Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions.
Systematic clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Aggregates evidence from ClinVar, gnomAD, CIViC, UniProt, and PDB across ACMG criteria. Produces pathogenicity scores (0-100), clinical recommendations, and treatment implications. Use when interpreting genetic variants, classifying variants of uncertain significance (VUS), performing ACMG variant classification, or translating variant calls to clinical actionability.
Comprehensive functional annotation of protein variants — pathogenicity, population frequency, structural context, and clinical significance. Integrates ProtVar (map_variant, get_function, get_population) for protein-level mapping and structural context, ClinVar for clinical classifications, gnomAD for population frequency with ancestry data, CADD for deleteriousness scores, and ClinGen for gene-disease validity. Produces a structured variant annotation report with evidence grading. Use when asked about protein variant impact, missense variant pathogenicity, ProtVar annotation, variant functional context, or combining population and structural evidence for a variant.
End-to-end variant-to-mechanism analysis: given a genetic variant (rsID or coordinates), trace its functional impact from regulatory context (GWAS, eQTL, RegulomeDB, ENCODE) through target gene identification (GTEx, OpenTargets L2G) to downstream pathway and disease biology (STRING, Reactome, GO enrichment, disease associations). Produces an evidence-graded mechanistic narrative linking genotype to phenotype. Use when asked "how does this variant cause disease?", "what is the mechanism of rs7903146?", "trace variant to pathway", or "connect this GWAS hit to biology".
Production-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions. Parses VCF files (streaming, large files), classifies mutation types (missense, nonsense, synonymous, frameshift, splice, intronic, intergenic) and structural variants (deletions, duplications, inversions, translocations), applies VAF/depth/quality/consequence filters, annotates with ClinVar/dbSNP/gnomAD/CADD via ToolUniverse, interprets SV/CNV clinical significance using ClinGen dosage sensitivity scores, computes variant statistics, and generates reports. Solves questions like "What fraction of variants with VAF < 0.3 are missense?", "How many non-reference variants remain after filtering intronic/intergenic?", "What is the pathogenicity of this deletion affecting BRCA1?", or "Which dosage-sensitive genes overlap this CNV?". Use when processing VCF files, annotating variants, filtering by VAF/depth/consequence, classifying mutations, interpreting structural var
Integrate structural biology data with proteomics for drug target validation. Retrieves protein structures from PDB (RCSB, PDBe), AlphaFold predictions, antibody structures (SAbDab), GPCR data (GPCRdb), binding pocket analysis (ProteinsPlus), and ligand interactions (BindingDB). Use when asked to find structures for a drug target, identify binding site ligands, cross-validate drug binding with structural data, assess structural druggability, or compare experimental vs predicted structures.
Comprehensive structural variant (SV) analysis skill for clinical genomics. Classifies SVs (deletions, duplications, inversions, translocations), assesses pathogenicity using ACMG-adapted criteria, evaluates gene disruption and dosage sensitivity, and provides clinical interpretation with evidence grading. Use when analyzing CNVs, large deletions/duplications, chromosomal rearrangements, or any structural variants requiring clinical interpretation.
Analyze spatial transcriptomics data to map gene expression in tissue architecture. Supports 10x Visium, MERFISH, seqFISH, Slide-seq, and imaging-based platforms. Performs spatial clustering, domain identification, cell-cell proximity analysis, spatial gene expression patterns, tissue architecture mapping, and integration with single-cell data. Use when analyzing spatial transcriptomics datasets, studying tissue organization, identifying spatial expression patterns, mapping cell-cell interactions in tissue context, characterizing tumor microenvironment spatial structure, or integrating spatial and single-cell RNA-seq data for comprehensive tissue analysis.
Research stem cells, iPSCs, organoids, and cell differentiation using ToolUniverse tools. Covers pluripotency marker identification, differentiation pathway analysis, organoid model characterization, cell type annotation, and disease modeling. Integrates CellxGene/HCA for single-cell atlas data, CellMarker for cell type markers, GEO for stem cell datasets, and pathway tools for differentiation signaling. Use when asked about stem cells, iPSCs, organoids, cell reprogramming, pluripotency, differentiation protocols, or 3D culture models.
Regulatory variant interpretation -- GWAS association lookup, eQTL analysis, chromatin state annotation, regulatory element overlap, and trait ontology resolution. Connects GWAS Catalog, GTEx, ENCODE, RegulomeDB, OpenTargets, OLS ontology, and Ensembl regulatory features. Use when users ask about non-coding variants, GWAS hits, eQTLs, regulatory elements, enhancer/promoter variants, or trait-associated SNPs.
Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.
Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type statistical analysis, gene-expression correlation, batch correction (Harmony), trajectory inference, and cell-cell communication analysis. NEW: Analyzes ligand-receptor interactions between cell types using OmniPath (CellPhoneDB, CellChatDB), scores communication strength, identifies signaling cascades, and handles multi-subunit receptor complexes. Integrates with ToolUniverse gene annotation tools (HPA, Ensembl, MyGene, UniProt) and enrichment tools (gseapy, PANTHER, STRING). Supports h5ad, 10X, CSV/TSV count matrices, and pre-annotated datasets. Use when analyzing single-cell RNA-seq data, studying cell-cell interactions, performing cell type differential expression, computing gene-expression correlations by cell type, analyzing tumor-immune communicatio
Predict and analyze protein 3D structure from amino acid sequence using ESMFold and AlphaFold. Covers de novo structure prediction (ESMFold for sequences up to ~800 residues), AlphaFold model retrieval, quality assessment (pLDDT scores), experimental structure comparison (RCSB), variant structural impact (ProtVar), and sequence physicochemical property calculation (ProtParam). Use when asked to predict protein structure from sequence, assess structure quality, compare predictions to experimental structures, or evaluate how mutations affect protein structure.
Rare disease genomics research -- disease identification via Orphanet, causative gene discovery, gene-disease validity assessment via GenCC, pathogenic variant lookup via ClinVar, HPO phenotype mapping, epidemiology and prevalence data, clinical trial search, and literature review. Use when users ask about rare diseases, orphan diseases, genetic causes of rare conditions, Orphanet codes, HPO phenotypes, gene-disease validity, rare disease prevalence, or treatment options for rare genetic disorders.
Analyze protein-protein interaction networks using STRING, BioGRID, and SASBDB databases. Maps protein identifiers, retrieves interaction networks with confidence scores, performs functional enrichment analysis (GO/KEGG/Reactome), and optionally includes structural data. No API key required for core functionality (STRING). Use when analyzing protein networks, discovering interaction partners, identifying functional modules, or studying protein complexes.
Investigate transcription factor binding, cis-regulatory elements, chromatin accessibility, and regulatory variant annotation. Use when asked about TF binding sites, enhancers, promoters, ChIP-seq data, ATAC-seq signals, candidate cis-regulatory elements (cCREs), or the regulatory impact of genomic variants.
Production-ready phylogenetics and sequence analysis skill for alignment processing, tree analysis, and evolutionary metrics. Computes treeness, RCV, treeness/RCV, parsimony informative sites, evolutionary rate, DVMC, tree length, alignment gap statistics, GC content, and bootstrap support using PhyKIT, Biopython, and DendroPy. Performs NJ/UPGMA/parsimony tree construction, Robinson-Foulds distance, Mann-Whitney U tests, and batch analysis across gene families. Integrates with ToolUniverse for sequence retrieval (NCBI, UniProt, Ensembl) and tree annotation. Use when processing FASTA/PHYLIP/Nexus/Newick files, computing phylogenetic metrics, comparing taxa groups, or answering questions about alignments, trees, parsimony, or molecular evolution.
Research plant genes, pathways, and species using PlantReactome, Ensembl Plants, POWO, UniProt, KEGG, and literature tools. Covers plant pathway analysis, gene function annotation, species identification, crop genomics, and comparative plant biology. Use when asked about plant genes, Arabidopsis, crop improvement, plant pathways, plant metabolism, photosynthesis, plant development, or plant species identification.
Connect GWAS variants to biological pathways for drug target discovery. Maps disease-associated SNPs to causal genes via eQTL colocalization (GTEx), links genes to enriched pathways (Reactome, KEGG, MetaCyc), and identifies druggable targets within disease-relevant pathways. Use when asked to translate GWAS findings into mechanistic insights, find pathways enriched for disease genes, discover drug targets from genetic evidence, or answer questions like "What pathways are disrupted in type 2 diabetes based on GWAS data?"
Guide pharmacogenomics (PGx) research -- drug-gene interaction lookup, CPIC guideline retrieval, variant-drug annotation, allele function status, FDA biomarker labeling, and clinical dosing recommendations. Covers the full CPIC-to-PharmGKB-to-clinical-recommendation workflow. Use when users ask about pharmacogenomics, drug-gene interactions, CPIC guidelines, genotype-guided dosing, PGx biomarkers, CYP enzyme phenotypes, or star allele interpretation.
Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Calculates genetic risk profiles, interprets PRS percentiles, and assesses disease predisposition across conditions including type 2 diabetes, coronary artery disease, and Alzheimer's disease. Use when asked to calculate polygenic risk scores, interpret genetic risk for complex diseases, build custom PRS from GWAS data, or answer questions like "What is my genetic predisposition to breast cancer?"
