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1. ▌
   Tooluniverse Peptide Target Deorphanization 2 · mims-harvard bundle

   Find the real protein target(s) of a peptide from its sequence — peptide target deorphanization / off-target identification, for ANY target class (GPCR, ion channel, protease, cytokine/growth-factor receptor, enzyme, integrin), not only GPCRs. Use when a peptide has a phenotype but does not bind its hypothesized target, when a peptide binds a target in one species or assay but not another, or to screen candidate targets for an orphan peptide. A target-class router steers a multi-route keyless pipeline (PROSITE/ELM motif, BLAST homology, HGNC/InterPro/GPCRdb/GtoPdb target-family enumeration, OpenTargets phenotype anchor, EnsemblCompara/Alliance cross-species reconciliation) plus optional NVIDIA-NIM co-folding (Boltz2, AlphaFold2-Multimer, OpenFold3) for structural confirmation.
   8 installs
2. ▌
   Tooluniverse Population Genetics 1000genomes 2 · mims-harvard

   Population genetics using the 1000 Genomes Project (IGSR) — superpopulation/population search, sample metadata, variant frequencies across AFR/AMR/EAS/EUR/SAS, ancestry-specific analyses. Use for ancestry comparison, population-aware allele frequency lookups, and 1000-Genomes-cohort-specific analyses (distinct from gnomAD which has different sample composition).
   11 installs
3. ▌
   Tooluniverse Variant Predictor Dms Validation 2 · mims-harvard

   Validate a variant-effect predictor (AlphaMissense, ESM-C SAE, ESM logits, EVE, conservation scores, or any per-variant numeric score) against experimental deep mutational scanning (DMS) data. Computes per-variant predictor scores, splits variants into neutral vs disruptive groups by DMS effect, runs a Mann-Whitney U test on the predictor scores, and sweeps the stratification thresholds for robustness. Use when you need to know whether a predictor's scores track real functional disruption on a specific protein.
   4 installs
4. ▌
   Tooluniverse Immunotherapy Response Prediction 2 · mims-harvard bundle

   Predict patient response to immune checkpoint inhibitors (ICIs) by integrating tumor mutational burden (TMB), microsatellite instability (MSI), PD-L1 expression, HLA status, and immune-related gene expression. Outputs ICI Response Score with drug-specific recommendations and resistance-risk assessment. Use for melanoma/NSCLC/RCC immunotherapy decision support.
   5 installs
5. ▌
   Tooluniverse Microbial Genome Characterization 2 · mims-harvard

   Genome-ASSEMBLY discovery, QC, and replicon mapping for any organism (bacteria, archaea, fungi, and beyond) using NCBI Datasets. Resolves an organism name or taxid to assemblies, picks the reference/representative or best-quality assembly, pulls assembly QC metrics (total length, contig/scaffold N50, contig count, GC%, assembly level, RefSeq category), enumerates chromosomes and plasmids via per-replicon sequence reports, and compares candidate assemblies on quality. Use for "what genomes are available for [organism]", "assembly stats / N50 / GC content for [GCF_/GCA_ accession]", "how many plasmids does [strain] have", "compare assemblies for [species]", "find the reference genome for [taxon]", "is this assembly Complete Genome or just contigs". NOT for gene-level orthology/synteny (use tooluniverse-comparative-genomics), plant gene structure (use tooluniverse-plant-genomics), de novo assembly from raw reads (no tool exists), or taxonomy-only name/lineage lookups.
   10 installs
6. ▌
   Tooluniverse Precision Medicine Stratification 2 · mims-harvard bundle

   Patient stratification for precision medicine — integrate genomic, clinical, and therapeutic data to split patients into responder/non-responder groups, risk tiers, or treatment-decision groups. Use for stratification-by-biomarker, treatment-selection logic, and personalized therapeutic strategy reports per patient subgroup.
   8 installs
7. ▌
   Tooluniverse Protein Structural Annotation Pdb 2 · mims-harvard

   Given a PDB structure, produce a per-residue annotation table: which residues sit at a binding interface (vs a partner chain), which line a ligand pocket, which are buried (core) vs solvent-exposed (surface), and optionally secondary structure. This is the structural track drawn under a DMS heatmap and the structural prior SAE feature drops are read against. Use when you need to anchor a variant-interpretation or DMS analysis to the protein's actual physical context.
   9 installs
8. ▌
   Tooluniverse Multiomic Disease Characterization 2 · mims-harvard bundle

   Comprehensive disease characterization across genomics, transcriptomics, proteomics, and pathways for systems-level understanding. Identifies therapeutic opportunities and biomarker candidates by integrating multi-layer molecular data. Use for full-omics disease deep-dive reports, mechanism mapping, and biomarker-and-target identification from multi-omics data.
   7 installs
9. ▌
   Tooluniverse Protein Sae Variant Interpretation 2 · mims-harvard

   Interpret a missense variant via ESMC-6B Sparse Autoencoder (SAE) feature activations. For a given protein + variant, computes which interpretable SAE features (catalytic, ligand-binding, PTM, structural motif, domain, etc.) are lost or gained at the mutation site. Use when standard pathogenicity scores (AlphaMissense, ClinVar) say a variant is damaging but you need a MECHANISTIC explanation — e.g. 'why is this variant LoF?' Complements (does not replace) variant-interpretation and variant-to-mechanism skills, which focus on ACMG classification or regulatory mechanism.
   5 installs
10. ▌
    Tooluniverse Residue Functional Mechanism Interpretation 2 · mims-harvard

    Given a set of residues in a protein, explain WHY they are functionally critical by combining structural context (binding interface, ligand pocket, core, secondary structure), UniProt features (active sites, binding sites, PTM sites, disulfides), optional SAE feature evidence, and optional DMS data. Accepts residues from any source: DMS hotspots (top-K by max effect), ClinVar recurrent variants, literature-reported hot regions, evolutionarily conserved positions, or user-curated lists. Returns a per-cluster mechanism call: catalytic / ligand-binding / interface / structural-core / PTM / regulatory / unknown.
    7 installs
11. ▌
    Tooluniverse 3 · mims-harvard bundle

    ToolUniverse plugin router. STEP 1 BEFORE ANY ANALYSIS: if the data folder contains `*_executed.ipynb`, run `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` to extract its cell outputs and apply EVERY filter/sample-exclusion the notebook used — even when the question says 'Using DESeq2/Run X/Compute Y' (this describes the METHOD the notebook used, not a request to rerun). The notebook's cell outputs are the only published authoritative answers; reimplementing or reading stale pre-computed CSVs in the data folder produces different numbers because of outlier-sample removal, library version, and filter steps you don't see by skimming. STEP 2 routing — pick a sub-skill name from this exact list (never invent): tooluniverse-rnaseq-deseq2 (RNA/miRNA-seq DE, correlation, PCA, clustering, dispersion), tooluniverse-gene-enrichment (GO/KEGG/Reactome/GSEA/pathway enrichment), tooluniverse-statistical-modeling (regression, ANOVA, ordinal/logistic, chi-square, correlation, power), toolunive
    4 installs
12. ▌
    Tooluniverse Molecular Cloning 2 · mims-harvard bundle

    Molecular Cloning Assembly Design (Gibson & Golden Gate)
    8 installs
13. ▌
    Tooluniverse Phylogenetics 3 · mims-harvard bundle

    Phylogenetic analysis — de novo multiple sequence alignment (Clustal Omega/MUSCLE/MAFFT via EBI_msa_align) and neighbour-joining/UPGMA tree building (EBI_build_phylogenetic_tree) from your own sequences, plus tree analysis, treeness, saturation (PhyKIT), parsimony-informative sites, alignment gap analysis, DVMC, long-branch detection, BUSCO orthologs. Uses PhyKIT, Biopython, DendroPy. Use to align a set of sequences, build a tree from sequences or an alignment, or for phylogenetic tree QC, multi-gene phylogenomics, evolutionary-rate analysis, and comparative-genomics studies.
    12 installs
14. ▌
    Tooluniverse Metabolomics Analysis 2 · mims-harvard bundle

    Analyze metabolomics data end-to-end — metabolite identification, quantification (TIC normalization, batch correction), differential analysis, and pathway interpretation. Use for processing mass-spec metabolomics output, normalization choice, untargeted metabolomics workflows, and integrating with other omics layers.
    9 installs
15. ▌
    Tooluniverse Metagenomics Analysis 2 · mims-harvard

    Microbiome and metagenomics analysis using MGnify, GTDB taxonomy, ENA sequencing data, and EuropePMC literature. Covers taxonomic classification, genome quality assessment, biome-clinical phenotype linkage, and pathway interpretation. Use for amplicon/shotgun metagenomics study analysis.
    6 installs
16. ▌
    Tooluniverse Protein Lof Mechanism 2 · mims-harvard

    Propose the mechanism by which a missense variant causes loss-of-function (LoF), synthesizing evidence from 5 independent layers: AlphaMissense pathogenicity, AlphaFold structural context, ESMC sequence likelihood, SAE feature disruption, and DynaMut2 stability ΔΔG. Distinguishes 'structural stability LoF' (mis-folding) from 'direct functional disruption' (catalytic / binding / PTM site damage). Use for coding missense variants where you need a mechanistic causal model, not just a pathogenicity score.
    10 installs
17. ▌
    Tooluniverse Rare Disease Genomics 2 · mims-harvard

    Rare disease genomics — disease identification (Orphanet), causative gene discovery, gene-disease validity (GenCC), variant interpretation (ClinVar), and translational research (ClinicalTrials.gov, drug repurposing for orphans). Use for rare-disease-gene curation, novel-gene-discovery analysis, and rare-disease drug-development support.
    8 installs
18. ▌
    Tooluniverse Structural Proteomics 2 · mims-harvard

    Structural biology plus proteomics integration for drug target validation. Combines PDB experimental structures, AlphaFold predictions, GPCRdb, SAbDab antibody structures, ProteinsPlus binding-site prediction, and BindingDB ligand-affinity data. Use for druggability assessment, binding-site characterization, ligand-pocket analysis, structural-confidence scoring (resolution, pLDDT), and antibody-target interface analysis.
    8 installs
19. ▌
    Tooluniverse Crispr Screen Analysis 2 · mims-harvard bundle

    Analyze CRISPR-Cas9 genetic screens — MAGeCK gene-level scores, sgRNA count QC, replicate correlation, hit prioritization, and pathway GSEA on screen output. Use for genome-wide essentiality screens, synthetic-lethality discovery, dropout vs positive-selection screen analysis, target identification, and resistance-screen interpretation. Includes screen-QC and statistical thresholds.
    7 installs
20. ▌
    Tooluniverse Drug Target Validation 2 · mims-harvard bundle

    Quantitative drug-target validation pipeline. Scores druggability, selectivity, safety profile, ADMET feasibility, and structural tractability with a composite Target Validation Score (0-100) and GO/NO-GO recommendation. Use for go/no-go decisions on a target before commit-to-medchem, target prioritization across a list, and target-deselection rationale.
    7 installs
21. ▌
    Tooluniverse Rare Disease Diagnosis 2 · mims-harvard bundle

    Rare disease differential diagnosis from patient phenotype — HPO term matching to candidate diseases (Orphanet, OMIM), gene panel prioritization, ACMG variant interpretation, and structure-based variant analysis. Use for diagnostic odyssey assistance, phenotype-to-disease ranking, and genetic-counseling differential generation.
    9 installs
22. ▌
    Tooluniverse Chemical Compound Retrieval 2 · mims-harvard bundle

    Retrieve chemical compound data from PubChem and ChEMBL with disambiguation, cross-referencing, and stereochemistry handling. Use for resolving compound names to SMILES/InChI/CID/ChEMBL IDs (including OPSIN deterministic IUPAC-name-to-structure parsing), fetching molecular properties, distinguishing isomers/stereo forms, and cross-validating identity across databases. Always use English compound names; flags ambiguous queries (e.g., Vitamin D has multiple forms).
    4 installs
23. ▌
    Tooluniverse Functional Genomics Screens 2 · mims-harvard

    Interpret hits from CRISPR-KO/CRISPRi/shRNA screens by integrating DepMap essentiality, gnomAD constraint scores, pathway context (Reactome, STRING), druggability (DGIdb), and clinical evidence (CIViC, COSMIC). Use for screen-hit prioritization, essentiality ranking, and turning a list of screen hits into a prioritized target shortlist.
    9 installs
24. ▌
    Tooluniverse Product Safety Surveillance 2 · mims-harvard bundle

    Post-market safety surveillance and recall/adverse-event RETRIEVAL across the full spectrum of FDA-regulated products that are NOT covered by the drug-AE signal skills: medical devices, food / dietary supplements / cosmetics, veterinary drugs, and drug supply (shortages). Orchestrates openFDA endpoints (MAUDE device adverse events + device recalls + 510(k), CAERS food/supplement/ cosmetic adverse events, veterinary adverse events, drug shortages, and cross-product enforcement/recall reports). USE WHEN the user asks: "are there adverse events for [device / pacemaker / infusion pump / insulin pump]", "device recalls for [firm/product]", "supplement / vitamin / cosmetic adverse reactions", "is [drug] in shortage", "what injectables are on shortage", "veterinary / animal adverse events for [drug] in [dog/cat/horse]", "food recall for listeria", "MAUDE report for [device]", "CAERS reactions for [brand]". DO NOT USE for drug adverse-event SIGNAL detection or disproportionality (PRR / ROR /...
    6 installs
25. ▌
    Tooluniverse Protein Structure Retrieval 2 · mims-harvard bundle

    Protein structure retrieval from RCSB PDB, PDBe, and AlphaFold with disambiguation, quality assessment (resolution, R-factor, pLDDT), and metadata. Distinguishes high-quality experimental (X-ray under 2 Angstrom) vs predicted vs medium-quality structures. Use for fetching protein structures, structure-quality comparison, and selecting structures for drug design or modeling.
    6 installs
26. ▌
    Tooluniverse Regulatory Variant Analysis 2 · mims-harvard

    Non-coding/regulatory variant interpretation — GWAS association lookup, eQTL evidence (GTEx), chromatin state (ENCODE), regulatory variant scoring (RegulomeDB, CADD), TF-binding disruption, and sequence-based deep-learning prediction (AlphaGenome/AlphaGenome Atlas) for when annotation databases are silent. Use for non-coding GWAS hit interpretation, eQTL-based gene assignment, and regulatory mechanism reasoning. Distinct from coding-variant tools.
    8 installs
27. ▌
    Tooluniverse Structural Variant Analysis 2 · mims-harvard bundle

    Structural variant (SV) clinical interpretation: deletions, duplications, inversions, translocations, complex rearrangements. Applies ACMG-adapted criteria with ClinGen HI/TS dosage scores, gnomAD frequencies, and ClinVar evidence. Produces 5-tier classification with explicit per-criterion evidence. Use for clinical genomics SV review, dosage-sensitivity assessment, breakpoint analysis, and CNV pathogenicity calls. Gene-dosage-driven reasoning.
    6 installs
28. ▌
    Tooluniverse Gpcr Structural Pharmacology 2 · mims-harvard

    GPCR receptor pharmacology — agonist/antagonist/inverse-agonist/biased-agonist classification, GPCRdb structural data, receptor-ligand binding analysis, antibody-target interface (SAbDab). Use for GPCR drug discovery, biased-agonism analysis, receptor subtype selectivity questions, and orthosteric vs allosteric pocket characterization.
    6 installs
29. ▌
    Tooluniverse Inorganic Physical Chemistry 2 · mims-harvard bundle

    Inorganic chemistry, physical chemistry, and materials science — crystal structures, coordination chemistry, lattice parameters, thermodynamic properties, electronic structure. Use for unit cell volume calculations, coordination geometry, materials property estimation, and inorganic-mechanism reasoning. Complementary to tooluniverse-organic-chemistry.
    5 installs
30. ▌
    Tooluniverse Protein Structure Prediction 2 · mims-harvard

    Protein 3D structure prediction from sequence — ESMFold de novo prediction, AlphaFold database retrieval, experimental structures from RCSB, ProtVar variant impact assessment, ProtParam sequence properties. Use for structure prediction when no experimental structure exists, fold-confidence scoring, and structure-guided variant interpretation.
    6 installs
31. ▌
    Tooluniverse Cancer Variant Interpretation 2 · mims-harvard bundle

    Clinical interpretation of somatic cancer mutations for precision oncology. Transforms a gene + variant + cancer-type input into an actionable report: clinical evidence tier (CIViC, OncoKB), therapeutic options (FDA-approved + investigational), resistance mechanisms, prognosis, and matching clinical trials. Use for tumor-board variant calls, somatic-mutation actionability assessment, and treatment selection. Always cancer-type-specific.
    11 installs
32. ▌
    Tooluniverse Natural Product Dereplication 2 · mims-harvard

    Dereplicate a putative natural product and assign its chemical taxonomy. Use to answer "is [compound] a known natural product", "what microbe/organism produces [compound]", "what chemical class is [compound]", "dereplicate this metabolite (by formula/exact mass/InChIKey/SMILES)", or "classify this molecule into ChemOnt". Searches NPAtlas for known microbial natural products (producing organism + literature reference), assigns the ChemOnt kingdom→superclass→class→subclass hierarchy via ClassyFire, resolves systematic IUPAC names to structure via OPSIN, and cross-references identity in PubChem. NOT for general drug/compound identity or ADMET (use tooluniverse-chemical-compound-retrieval / tooluniverse-small-molecule-discovery) and NOT for metabolomics pathway/enrichment analysis (use tooluniverse-metabolomics skills).
    6 installs
33. ▌
    Tooluniverse Protein Modification Analysis 2 · mims-harvard

    Post-translational modification (PTM) analysis — phosphorylation, ubiquitination, acetylation, glycosylation, methylation. Uses iPTMnet (sites + enzymes), ProtVar (functional consequences), UniProt (baseline), STRING, ELM (linear motifs), MassIVE/ProteomeXchange (experimental). Use for PTM site annotation, kinase-substrate identification, and PTM-disease associations.
    7 installs
34. ▌
    Tooluniverse Variant Functional Annotation 2 · mims-harvard

    Functional annotation of protein variants — ProtVar structural/functional context, ClinVar clinical classifications, gnomAD population frequencies, CADD deleteriousness, ClinGen gene-disease validity, plus FAVOR one-call comprehensive GRCh38 annotation. Use for variant annotation pipelines, missense effect prediction, and protein-level variant interpretation with functional context.
    10 installs
35. ▌
    Tooluniverse Sdk 2 · mims-harvard bundle

    Build AI scientist systems with the ToolUniverse Python SDK for scientific research. Covers the 3 calling patterns (`tu.run` portable dict API, `tu.tools.X` function API, direct class instantiation), tool loading, batch execution, MCP server integration, and embedding-based tool search. Use for SDK programming, custom tool composition, benchmarking pipelines, and integrating ToolUniverse into research workflows.
    7 installs
36. ▌
    Setup Tooluniverse 2 · mims-harvard bundle

    Install and configure ToolUniverse for any use case — MCP server (chat-based), CLI (command line with 14 subcommands), or Python SDK (Coding API with 3 calling patterns). Covers uv/uvx setup, MCP configuration for 12+ AI clients (Cursor, Claude Desktop, Windsurf, VS Code, Codex, Gemini CLI, Trae, Cline, etc.), full CLI reference (tu list/grep/info/find/run/test/status/build/remote/doctor/serve/connect/connections/disconnect), Coding API quickstart, agentic tools, code executor, API key walkthrough, skill installation, and upgrading. Use when user asks how to set up ToolUniverse, which access mode to use (MCP vs CLI vs SDK), configuring MCP servers, using the CLI, troubleshooting installation, upgrading, or mentions installing ToolUniverse or setting up scientific tools. Also triggers for "how do I use ToolUniverse", "what's the best way to access tools", "command line", "tu command", "coding API", "tu build".
    7 installs
37. ▌
    Tooluniverse Biomedical Fact Lookup 3 · mims-harvard

    Answer biomedical FACTUAL / recall / multiple-choice questions by querying ToolUniverse database tools instead of answering from memory. Triggers on any 'which gene/drug/variant/disease/pathway/miRNA/TF...' lookup, any question phrased 'according to <database>' (DisGeNet, OMIM, MSigDB, miRDB, GTRD, MGI, Ensembl, ClinVar, ChEMBL, OpenTargets, Reactome, GtoPdb, UniProt...), and multiple-choice biology/medicine knowledge questions where one option must be verified against an authoritative source. NOT for analyzing user-supplied data files (CSV/VCF/h5ad → use the data-analysis router) and NOT for open-ended literature synthesis. Use whenever a single correct answer exists in a public biomedical database and could be looked up rather than guessed.
    7 installs
38. ▌
    Tooluniverse Immunology 2 · mims-harvard

    Immunology research workflows: antibody-antigen interactions, T/B cell repertoire, MHC/HLA binding prediction, autoimmune disease genetics, vaccine epitope mapping. Uses IEDB, IMGT, SAbDab, UniProt. Use for adaptive immunity questions, immune response analysis, antibody/TCR/BCR characterization, immunogenicity prediction, and immune-pathway-to-disease mapping.
    11 installs
39. ▌
    Tooluniverse Lipidomics 2 · mims-harvard

    Lipid analysis and lipid-disease associations using LIPID MAPS classification, HMDB metabolite data, KEGG/Reactome lipid pathways (sphingolipid, eicosanoid, steroid, fatty acid), and PubChem chemical info. Use for lipid identification, lipid metabolism pathway mapping, and lipid-associated disease analysis (cardiovascular, diabetes, NAFLD).
    7 installs
40. ▌
    Tooluniverse Toxicology 2 · mims-harvard

    Drug and chemical toxicity assessment via adverse outcome pathways (AOPs), real-world FAERS adverse event signals, FDA labels, and toxicogenomic associations. Triangulates molecular initiating event to cellular outcome to organ-level toxicity to clinical adverse event. Use for hepatotoxicity/cardiotoxicity/nephrotoxicity prediction and toxicology reports.
    10 installs
41. ▌
    Tooluniverse Custom Tool 2 · mims-harvard bundle

    Add custom local tools to ToolUniverse alongside the 1000+ built-in tools. Covers JSON-config tools (simplest, no code), Python class tools (REST/SOAP/GraphQL APIs, computational logic), and best-practices for return schemas. Use for wrapping new APIs, adding domain-specific computations, or contributing tools to the registry.
    7 installs
42. ▌
    Tooluniverse Network Pharmacology 2 · mims-harvard bundle

    Compound-target-disease network construction and analysis for drug repurposing, polypharmacology discovery, and multi-target drug design. Uses STRING, BioGRID, ChEMBL, DGIdb, OMIM, OpenTargets. Use for off-target effect prediction, network-based drug repurposing, and identifying molecules with desired multi-target profile.
    8 installs
43. ▌
    Tooluniverse Polygenic Risk Score 2 · mims-harvard bundle

    Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Covers PRS construction (clumping/thresholding, PRS-CS), validation in independent cohorts, ancestry-aware adjustment, and clinical interpretation (population-relative risk, not absolute prediction). Use for PRS-based risk stratification.
    4 installs
44. ▌
    Tooluniverse Protein Interactions 2 · mims-harvard bundle

    Protein-protein interaction (PPI) network analysis — STRING (predicted + experimental), BioGRID (curated), SASBDB (small-angle scattering). Distinguishes physical interactions (binding) from functional associations (co-expression, co-regulation). Use for interactome queries, complex partner identification, and pathway-level interaction analysis.
    4 installs
45. ▌
    Tooluniverse Statistical Modeling 2 · mims-harvard bundle

    Statistical modeling — linear/logistic/ordinal/Poisson regression, ANOVA, Kruskal-Wallis, chi-square, Mann-Whitney, Cox survival, spline fits (R `ns()`), odds ratios, Cohen's d, F-statistic, p-value computation. Specializes in clinical-trial AE analysis (SDTM DM/AE), severity ordinal regression, and per-feature stat workflows.
    11 installs
46. ▌
    Tooluniverse Variant To Mechanism 2 · mims-harvard

    End-to-end variant-to-mechanism analysis — trace a variant (rsID/coordinates) through regulatory context, target gene(s), molecular pathway(s), and phenotypic consequences. Integrates 7+ databases across 3 evidence layers (regulatory, molecular, disease) for a mechanistic model. Use for GWAS-hit-to-mechanism, eQTL-causal-gene tracing, and full causal-chain reports.
    7 installs
47. ▌
    Tooluniverse Cancer Classification 2 · mims-harvard

    Translate free-text tumor descriptions to OncoTree codes and resolve cancer subtypes/tissue hierarchy. Cross-references UMLS/NCI vocabularies. Use for standardizing cancer-type nomenclature in EHR free-text, building cohorts in OncoKB or GDC, mapping tumor-board notes to ontology codes, and ensuring consistent terminology across cancer-genomics pipelines.
    5 installs
48. ▌
    Tooluniverse Clinical Risk Scoring 2 · mims-harvard

    Compute and interpret validated bedside clinical risk scores and pretest probabilities for an INDIVIDUAL patient — pick the right score for the scenario, gather inputs, run the deterministic calculator tool, and read the result against an interpretation table. Covers CHA2DS2-VASc (AF stroke risk), HAS-BLED (bleeding on anticoagulation), CURB-65 (pneumonia severity / admit decision), qSOFA (sepsis screen), Child-Pugh + MELD-Na (cirrhosis severity / transplant priority), Wells DVT and Wells PE (VTE pretest probability), ASCVD (10-year cardiovascular risk / statin decision), and eGFR CKD-EPI (kidney function / drug dosing). Use when asked things like "stroke risk for this AF patient", "should this patient be anticoagulated", "pneumonia severity — admit or not?", "sepsis screen this patient", "DVT/PE pretest probability", "10-year cardiovascular risk", "cirrhosis severity / MELD score", or "eGFR / kidney function". Pairs CHA2DS2-VASc with HAS-BLED to weigh anticoagulation. NOT for...
    6 installs
49. ▌
    Tooluniverse Clinical Trial Design 2 · mims-harvard bundle

    Strategic clinical trial design feasibility assessment. Analyzes 6 dimensions (endpoint, population, comparator, effect size, duration, regulatory pathway) using precedent trials and FDA guidance. Produces enrollment projections, endpoint recommendations, and approval-pathway analysis. Use for trial-protocol design, power/sample-size estimation, comparator selection, and FDA submission strategy. Driven by precedent-based reasoning rather than first-principles math.
    9 installs
50. ▌
    Tooluniverse Drug Drug Interaction 2 · mims-harvard bundle

    Assess drug-drug interactions — CYP metabolic interactions (substrate/inhibitor/inducer), transporter (P-gp, BCRP, OATP) effects, pharmacodynamic synergy/antagonism, clinical significance scoring, and management recommendations. Use for polypharmacy review, prescribing decision support, and safety analysis when adding or switching drugs.
    8 installs
51. ▌
    Tooluniverse Epigenomics Chromatin 2 · mims-harvard

    Histone-modification ChIP-seq, ATAC-seq accessibility, chromatin state, and TF binding analysis from ENCODE, Roadmap Epigenomics, ChIP-Atlas. Use for chromatin-state-by-tissue queries, TF-binding-by-region, regulatory landscape mapping, and ENCODE-cCRE annotations. For DNA methylation use tooluniverse-epigenomics; for RNA-seq use tooluniverse-rnaseq-deseq2.
    8 installs
52. ▌
    Tooluniverse Chemical Sourcing 2 · mims-harvard

    Find commercial sources for chemical compounds — PubChem/ChEMBL identity resolution then vendor catalog search across ZINC, Enamine, eMolecules, Mcule. Compares pricing, availability, and identifies purchasable analogs when an exact compound is not in stock. Use for chemical procurement, virtual library curation, and 'where can I buy X' questions for synthesis planning.
    6 installs
53. ▌
    Tooluniverse Dataset Discovery 2 · mims-harvard

    Find and evaluate research datasets for any scientific question. Maps research questions to required study designs (longitudinal vs cross-sectional, observational vs experimental, single-cohort vs multi-cohort). Use when the user asks 'find data about X', 'where can I get data on Y', or needs a specific cohort/survey/repository. Covers GEO, ArrayExpress, dbGaP, NHANES, UK Biobank, ClinicalTrials.gov, GWAS Catalog, and 30+ scientific repositories.
    10 installs
54. ▌
    Tooluniverse Kegg Disease Drug 2 · mims-harvard

    KEGG-based disease-drug-variant network research. Connects diseases to causal genes, drugs to molecular targets, and variants to pathways using KEGG's editorially curated databases (KEGG Disease, Drug, Network, Variant, Pathway). Use for drug repurposing via shared pathways, mechanistic disease-gene-drug networks, and pathway-based target discovery. Distinguishes direct (binding) vs indirect (pathway co-membership) drug-target relationships.
    10 installs
55. ▌
    Tooluniverse Organic Chemistry 2 · mims-harvard bundle

    Organic chemistry reasoning guide for reaction product prediction, mechanism analysis (electrophilic/nucleophilic substitution, addition, elimination, pericyclic, radical), and spectroscopy interpretation (1H/13C NMR, IR, MS). Reasons from first principles (electron flow, kinetic vs thermodynamic) rather than pattern-matching named reactions. Use for organic synthesis problems and mechanism explanations.
    9 installs
56. ▌
    Tooluniverse Pharmacovigilance 2 · mims-harvard bundle

    Drug safety and adverse event analysis — FAERS spontaneous-report mining, FDA black-box warnings, signal detection (PRR, ROR, IC), risk factors by demographic/comorbidity, and label change tracking. Use for post-market safety surveillance, AE signal investigation, drug-AE association strength scoring, and pharmacovigilance reports.
    5 installs
57. ▌
    Tooluniverse Sequence Analysis 2 · mims-harvard bundle

    Biological sequence analysis — gene/protein sequence retrieval (NCBI, Ensembl, UniProt), nucleotide/protein search, ortholog discovery, and FASTQ QC + alignment workflows (Trimmomatic, BWA, samtools, coverage depth). Use for sequence retrieval, sequence comparison, FASTQ QC analysis, and read alignment pre-processing.
    5 installs
58. ▌
    Tooluniverse Gwas Trait To Gene 2 · mims-harvard bundle

    Discover causal genes for diseases/traits from GWAS data using Open Targets L2G (locus-to-gene) scoring — integrates eQTL, chromatin interaction, and distance evidence. Use for trait-to-gene mapping, drug-target hypothesis generation from GWAS, and replacing the 'nearest gene' heuristic with multi-evidence L2G scores.
    4 installs
59. ▌
    Tooluniverse Hla Immunogenomics 2 · mims-harvard

    HLA gene-family analysis and MHC-peptide binding for transplant compatibility, vaccine epitope coverage, and cancer immunotherapy. Uses IMGT (HLA polymorphism), IEDB (epitope-MHC binding), UniProt (annotation), DGIdb (druggability). Use for HLA typing/imputation review, vaccine HLA coverage, and immunotherapy prediction biomarkers (HLA-LOH, neoantigen presentation).
    10 installs
60. ▌
    Tooluniverse Infectious Disease 2 · mims-harvard bundle

    Rapid pathogen characterization and drug repurposing for outbreaks. Combines pathogen genomics (NCBI, BVBRC), host immune response (IEDB), drug-target databases (ChEMBL, DGIdb), and literature surveillance (PubMed/EuropePMC). Use for emerging-pathogen profiling, antiviral candidate identification, and outbreak intelligence reporting.
    7 installs
61. ▌
    Tooluniverse Precision Oncology 2 · mims-harvard bundle

    Cancer treatment recommendations from molecular profile (mutations + cancer type + biomarkers) — FDA-approved + investigational therapies, resistance mechanisms, matching clinical trials, prognosis. Uses CIViC, ClinVar, OpenTargets, ClinicalTrials.gov. Use for tumor-board treatment recommendations, evidence-tiered actionability assessment, and FDA-precedent-driven therapy selection.
    10 installs
62. ▌
    Tooluniverse Stem Cell Organoid 2 · mims-harvard

    Stem cell, iPSC, and organoid research — pluripotency markers, differentiation protocol pathways, lineage commitment factors, organoid model selection. Use for iPSC characterization, differentiation protocol design via developmental-pathway recapitulation, and organoid-model selection for disease modeling.
    3 installs
63. ▌
    Tooluniverse Cell Line Profiling 2 · mims-harvard bundle

    Cancer cell-line selection and profiling for experimental model choice. Cross-references DepMap, Cellosaurus, COSMIC, PharmacoDB to deliver identity verification, mutation/CNV profile, gene dependencies, drug sensitivities, and druggable targets. Use to answer 'which cell line should I use for studying gene X?' or 'is this cell line a good model for cancer Y?'. Outputs ranked recommendations with rationale, growth characteristics, and known pitfalls.
    8 installs
64. ▌
    Tooluniverse Spatial Omics Analysis 2 · mims-harvard bundle

    Spatial multi-omics interpretation pipeline. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into biological insights via domain-by-domain characterization, cell-type composition, spatial gene expression patterns, RNA+protein+metabolite integration. Use for Visium, MERFISH, seqFISH, Slide-seq, spatial proteomics, and spatial multi-omics interpretation. Goes beyond statistics to disease mechanisms and therapeutic opportunities.
    8 installs
65. ▌
    Tooluniverse Variant Interpretation 2 · mims-harvard bundle

    Clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Use for VUS classification, pathogenicity assessment with cited criteria, structure-based variant impact (AlphaFold/PDB), non-coding/regulatory variant effect prediction with sequence deep-learning models (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2), and producing clinical-grade variant reports for return of results or molecular tumor boards. Use this whenever a user asks about a variant's significance, an intronic/promoter/enhancer/UTR non-coding variant's functional impact, or needs ACMG classification — even if they don't say "ACMG".
    6 installs
66. ▌
    Tooluniverse Adverse Event Detection 2 · mims-harvard bundle

    Detect and analyze adverse drug event signals using FDA FAERS reports, drug labels, and disproportionality statistics (PRR, ROR, IC). Generates quantitative safety signal scores (0-100) with evidence grading. Use for post-market surveillance, pharmacovigilance, drug safety assessment, regulatory submissions, and detecting rare AE signals not visible in clinical trials.
    7 installs
67. ▌
    Tooluniverse Adverse Outcome Pathway 2 · mims-harvard

    Map environmental and industrial chemicals to adverse outcome pathways (AOPs) — molecular initiating event to organ-level toxicity. Uses AOPWiki, GHS classification, IARC carcinogen status, and LD50 data. Use for environmental/industrial chemical risk assessment, regulatory-grade hazard characterization, and AOP stressor mapping. Distinct from drug-safety analysis (use tooluniverse-pharmacovigilance for drugs).
    6 installs
68. ▌
    Tooluniverse Clinical Trial Matching 2 · mims-harvard bundle

    AI-driven patient-to-trial matching for precision oncology and rare-disease care. Transforms a patient's molecular profile (mutations, biomarkers, expression) and clinical state into ranked clinical-trial recommendations with evidence tiers. Searches ClinicalTrials.gov, the EU CTIS register (European/EEA trials), AND the ISRCTN registry (UK/international) plus cross-references CIViC, OpenTargets, ChEMBL, and FDA labels. Use for matching patients to trials by genotype, biomarker-driven trial selection, trial-eligibility scoring, and finding trials across the US, Europe, and the UK.
    5 installs
69. ▌
    Tooluniverse Drug Mechanism Research 2 · mims-harvard

    Trace drug mechanism of action — primary target → downstream signaling → pathway perturbation → tissue/organ effect → clinical outcome. Uses DrugBank, ChEMBL, KEGG, Reactome, STRING. Use for understanding how a drug works, identifying off-target effects, mechanism-based combination therapy design, and writing mechanism sections of reports.
    4 installs
70. ▌
    Tooluniverse Gwas Snp Interpretation 2 · mims-harvard bundle

    Interpret a single GWAS SNP across multiple databases — GWAS Catalog hits, LD/haplotype context, eQTL evidence, regulatory annotation, ClinVar pathogenicity, gnomAD frequency. Use for 'what does this SNP do', SNP-to-mechanism tracing, and resolving lead-SNP-vs-causal-variant ambiguity. Always considers LD structure before claiming a SNP is mechanistically responsible.
    4 installs
71. ▌
    Tooluniverse Mendelian Randomization 2 · mims-harvard

    Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y, whether an observational association is actually causal or just correlation, if a biomarker/trait is a causal risk factor, wants to triangulate epidemiology against genetic evidence, or mentions Mendelian randomization, instrumental-variable analysis, two-sample MR, or genetic causal evidence — even if they never say "MR" (e.g. "is LDL cholesterol actually causal for heart disease?", "does BMI cause type 2 diabetes or just correlate?", "is CRP a causal driver of stroke?"). Covers trait-label resolution, MR effect direction/magnitude, instrument quality (MOE score), method agreement (IVW vs MR-Egger vs weighted median), bidirectional MR for reverse causation, and distinguishing causation from genetic correlation. Not for plain GWAS...
    6 installs
72. ▌
    Tooluniverse Model Organism Genetics 2 · mims-harvard

    Cross-species genetic analysis using model organism databases (MGI mouse, ZFIN zebrafish, FlyBase fruit fly, WormBase worm, SGD yeast, RGD rat, GBIF taxonomy). Maps human genes to orthologs, retrieves phenotype/expression/functional data, assesses gene function conservation, and identifies the best animal model for studying a human gene or disease.
    12 installs
73. ▌
    Tooluniverse Multi Omics Integration 2 · mims-harvard bundle

    Multi-omics integration — orchestrate per-layer analysis (transcriptomics, proteomics, epigenomics, genomics, metabolomics) then perform cross-omics correlation, multi-omics clustering, and pathway-level integration. Use for integrative systems-biology analysis, multi-modal disease characterization, and cross-omics biomarker discovery.
    4 installs
74. ▌
    Tooluniverse Spatial Transcriptomics 2 · mims-harvard bundle

    Spatial transcriptomics analysis — Visium, MERFISH, seqFISH, Slide-seq. Maps gene expression to tissue architecture, identifies spatially variable genes (SVGs), tissue-domain segmentation, and cell-cell interaction inference. Use for spatial gene-expression questions, tissue architecture analysis, and SVG identification.
    7 installs
75. ▌
    Tooluniverse Computational Biophysics 2 · mims-harvard bundle

    Solve quantitative problems in biophysics — pharmacokinetics (PK volume of distribution, clearance, half-life), epidemiology (R0, attack rate), toxicology (LD50, NOAEL), population genetics (Hardy-Weinberg, Fst), enzyme kinetics (Michaelis-Menten), thermodynamics. Use for first-principles quantitative biology calculations, dose calculations, exposure assessment, and biophysical-property estimation.
    4 installs
76. ▌
    Tooluniverse Clinical Guidelines 2 · mims-harvard bundle

    Search and retrieve clinical practice guidelines from 12+ authoritative sources — NICE, WHO, NCCN, AHA, ADA, SIGN, USPSTF, IDSA, NIH consensus, ESMO/ESC/EASL European societies, and US specialty associations. Use for evidence-graded treatment recommendations, dosing protocols, screening guidance, and authoritative-source-prioritized clinical guidance (NICE/WHO ranked above society guidelines).
    5 installs
77. ▌
    Tooluniverse Electron Microscopy 2 · mims-harvard

    Search and analyze electron microscopy data — cryo-EM density maps (EMDB), fitted atomic models (PDB), raw micrograph datasets (EMPIAR), and cryo-electron tomography volumes (CryoET Data Portal). Use for finding 3D structural data on a protein/complex, comparing experimental EM resolution to AlphaFold confidence, and accessing raw EM data for re-processing.
    10 installs
78. ▌
    Tooluniverse Gwas Drug Discovery 2 · mims-harvard bundle

    Transform GWAS signals into drug targets and repurposing opportunities. Connects GWAS-significant loci to causal genes via fine-mapping/eQTL, then to druggable proteins via DGIdb/OpenTargets, then to existing drugs via ChEMBL. Use for GWAS-to-target hypothesis generation, druggable-fraction analysis of disease loci, and human-genetics-validated drug-repurposing prioritization.
    9 installs
79. ▌
    Tooluniverse Gwas Study Explorer 2 · mims-harvard bundle

    Compare GWAS studies, perform meta-analyses across cohorts, and assess signal replication. Uses GWAS Catalog metadata, study-level statistics, and cross-cohort comparison. Use for evaluating GWAS reproducibility for a trait, meta-analysis sample size and effect-size aggregation, and detecting study heterogeneity (population, design, ancestry).
    7 installs
80. ▌
    Tooluniverse Microbiome Research 2 · mims-harvard

    Microbiome research using MGnify, GTDB, ENA, OLS (ENVO biomes), and EuropePMC. Covers study discovery, taxonomic profiling, host-microbe interaction analysis, and biome-by-condition queries. Use for microbiome study selection, organism-environment associations, and clinical-microbiome literature review. Distinct from analytical workflow (use tooluniverse-metagenomics-analysis for that).
    3 installs
81. ▌
    Tooluniverse Population Genetics 2 · mims-harvard bundle

    Population genetics analysis — allele frequencies (gnomAD, 1000 Genomes), Hardy-Weinberg equilibrium testing, Fst between populations, GWAS associations, evolutionary constraint scores. Use for cross-population variant comparison, ancestry-aware allele frequency lookups, and population-level evolutionary analysis.
    5 installs
82. ▌
    Tooluniverse Proteomics Analysis 2 · mims-harvard bundle

    Mass-spec proteomics analysis — protein identification, quantification (LFQ, TMT, iTRAQ), differential expression (tumor vs normal, treatment vs control), PTM identification, and pathway enrichment on protein lists. Use when you have proteomics MS output, asking about protein abundance differences, or doing systems-level proteomic interpretation.
    9 installs
83. ▌
    Tooluniverse Regulatory Genomics 2 · mims-harvard

    Transcription factor binding, cis-regulatory elements (cCREs), chromatin accessibility, and regulatory annotation using JASPAR (motifs), ENCODE (cCREs, ChIP-seq), RegulomeDB (regulatory variant scoring), UCSC — plus sequence-based deep-learning prediction of regulatory activity and non-coding variant effects (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2). Use for regulatory element annotation, TF-binding-site prediction, regulatory-region functional impact assessment, and predicting how a non-coding variant or a raw DNA sequence affects expression/chromatin/accessibility. Use this whenever a user asks what regulates a gene, whether a SNP hits a regulatory element, or to predict a non-coding variant's functional effect from sequence.
    10 installs
84. ▌
    Tooluniverse Antibody Engineering 2 · mims-harvard bundle

    Therapeutic antibody engineering and optimization, lead-to-clinical-candidate. Covers sequence humanization (germline alignment, framework retention), affinity maturation, developability (aggregation, stability, PTMs), structure modeling (AlphaFold/PDB CDR analysis), immunogenicity prediction, and manufacturing feasibility. Use for biologic-drug optimization, mAb design review, biosimilar engineering, and clinical-precedent comparison.
    11 installs
85. ▌
    Tooluniverse Cancer Genomics Tcga 2 · mims-harvard

    TCGA/GDC cancer genomics analysis — cohort construction, clinical metadata retrieval, somatic mutation frequencies, survival analysis, and multi-omics integration. Use for TCGA-BRCA-style cohort studies, mutation prevalence by cancer type, survival-by-mutation analysis, and pan-cancer driver discovery. Always cancer-type-specific (don't use pan-cancer counts without cohort context).
    9 installs
86. ▌
    Tooluniverse Comparative Genomics 2 · mims-harvard bundle

    Cross-species gene comparison and ortholog analysis. Integrates Ensembl Compara orthologs, NCBI Gene, UniProt, OLS, Monarch, and OpenTargets to identify orthologs, paralogs, sequence conservation, functional conservation across species, and lineage-specific gene gains/losses. Use for phylogenetic gene tracing, model-organism mapping, and evolutionary-genomics queries.
    6 installs
87. ▌
    Tooluniverse Ecology Biodiversity 2 · mims-harvard

    Ecology, biodiversity, and conservation biology research — species identification (GBIF, NCBI Taxonomy), invasive species impact, ecosystem dynamics, conservation status (IUCN), niche ecology. Use for biodiversity questions, species comparison, invasion biology, conservation prioritization, and ecology-related literature search.
    6 installs
88. ▌
    Tooluniverse Metabolomics Pathway 2 · mims-harvard

    Metabolomics pathway analysis — metabolite identification (HMDB, KEGG, ChEBI), pathway mapping (Reactome, KEGG, MetaCyc), disease associations, enzyme/gene linkage. Use for metabolite-to-pathway-to-disease connections, BridgeDb-based ID conversion, and integrating metabolomics with gene-level pathway analyses.
    4 installs
89. ▌
    Tooluniverse Epidemiological Analysis 2 · mims-harvard

    End-to-end observational epidemiology analysis — from research question (PECO Population/Exposure/Comparator/Outcome) to publication-ready statistical report. Covers cohort/case-control/cross-sectional design, regression with confounders, propensity scoring, sensitivity analysis. Writes Python code for every step. Use for epidemiology study analysis, NHANES/UK-Biobank-style analyses.
    4 installs
90. ▌
    Tooluniverse Gene Disease Association 2 · mims-harvard

    Gene-disease association analysis across DisGeNET, OpenTargets, Monarch, OMIM, GenCC, Orphanet. Cross-references multiple sources for evidence-graded association reports with concordance scoring (5/5 sources agree → strong, 1/5 → weak). Use for 'which diseases is gene X associated with' or 'which genes cause disease Y' queries with quantitative confidence.
    5 installs
91. ▌
    Tooluniverse Gene Regulatory Networks 2 · mims-harvard

    Gene regulatory network analysis — TF-target inference (JASPAR motifs, ChIP-seq), motif scanning, eQTL integration, perturbation evidence (knockout/overexpression). Use for 'which TF regulates gene X', 'which genes does TF Y target', regulatory pathway reconstruction. Distinguishes direct (binding) vs indirect (co-expression) regulatory evidence.
    10 installs
92. ▌
    Tooluniverse Literature Deep Research 2 · mims-harvard bundle

    Deep literature review — PubMed, EuropePMC, bioRxiv preprints, citation networks, evidence synthesis. Disambiguates queries, runs collision-aware searches, grades evidence T1-T4, and produces structured reports. Use for systematic literature review, meta-analysis evidence collection, and detailed answer-with-citations workflows.
    7 installs
93. ▌
    Tooluniverse Pathway Disease Genetics 2 · mims-harvard

    Connect GWAS variants to biological pathways and druggable targets. Maps GWAS hits to causal genes (via fine-mapping/eQTL), then to pathways (Reactome, KEGG, WikiPathways), then to existing drugs hitting those pathways. Use for pathway-level disease mechanisms, druggable-pathway prioritization from GWAS, SNP-to-pathway-to-target tracing, and tissue-specific eQTL evidence for drug target hypotheses.
    7 installs
94. ▌
    Tooluniverse Small Molecule Discovery 2 · mims-harvard

    Small molecule identification, characterization, and procurement — PubChem, ChEMBL, BindingDB, ADMET-AI, SwissADME, eMolecules, Enamine. Covers compound name to structure to activity to ADMET properties to commercial sourcing. Use for chemical biology, lead identification, probe selection, and the full small-molecule discovery pipeline.
    7 installs
95. ▌
    Tooluniverse Clinical Data Integration 2 · mims-harvard

    End-to-end drug safety review integrating FDA labels, FAERS adverse event reports, PRR/ROR disproportionality, pharmacogenomic biomarkers, clinical trial data, and published literature. Use for regulatory drug safety reviews, comprehensive pharmacovigilance reports, label-vs-real-world AE comparison, and clinical decision support for drug safety.
    9 installs
96. ▌
    Tooluniverse Data Integration Analysis 2 · mims-harvard

    Integrate computed statistical results (DEGs, GWAS hits, associations) with biological context from ToolUniverse databases (UniProt, GO, Reactome, ClinVar, OpenTargets). Use for adding gene function/pathway/disease annotations to a result list, building biological narrative around statistical findings, and going beyond p-values to mechanism.
    6 installs
97. ▌
    Tooluniverse Expression Data Retrieval 2 · mims-harvard bundle

    Retrieve gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation and quality assessment. Use for finding RNA-seq/microarray datasets by organism/tissue/condition, comparing across studies (case-control, time-series, dose-response), and assessing dataset suitability before downloading. Always uses English search terms.
    8 installs
98. ▌
    Tooluniverse Proteomics Data Retrieval 2 · mims-harvard

    Find and retrieve proteomics datasets from MassIVE and ProteomeXchange. Search by species, keyword, or accession; retrieve detailed metadata (instruments, publications, species, PTMs studied). Use for locating public proteomics datasets to reanalyze, comparing instrument/protocol coverage across studies, and pre-download dataset evaluation.
    11 installs
99. ▌
    Tooluniverse Diagnostic Test Evaluation 2 · mims-harvard bundle

    Diagnostic test / biomarker accuracy — sensitivity, specificity, PPV, NPV, likelihood ratios, accuracy from a 2x2 table; ROC curve, AUC, and the optimal cutoff (Youden) for a continuous biomarker; and post-test probability via Bayes. Use when you have test results vs a gold standard (binary 2x2, or a continuous score + true labels) and need to judge how good the test is, pick a threshold, or compute the probability of disease given a result. Emphasizes the prevalence-dependence of PPV/NPV.
    6 installs
100. ▌
     Tooluniverse Immune Repertoire Analysis 2 · mims-harvard bundle

     TCR/BCR repertoire analysis — V(D)J segment usage, CDR3 sequence diversity, clonality scoring, antigen specificity matching to IEDB, public-clone identification. Use for adaptive immune response characterization, post-treatment immune monitoring, antigen-specific clone tracking, and clonal-expansion analysis in immunotherapy or vaccination studies.
     11 installs