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bioinformatics

1.2K

tooluniverse-variant-interpretation

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.

mims-harvard

research

bioinformatics

1.2K

tooluniverse-variant-analysis

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

mims-harvard

research

bioinformatics

1.2K

tooluniverse-spatial-transcriptomics

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.

mims-harvard

research

scientific-computing

1.2K

tooluniverse-sequence-retrieval

Retrieves biological sequences (DNA, RNA, protein) from NCBI and ENA with gene disambiguation, accession type handling, and comprehensive sequence profiles. Creates detailed reports with sequence metadata, cross-database references, and download options. Use when users need nucleotide sequences, protein sequences, genome data, or mention GenBank, RefSeq, EMBL accessions.

mims-harvard

research

bioinformatics

1.2K

tooluniverse-regulatory-variant-analysis

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.

mims-harvard

research

bioinformatics

1.2K

tooluniverse-single-cell

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

mims-harvard

research

scientific-computing

1.2K

tooluniverse-protein-structure-retrieval

Retrieves protein structure data from RCSB PDB, PDBe, and AlphaFold with protein disambiguation, quality assessment, and comprehensive structural profiles. Creates detailed structure reports with experimental metadata, ligand information, and download links. Use when users need protein structures, 3D models, crystallography data, or mention PDB IDs (4-character codes like 1ABC) or UniProt accessions.

mims-harvard

research

bioinformatics

1.2K

tooluniverse-multiomic-disease-characterization

Comprehensive multi-omics disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers for systems-level understanding. Produces a detailed multi-omics report with quantitative confidence scoring (0-100), cross-layer gene concordance analysis, biomarker candidates, therapeutic opportunities, and mechanistic hypotheses. Uses 80+ ToolUniverse tools across 8 analysis layers. Use when users ask about disease mechanisms, multi-omics analysis, systems biology of disease, biomarker discovery, or therapeutic target identification from a disease perspective.

mims-harvard

research

bioinformatics

1.2K

tooluniverse-population-genetics-1000genomes

Population genetics research using the 1000 Genomes Project (IGSR) -- search populations by superpopulation ancestry (AFR, AMR, EAS, EUR, SAS), retrieve samples by population code, list available data collections, and integrate with GWAS tools for population stratification analysis. Use when users ask about 1000 Genomes populations, sample ancestry, allele frequency variation across continental groups, population-specific GWAS interpretation, or IGSR data collections like the 30x high-coverage resequencing or HGSVC.

mims-harvard

research

bioinformatics

1.2K

tooluniverse-spatial-omics-analysis

Computational analysis framework for spatial multi-omics data integration. Given spatially variable genes (SVGs), spatial domain annotations, tissue type, and disease context from spatial transcriptomics/proteomics experiments (10x Visium, MERFISH, DBiTplus, SLIDE-seq, etc.), performs comprehensive biological interpretation including pathway enrichment, cell-cell interaction inference, druggable target identification, immune microenvironment characterization, and multi-modal integration. Produces a detailed markdown report with Spatial Omics Integration Score (0-100), domain-by-domain characterization, and validation recommendations. Uses 70+ ToolUniverse tools across 9 analysis phases. Use when users ask about spatial transcriptomics analysis, spatial omics interpretation, tissue heterogeneity, spatial gene expression patterns, tumor microenvironment mapping, tissue zonation, or cell-cell communication from spatial data.

mims-harvard

research

bioinformatics

1.2K

tooluniverse-gwas-trait-to-gene

Discover genes associated with diseases and traits using GWAS data from the GWAS Catalog (500,000+ associations) and Open Targets Genetics (L2G predictions). Identifies genetic risk factors, prioritizes causal genes via locus-to-gene scoring, and assesses druggability. Use when asked to find genes associated with a disease or trait, discover genetic risk factors, translate GWAS signals to gene targets, or answer questions like "What genes are associated with type 2 diabetes?"

mims-harvard

research

bioinformatics

1.2K

tooluniverse-gwas-snp-interpretation

Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple databases (GWAS Catalog, Open Targets Genetics, ClinVar). Retrieves variant annotations, GWAS trait associations, fine-mapping evidence, locus-to-gene predictions, and clinical significance. Use when asked to interpret a SNP by rsID, find disease associations for a variant, assess clinical significance, or answer questions like "What diseases is rs429358 associated with?" or "Interpret rs7903146".

mims-harvard

research

computational-chemistry

1.2K

tooluniverse-admet-prediction

Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling of drug candidates using ADMETAI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, and PubChem properties. Generates a structured ADMET scorecard with pass/fail verdicts per category. Use when asked about drug-likeness, ADMET properties, bioavailability, toxicity prediction, BBB penetration, CYP interactions, pharmacokinetic profiling, Lipinski rule of five, or ADME/PK assessment of a compound.

mims-harvard

research

computational-chemistry

1.2K

tooluniverse-chemical-safety

Comprehensive chemical safety and toxicology assessment integrating ADMET-AI predictions, CTD toxicogenomics, FDA label safety data, DrugBank safety profiles, and STITCH chemical-protein interactions. Performs predictive toxicology (AMES, DILI, LD50, carcinogenicity), organ/system toxicity profiling, chemical-gene-disease relationship mapping, regulatory safety extraction, and environmental hazard assessment. Use when asked about chemical toxicity, drug safety profiling, ADMET properties, environmental health risks, chemical hazard assessment, or toxicogenomic analysis.

mims-harvard

research

computational-chemistry

1.2K

tooluniverse-chemical-sourcing

Find commercial sources for chemical compounds using ZINC, Enamine, eMolecules, and Mcule. Covers compound identification, vendor search, pricing, analog discovery, and order preparation. Use when buying compounds, checking commercial availability, comparing vendors, or finding purchasable analogs.

mims-harvard

research

computational-chemistry

1.2K

tooluniverse-clinical-trial-matching

AI-driven patient-to-trial matching for precision medicine and oncology. Given a patient profile (disease, molecular alterations, stage, prior treatments), discovers and ranks clinical trials from ClinicalTrials.gov using multi-dimensional matching across molecular eligibility, clinical criteria, drug-biomarker alignment, evidence strength, and geographic feasibility. Produces a quantitative Trial Match Score (0-100) per trial with tiered recommendations and a comprehensive markdown report. Use when oncologists, molecular tumor boards, or patients ask about clinical trial options for specific cancer types, biomarker profiles, or post-progression scenarios.

mims-harvard

research

computational-chemistry

1.2K

tooluniverse-drug-drug-interaction

Comprehensive drug-drug interaction (DDI) prediction and risk assessment. Analyzes interaction mechanisms (CYP450, transporters, pharmacodynamic), severity classification, clinical evidence grading, and provides management strategies. Supports single drug pairs, polypharmacy analysis (3+ drugs), and alternative drug recommendations. Use when users ask about drug interactions, medication safety, polypharmacy risks, or need DDI assessment for clinical decision support.

mims-harvard

research

computational-chemistry

1.2K

tooluniverse-protein-therapeutic-design

Design novel protein therapeutics (binders, enzymes, scaffolds) using AI-guided de novo design. Uses RFdiffusion for backbone generation, ProteinMPNN for sequence design, ESMFold/AlphaFold2 for validation. Use when asked to design protein binders, therapeutic proteins, or engineer protein function.

mims-harvard

research

computational-chemistry

1.2K

tooluniverse-rare-disease-diagnosis

Provide differential diagnosis for patients with suspected rare diseases based on phenotype and genetic data. Matches symptoms to HPO terms, identifies candidate diseases from Orphanet/OMIM, prioritizes genes for testing, interprets variants of uncertain significance. Use when clinician asks about rare disease diagnosis, unexplained phenotypes, or genetic testing interpretation.

mims-harvard

research

computational-chemistry

1.2K

tooluniverse-small-molecule-discovery

Find, characterize, and source small molecules for chemical biology and drug discovery. Covers compound identification (PubChem, ChEMBL), structure search, binding affinity data, ADMET/drug-likeness prediction, and commercial availability (eMolecules, Enamine). Use when asked to find compounds, assess drug-likeness, search by structure, retrieve binding affinities, or source chemicals.

mims-harvard

research

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