Sample QC Triage
Deterministic multi-sample QC triage for identity, sex, contamination, and batch-shift outliers
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Deterministic multi-sample QC triage for identity, sex, contamination, and batch-shift outliers
Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass...
Constraint-based (COBRA) analysis of genome-scale metabolic models: FBA, FVA, knockouts, flux sampling, production envelopes, gapfilling, media optimization. Use for strain design, essential gene ID, flux analysis. For kinetic modeling use tellurium; for visualization use Escher.
Multi-Omics Factor Analysis v2 (MOFA+) with mofapy2. Jointly decompose omics layers (scRNA, ATAC, proteomics, methylation) into latent factors capturing major variation. Multi-group designs. AnnData views → MOFA object → train → variance explained → correlate factors with metadata → visualize/clu...
This skill should be used when the user asks to "run initial analysis", "analyze single-cell data", "QC my data", "run bioinformatics pipeline", "generate analysis report", "explore my dataset", "do exploratory data analysis", "initial data analysis", or needs to perform quality control, dimensio...
Render a 4-panel regional LocusCompare diagnostic for one (lead variant, exposure study, outcome study) tuple - overlays GWAS Manhattan, QTL Manhattan, GENCODE gene track, and cross-trait scatter colored by LD r². Use when an agent needs visual confirmation that two GWAS / QTL signals share the s...
AI-powered patient digital twin creation for clinical trial simulation, treatment outcome prediction, and personalized medicine using real-world data and multi-omics integration.
AI-powered extracellular vesicle and exosome analysis for cancer biomarker discovery, liquid biopsy applications, and intercellular communication profiling.
AI-powered analysis of patient-derived organoid (PDO) drug screening for personalized oncology treatment selection and biomarker discovery.
AI-powered pan-cancer analysis integrating genomic, transcriptomic, proteomic, and epigenomic data for cancer subtyping, driver identification, and cross-cancer pattern discovery.
Performs GO (Gene Ontology) and KEGG pathway enrichment analysis on\
Comprehensive immune repertoire analysis for T-cell and B-cell receptor sequencing data. Analyze TCR/BCR repertoires to assess clonality, diversity, V(D)J gene usage, CDR3 characteristics, convergence, and predict epitope specificity. Integrate with single-cell data for clonotype-phenotype associ...
AI-powered PROTAC (Proteolysis Targeting Chimera) design for targeted protein degradation, integrating ternary complex prediction, linker optimization, and ADMET modeling.
Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis.
Graph-RAG Solver
Build digital twin patient models to test drug efficacy and toxicity
Beautify meta-analysis forest plots with customizable odds ratio points
Auto-annotate cell clusters from single-cell RNA data using marker genes.
Design multi-omics integration strategies for transcriptomics, proteomics,
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.
---name: nicheformer-spatial-agent description: Foundation model-powered spatial transcriptomics analysis leveraging 53M+ spatially resolved cells for cellular architecture modeling and tissue niche discovery. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-20" compatib...
---name: prs-net-deep-learning-agent description: Geometric deep learning-based polygenic risk score prediction using PRS-Net for modeling gene interactions, enhanced disease prediction, and cross-ancestry portability. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-20"...
AI-powered analysis of T-cell exhaustion states, epigenetic scarring, stem-like T-cell populations, and checkpoint blockade response prediction in cancer immunotherapy.
AI-powered analysis of cellular senescence for aging research, cancer therapy response, and senolytic drug development.
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