5.838 Skills
End-to-end neoantigen discovery from somatic variants to ranked vaccine candidates. Integrates HLA typing, MHC binding prediction, pVACtools neoantigen calling, and immunogenicity scoring. Use when identifying tumor neoantigens for personalized vaccine design or checkpoint biomarkers.
R-based single cell analysis using Seurat v5 for .rds/.rdata files
Local-only regression patterns for hostname resolution in socket tests
Use when analyzing Rspack/Webpack bundles from local `rsdoctor-data.json` and producing evidence-based optimization recommendations.
Protein structure analysis skill. PDB structure retrieval, structural alignment (TM-align), binding site analysis, and structure-function relationship exploration.
scATAC-seq Signac skill. Single-cell ATAC-seq analysis with Signac/ArchR, peak calling, motif enrichment, chromatin accessibility clustering, and gene activity scoring.
scVI integration skill. Deep generative model-based single-cell analysis with scvi-tools, batch correction, cell type annotation, and multi-modal data integration.
Variant interpretation skill. ACMG/AMP variant classification, pathogenicity evidence aggregation, clinical significance assessment, and variant report generation.
UniProt proteome skill. UniProt protein sequence/annotation retrieval, proteome-wide queries, functional annotation extraction, and protein feature analysis.
Spatial transcriptomics skill. Visium/MERFISH/Slide-seq data analysis, spatial gene expression mapping, spatial clustering, and tissue region deconvolution.
Single-cell genomics skill. scRNA-seq analysis (Scanpy/Seurat), cell clustering, trajectory inference, cell type annotation, and gene regulatory network inference.
Non-coding RNA skill. miRNA target prediction, lncRNA functional annotation, ncRNA expression analysis, small RNA-seq processing, and RNA secondary structure prediction.
Protein interaction network skill. STRING/BioGRID PPI data integration, interaction confidence scoring, network topology analysis, and protein complex identification.
Spatial multi-omics skill. Spatially resolved transcriptomics + proteomics integration, spatial niche identification, and multi-modal spatial data analysis.
Population genetics skill. Allele frequency analysis, Hardy-Weinberg testing, Fst/neutrality statistics, population structure (ADMIXTURE/PCA), and demographic inference.
MERRA-2 再分析数据完整参考：变量名、气溶胶产品、访问方式
Time series data structures and diagnostics in R. Use when working with ts, zoo, xts objects, autocorrelation, or handling irregular time series.
Generate personalized treatment plans based on pharmacogenomics, variant interpretation, and patient-specific data. Integrates ClinPGx and ClinVar databases for evidence-based precision therapeutics.
PharmGKB pharmacogenomics skill. PharmGKB clinical annotation queries, drug-gene interaction lookup, dosing guideline retrieval, and pharmacogenomic pathway analysis.
Phylogenetics skill. Phylogenetic tree construction (ML/Bayesian), multiple sequence alignment, divergence time estimation, and evolutionary analysis pipelines.
Pharmacogenomics skill. Genotype-drug response associations, pharmacogenomic variant annotation, dosing guideline integration, and PGx biomarker analysis.
Protein domain and family skill. InterPro/Pfam domain annotation, protein family classification, domain architecture analysis, and functional domain prediction.
Full de novo structure elucidation - skip dereplication and solve the structure from NMR correlations. Use when dereplication returned no matches, the compound is known to be novel, or you want to solve the structure from first principles.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
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.
Titan bio-data integration status
Pathway enrichment skill. GSEA, ORA, KEGG/Reactome/WikiPathways enrichment, gene set analysis, leading-edge analysis, and pathway crosstalk identification.
Open Targets Genetics skill. GWAS trait-gene associations, L2G scoring, colocation analysis, and variant-to-gene mapping from Open Targets Genetics portal.
rRNA taxonomy skill. 16S/18S/ITS rRNA-based taxonomic classification, OTU/ASV analysis, taxonomic database queries, and microbial diversity profiling.
Microbiome and metagenomics skill. 16S rRNA analysis, shotgun metagenomics, diversity metrics, taxonomic profiling, functional metagenomics, and microbiome association studies.