultaneously or when accounting for variable sgRNA efficiency across experiments.

Call copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.

--> --- name: bio-comparative-genomics-synteny-analysis description: Analyze genome collinearity and syntenic blocks using MCScanX, SyRI, and JCVI for comparative genomics. Detect conserved gene order, chromosomal rearrangements, and whole-genome duplications. Use when comparing genome structure between species or identifying conserved genomic regions. tool_type: mixed primary_tool: MCScanX measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-to...

ChIP-seq peak calling using MACS3 (or MACS2). Call narrow peaks for transcription factors or broad peaks for histone modifications. Supports input control, fragment size modeling, and various output formats including narrowPeak and broadPeak BED files. Use when calling peaks from ChIP-seq alignments.

Annotate ChIP-seq peaks to genomic features and genes using ChIPseeker. Assign peaks to promoters, exons, introns, and intergenic regions. Find nearest genes and calculate distance to TSS. Generate annotation plots and statistics. Use when annotating ChIP-seq peaks to genomic features.

De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences.

Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.

Estimate causal effects between exposures and outcomes using genetic variants as instrumental variables with TwoSampleMR. Implements IVW, MR-Egger, weighted median, and MR-PRESSO methods for robust causal inference from GWAS summary statistics. Use when testing whether an exposure causally affects an outcome using genetic instruments.

Test whether two traits share a causal variant at a genomic locus using Bayesian colocalization with coloc. Computes posterior probabilities for shared vs distinct causal variants between GWAS and eQTL signals. Use when determining if a GWAS signal and an eQTL share the same causal variant.

Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.