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Remove batch effects from RNA-seq data using ComBat, ComBat-Seq, limma removeBatchEffect, and SVA for unknown batch variables. Use when correcting batch effects in expression data.
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies ...
Generate reproducible bioinformatics/data-science Methods + run documentation from workflow run artifacts (Nextflow/Snakemake/CWL), including a top-of-doc workflow summary, exact executed commands, tool versions, parameters, QC, and outputs.
Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types using reference datasets.
Quality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read characteristics. Use when assessing ONT or PacBio run quality or filtering reads before assembly or alignment.
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.
Count reads per gene from aligned BAM files using Subread featureCounts. Use when processing BAM files from STAR/HISAT2 to generate gene-level counts for DESeq2/edgeR.
Detect transcription factor binding sites through footprinting analysis in ATAC-seq data using TOBIAS. Use when identifying TF occupancy patterns within accessible regions, as TF binding protects DNA from Tn5 cutting.
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.
KEGG pathway and module enrichment analysis using clusterProfiler enrichKEGG and enrichMKEGG. Use when identifying metabolic and signaling pathways over-represented in a gene list. Supports 4000+ organisms via KEGG online database.
Ingest, QC, and map reads with reproducible outputs. Use for raw read processing and coverage stats.
Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters. Use when performing dimensionality reduction and cl...
Cluster proteins into orthogroups and derive pangenome matrices.
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.
---name: rna-velocity-agent description: AI-powered RNA velocity analysis for predicting cellular state transitions, differentiation trajectories, and dynamic gene regulation from single-cell RNA sequencing data. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compa...
---name: multimodal-radpath-fusion-agent description: AI-powered multimodal diagnostic fusion integrating radiology imaging (CT/MRI/PET), digital pathology (WSI), genomics, and clinical data for comprehensive cancer diagnosis and treatment planning. license: MIT metadata: author: AI Group version...
---name: scfoundation-model-agent description: Unified agent for leveraging single-cell foundation models (scGPT, scBERT, Geneformer, scFoundation) for cross-species annotation, perturbation prediction, and gene network inference. license: MIT metadata: author: AI Group version: "1.0.0" created: ...
---name: tumor-clonal-evolution-agent description: AI-powered analysis of tumor clonal architecture, subclonal dynamics, and evolutionary trajectories from multi-region sequencing and longitudinal liquid biopsy data. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" c...
---name: tcr-pmhc-prediction-agent description: AI-powered TCR-peptide-MHC interaction prediction using AlphaFold3 and deep learning for therapeutic TCR discovery, neoantigen validation, and T cell immunogenicity assessment. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-...
---name: molecular-glue-discovery-agent description: AI-powered molecular glue discovery for targeted protein degradation, enabling neo-substrate recruitment and undruggable target degradation through E3 ligase interface modulation. license: MIT metadata: author: AI Group version: "1.0.0" created...
---name: antibody-design-agent description: An advanced agent for de novo antibody design and optimization using state-of-the-art protein language models (MAGE, RFdiffusion). license: MIT metadata: author: VUMC / UW Baker Lab version: "1.0.0" compatibility:
---name: cancer-metabolism-agent description: AI-powered analysis of cancer metabolic reprogramming including Warburg effect, glutamine addiction, lipid metabolism, and metabolic vulnerabilities for therapeutic targeting. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-...
---name: cellfree-rna-agent description: AI-powered cell-free RNA analysis from liquid biopsy for cancer detection, tissue-of-origin identification, and non-invasive transcriptomic profiling. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:
---name: cryoem-ai-drug-design-agent description: AI-powered integration of cryo-EM structural data with generative AI and molecular dynamics for structure-based drug design targeting flexible proteins and membrane complexes. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026...
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