drug-discovery-prompts/upstream/mims-harvard-ToolUniverse/skills/tooluniverse-rare-disease-diagnosis/TOOLS_REFERENCE.md

title, task, lineage_type, upstream_source, upstream_sha, imported_at, prompt_class, upstream_changes, author, validated

title	task	lineage_type	upstream_source	upstream_sha	imported_at	prompt_class	upstream_changes	author	validated
Rare Disease Diagnosis - Tool Reference		import	https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-rare-disease-diagnosis/TOOLS_REFERENCE.md	e2520a96	2026-06-26	prompt	accepted	upstream	false

Rare Disease Diagnosis - Tool Reference

Phase 1: Phenotype Standardization

HPO Tools

Tool	Purpose	Key Parameters
HPO_search_terms	Search HPO by text	query
HPO_get_term	Get HPO term details	hp_id
HPO_get_genes_by_phenotype	Genes associated with HPO term	hp_id
HPO_get_diseases_by_phenotype	Diseases with HPO term	hp_id

Example - Convert symptom to HPO:

# Search for HPO term
results = tu.tools.HPO_search_terms(query="tall stature")
# Returns: [{"id": "HP:0000098", "name": "Tall stature", ...}]

---

Phase 2: Disease Matching

Orphanet Tools (UPDATED)

Tool	Purpose	Key Parameters
Orphanet_search_diseases	Search rare diseases	operation="search_diseases", query
Orphanet_get_disease	Get disease details	operation="get_disease", orpha_code
Orphanet_get_genes	Genes for disease	operation="get_genes", orpha_code
Orphanet_get_classification	Disease hierarchy	operation="get_classification", orpha_code
Orphanet_search_by_name	Exact name search	operation="search_by_name", name, exact

Example - Search Orphanet (NEW):

# Search for rare diseases
results = tu.tools.Orphanet_search_diseases(
    operation="search_diseases",
    query="Marfan"
)
# Returns: List of matching rare diseases with ORPHA codes

# Get genes for a disease
genes = tu.tools.Orphanet_get_genes(
    operation="get_genes",
    orpha_code="558"
)
# Returns: FBN1 (causative), associated genes

Common Orphanet Disease Codes:

Disease	ORPHA Code
Marfan syndrome	558
Loeys-Dietz syndrome	60030
Vascular EDS	286
Alexander disease	58
Prader-Willi syndrome	739

OMIM Tools (UPDATED)

⚠️ Requires: OMIM_API_KEY environment variable (register at omim.org/api)

Tool	Purpose	Key Parameters
OMIM_search	Search OMIM	operation="search", query, limit
OMIM_get_entry	Get MIM entry	operation="get_entry", mim_number
OMIM_get_clinical_synopsis	Clinical features by organ	operation="get_clinical_synopsis", mim_number
OMIM_get_gene_map	Gene-disease mappings	operation="get_gene_map", mim_number or chromosome

Example - Get OMIM details (NEW):

# Search OMIM
search = tu.tools.OMIM_search(
    operation="search",
    query="BRCA1",
    limit=5
)
# Returns: List of MIM numbers

# Get detailed entry
entry = tu.tools.OMIM_get_entry(
    operation="get_entry",
    mim_number="154700"  # Marfan syndrome
)
# Returns: Full text, inheritance, molecular genetics

# Get clinical synopsis (structured phenotype)
synopsis = tu.tools.OMIM_get_clinical_synopsis(
    operation="get_clinical_synopsis",
    mim_number="154700"
)
# Returns: Features by organ system (neurologicCentralNervousSystem, cardiovascular, etc.)

DisGeNET Tools (NEW)

⚠️ Requires: DISGENET_API_KEY environment variable (register free at disgenet.org)

Tool	Purpose	Key Parameters
DisGeNET_search_gene	Diseases for a gene	operation="search_gene", gene, limit
DisGeNET_search_disease	Genes for a disease	operation="search_disease", disease, limit
DisGeNET_get_gda	Gene-disease associations	operation="get_gda", gene/disease, source, min_score
DisGeNET_get_vda	Variant-disease associations	operation="get_vda", variant/gene, limit
DisGeNET_get_disease_genes	All genes for disease	operation="get_disease_genes", disease, min_score

Example - DisGeNET gene-disease associations:

# Get diseases associated with gene
result = tu.tools.DisGeNET_search_gene(
    operation="search_gene",
    gene="FBN1",
    limit=20
)
# Returns: Marfan syndrome (score: 0.95), MASS phenotype, etc.

# Get high-confidence curated associations
gda = tu.tools.DisGeNET_get_gda(
    operation="get_gda",
    gene="FBN1",
    source="CURATED",
    min_score=0.3,
    limit=20
)
# Returns: Gene-disease associations with evidence scores

# Get variant-disease associations for diagnosis
vda = tu.tools.DisGeNET_get_vda(
    operation="get_vda",
    gene="FBN1",
    limit=30
)
# Returns: Variants with disease associations

DisGeNET Score Interpretation:

Score	Interpretation	Use
>0.7	Very Strong	High confidence
0.4-0.7	Strong	Good evidence
0.2-0.4	Moderate	Consider
<0.2	Weak	Low confidence

ClinGen - Gene-Disease Validity (NEW)

Authoritative curation of gene-disease relationships.

Tool	Purpose	Key Parameters
ClinGen_search_gene_validity	Gene-disease validity	gene
ClinGen_search_dosage_sensitivity	HI/TS scores	gene
ClinGen_search_actionability	Clinical actionability	gene
ClinGen_get_variant_classifications	Expert variant classifications	gene, variant

# Check gene-disease validity classification
validity = tu.tools.ClinGen_search_gene_validity(gene="FBN1")
# Returns: Definitive for Marfan syndrome, Strong for MASS phenotype

# Check dosage sensitivity (for CNV interpretation)
dosage = tu.tools.ClinGen_search_dosage_sensitivity(gene="MECP2")
# Returns: HI Score 3 (haploinsufficient), TS Score 0

# Check clinical actionability
actionability = tu.tools.ClinGen_search_actionability(gene="BRCA1")
# Returns: Adult and pediatric actionability data

ClinGen Validity Classification (for gene panel prioritization):

Classification	Include in Panel?	ACMG Impact
Definitive	Yes - mandatory	Strong PP4 support
Strong	Yes	Good PP4 support
Moderate	Yes	Moderate PP4 support
Limited	Yes, but flag	Weak support
Disputed	Exclude	Conflicting evidence
Refuted	EXCLUDE	Gene not causative

Dosage Sensitivity Scores (for CNV interpretation):

Score	Meaning	ACMG Impact
3	Sufficient evidence	PVS1 for LOF deletions
2	Emerging evidence	PM1
1	Little evidence	Weak support
0/40	None/Unlikely	No dosage sensitivity

OpenTargets Disease Tools

Tool	Purpose	Key Parameters
OpenTargets_get_disease_description_by_efoId	Disease details	efoId
OpenTargets_get_associated_targets_by_disease_efoId	Genes for disease	efoId
OpenTargets_get_diseases_phenotypes_by_target_ensembl	Diseases for gene	ensemblId

---

Phase 3: Gene Panel

Gene Information

Tool	Purpose	Key Parameters
MyGene_query_genes	Search genes	q, species
MyGene_get_gene_annotation	Gene details	geneid
ensembl_lookup_gene	Ensembl gene info	id, species

Parameter Note: Use q not gene for MyGene_query_genes.

Expression Validation

Tool	Purpose	Key Parameters
GTEx_get_median_gene_expression	Tissue expression	gencode_id
HPA_get_rna_expression_by_source	Protein expression	ensembl_id

Note: GTEx requires versioned Ensembl ID (e.g., ENSG00000166147.15)

Constraint Scores

Tool	Purpose	Key Parameters
gnomad_get_gene_constraints	pLI, LOEUF scores	gene_symbol
gnomad_get_gene_constraints	Constraint data	gene

---

Phase 4: Variant Interpretation

ClinVar Tools

Tool	Purpose	Key Parameters
ClinVar_search_variants	Search variants	query
ClinVar_get_variant_details	Get variant details	id (not variant_id)
ClinVar_get_clinical_significance	Classification history	id

Parameter Note: Use id not variant_id for ClinVar lookups.

Population Frequency

Tool	Purpose	Key Parameters
gnomad_get_variant	Allele frequencies	variant_id
gnomad_get_variant	Variant annotations	variant_id

Variant ID Format: 1-55505647-G-A (chrom-pos-ref-alt)

Pathogenicity Prediction (ENHANCED)

Tool	Purpose	Key Parameters
CADD_get_variant_score	CADD deleteriousness (NEW API)	chrom, pos, ref, alt, version
AlphaMissense_get_variant_score	DeepMind pathogenicity (NEW)	uniprot_id, variant
ESM_explain_variant_mechanism	ESMC-6B SAE mechanism of effect (NEW)	sequence, position, ref_aa, alt_aa
EVE_get_variant_score	Evolutionary pathogenicity (NEW)	chrom, pos, ref, alt OR variant (HGVS)
SpliceAI_predict_splice	Splice impact (NEW)	variant, genome
SpliceAI_get_max_delta	Quick splice triage (NEW)	variant, genome
SpliceAI_predict_pangolin	Alternative splice model	variant, genome

CADD API (NEW)

Direct access to CADD deleteriousness scores:

# Get CADD score for variant
result = tu.tools.CADD_get_variant_score(
    chrom="15",
    pos=48942946,
    ref="G",
    alt="A",
    version="GRCh38-v1.7"
)
# Returns: phred_score, raw_score, interpretation
# PHRED ≥20 = top 1% deleterious (PP3 support)

AlphaMissense (NEW)

DeepMind's state-of-the-art missense pathogenicity prediction (~90% accuracy):

# Get pathogenicity score for missense variant
result = tu.tools.AlphaMissense_get_variant_score(
    uniprot_id="P35555",  # FBN1
    variant="E1541K"  # or "p.E1541K"
)
# Returns: pathogenicity_score, classification (pathogenic/ambiguous/benign)
# Thresholds: >0.564 pathogenic, <0.34 benign

ESMC-6B SAE — Mechanism of Effect (NEW)

AlphaMissense gives a pathogenicity score but no mechanism. ESMC-6B Sparse Autoencoder features identify which interpretable protein-language-model features the mutation disrupts (catalytic, ligand-binding, PTM, domain, transmembrane, etc.). Use as a mechanism complement when the report needs to explain how a variant is pathogenic, not just whether.

# One-call mechanism for a VUS (requires WT protein sequence)
result = tu.tools.ESM_explain_variant_mechanism(
    sequence=wt_protein_sequence,
    position=1541, ref_aa="E", alt_aa="K",
    top_k_features=5,
)
# result["data"]["mechanism_summary"] e.g.:
#   "Disrupted feature categories (lost): ligand-binding=2, domain=1"

Other SAE tools (advanced):

• ESM_score_variant_sae_disruption — single variant, raw feature deltas, no labels (faster, no describe-feature calls)
• ESM_score_variant_sae_batch — many variants at once (N+1 Forge calls instead of 2N); use for saturation mutagenesis
• ESM_get_region_sae_features — aggregate features over a residue range (e.g. characterize a domain or motif)
• ESM_describe_sae_feature — biological category label for a feature_id (cached per id)

Mapping SAE categories → ACMG support:

SAE category lost	Mechanistic claim	ACMG line
catalytic	Active-site disruption	Mechanistic support for PP3
ligand-binding / ptm / domain	Functional site disruption	Supports PP3
structural-stability / secondary-structure	Fold-destabilizing	Supports PP3
transmembrane / signal-peptide	Targeting / membrane integration	Supports PP3
(no interpretable change)	No mechanistic signal	Do not strengthen PP3 above the predictor score alone

Requires: ESM_API_KEY env var (free non-commercial token at https://forge.evolutionaryscale.ai) and pip install 'esm @ git+https://github.com/evolutionaryscale/esm@ee891c52' (SAE support on unmerged feature branch; PyPI esm 3.2.x lacks SAEConfig). Outputs governed by EvolutionaryScale Cambrian Inference License — non-commercial use only.

EVE (NEW)

Evolutionary variant effect prediction (Harvard/Oxford):

# Get EVE score
result = tu.tools.EVE_get_variant_score(
    chrom="15",
    pos=48942946,
    ref="G",
    alt="A"
)
# Returns: eve_score, classification (likely_pathogenic/likely_benign)
# Threshold: >0.5 likely pathogenic

SpliceAI - Splice Variant Prediction (NEW)

Deep learning model for predicting splice-altering effects. ~15% of pathogenic variants affect splicing.

# Full splice prediction
result = tu.tools.SpliceAI_predict_splice(
    variant="chr15-48942946-G-A",
    genome="38"  # or "37"
)
# Returns: DS_AG, DS_AL, DS_DG, DS_DL scores + max_delta_score + interpretation

# Quick triage (max score only)
quick = tu.tools.SpliceAI_get_max_delta(
    variant="chr15-48942946-G-A",
    genome="38"
)
# Returns: max_delta_score, interpretation, pathogenicity_threshold

Variant Format: chr{chrom}-{pos}-{ref}-{alt}

SpliceAI Delta Score Interpretation:

Score Type	Meaning
DS_AG	Acceptor Gain (creates new)
DS_AL	Acceptor Loss (disrupts existing)
DS_DG	Donor Gain (creates new)
DS_DL	Donor Loss (disrupts existing)

Max Score Thresholds for ACMG:

Max Delta Score	Interpretation	ACMG
≥0.8	High splice impact	PP3 (strong)
0.5-0.8	Moderate impact	PP3 (supporting)
0.2-0.5	Low impact	PP3 (weak)
<0.2	Likely no impact	BP7 (if synonymous)

When to Use SpliceAI:

• Intronic variants within ±50bp of splice sites
• Synonymous variants (may still affect splicing)
• Exonic variants near splice junctions
• Variants creating cryptic splice sites

---

Prediction Tool Thresholds for PP3:

Tool	Damaging	Uncertain	Benign
AlphaMissense	>0.564	0.34-0.564	<0.34
CADD PHRED	≥20	15-20	<15
EVE	>0.5	-	≤0.5
SpliceAI	≥0.5	0.2-0.5	<0.2

Recommended Strategy for VUS:

1. Run all predictors (AlphaMissense, CADD, EVE for missense; SpliceAI for splice)
2. If ≥2 concordant damaging → Strong PP3 support
3. If ≥2 concordant benign → BP4 support
4. If discordant → Weight AlphaMissense highest for missense, SpliceAI for splice

---

Phase 3.5: Expression & Regulatory Context (NEW)

CELLxGENE - Single-Cell Expression

Tool	Purpose	Key Parameters
CELLxGENE_get_expression_data	Cell-type specific expression	gene, tissue
CELLxGENE_get_cell_metadata	Cell type annotations	gene
CELLxGENE_download_h5ad	Download full dataset	dataset_id
CELLxGENE_get_embeddings	UMAP/tSNE coordinates	dataset_id

Example - Get cell-type expression:

# Get expression across cell types
expression = tu.tools.CELLxGENE_get_expression_data(
    gene="FBN1",
    tissue="heart"
)
# Returns: Expression values per cell type

Why use it: Validates that candidate genes are expressed in disease-relevant cell types (e.g., fibroblasts for connective tissue disorders).

ChIPAtlas - Transcription Factor Binding

Tool	Purpose	Key Parameters
ChIPAtlas_enrichment_analysis	TF binding enrichment	gene, cell_type
ChIPAtlas_get_peak_data	ChIP-seq peaks	gene, experiment_type
ChIPAtlas_search_datasets	Find experiments	antigen, cell_type
ChIPAtlas_get_experiments	Experiment metadata	experiment_id

Example - Get regulatory context:

# Find TFs that regulate gene
tf_binding = tu.tools.ChIPAtlas_enrichment_analysis(
    gene="FBN1",
    cell_type="Fibroblast"
)
# Returns: TFs with significant binding near gene

Why use it: Identifies regulatory mechanisms that may be disrupted; helps interpret regulatory variants.

ENCODE - Regulatory Elements

Tool	Purpose	Key Parameters
ENCODE_search_experiments	Find experiments	assay_title, biosample
ENCODE_get_experiment	Experiment details	accession
ENCODE_get_biosample	Sample annotations	accession
ENCODE_list_files	Get data files	experiment_accession

Example - Get regulatory data:

# Search for regulatory data
experiments = tu.tools.ENCODE_search_experiments(
    assay_title="ATAC-seq",
    biosample="heart"
)

---

Phase 3.6: Pathway Analysis (NEW)

KEGG - Metabolic & Signaling Pathways

Tool	Purpose	Key Parameters
kegg_search_pathway	Search pathways	query
kegg_get_pathway_info	Pathway details	pathway_id
kegg_find_genes	Find gene in KEGG	query
kegg_get_gene_info	Gene pathway membership	gene_id

Example - Get pathway context:

# Find gene in KEGG
kegg_gene = tu.tools.kegg_find_genes(query="hsa:FBN1")
# Get pathway membership
gene_info = tu.tools.kegg_get_gene_info(gene_id="hsa:2200")
# Returns: Pathways containing FBN1

Reactome - Biological Processes

Tool	Purpose	Key Parameters
ReactomeContent_search	Search pathways	query
Reactome_get_pathway	Pathway details	pathway_id
reactome_disease_target_score	Disease-pathway links	disease, target

Example - Get Reactome pathways:

# Search for pathways
pathways = tu.tools.ReactomeContent_search(query="TGF-beta signaling")

IntAct - Protein-Protein Interactions

Tool	Purpose	Key Parameters
intact_search_interactions	Search interactions	query, species
intact_get_interaction_network	Network view	gene, depth
intact_get_complex_details	Protein complexes	complex_id

Example - Get protein interactions:

# Get interaction partners
interactions = tu.tools.intact_search_interactions(
    query="FBN1",
    species="human"
)
# Returns: Direct interaction partners with confidence scores

Why use it: Identifies protein complexes and pathways; variants may disrupt protein-protein interactions.

---

Phase 5: Structure Analysis (NVIDIA NIM)

Structure Prediction

Tool	Purpose	Key Parameters
NvidiaNIM_alphafold2	High-accuracy prediction	sequence, algorithm
NvidiaNIM_esmfold	Fast prediction	sequence

Example - AlphaFold2 prediction:

structure = tu.tools.NvidiaNIM_alphafold2(
    sequence=protein_sequence,
    algorithm="mmseqs2",
    relax_prediction=False
)
# Returns: PDB structure with pLDDT scores

Domain Annotation

Tool	Purpose	Key Parameters
InterPro_get_protein_domains	Domain architecture	accession
UniProt_get_features_by_accession	Sequence features	accession
Pfam_get_protein_annotations	Pfam domains	uniprot_id

---

Phase 6: Literature Evidence (NEW)

PubMed - Published Literature

Tool	Purpose	Key Parameters
PubMed_search_articles	Search articles	query, limit
PubMed_get_article	Get article details	pmid
PubMed_get_related	Related articles	pmid
PubMed_get_cited_by	Citation tracking	pmid

Example - Search disease literature:

# Disease-specific search
papers = tu.tools.PubMed_search_articles(
    query='"Marfan syndrome" AND (FBN1 OR genetics)',
    limit=20
)

BioRxiv/MedRxiv - Preprints

Tool	Purpose	Key Parameters
EuropePMC_search_articles	Search preprints (bioRxiv/medRxiv)	query, source='PPR', pageSize
BioRxiv_get_preprint	Get preprint by DOI	doi
ArXiv_search_papers	Search ArXiv	query, category, limit

Example - Search preprints (bioRxiv/medRxiv don't have search APIs, use EuropePMC):

# Search for recent preprints
preprints = tu.tools.EuropePMC_search_articles(
    query="Marfan syndrome genetics",
    source="PPR",  # PPR = Preprints only
    pageSize=10
)

# Get full metadata if you have a DOI
if doi_from_results.startswith('10.1101/'):
    full = tu.tools.BioRxiv_get_preprint(doi=doi_from_results)
# Returns: Recent preprints (not peer-reviewed)

⚠️ Important: Preprints are NOT peer-reviewed. Flag this in reports.

OpenAlex - Citation Analysis

Tool	Purpose	Key Parameters
openalex_search_works	Search publications	query, limit
openalex_get_author	Author metrics	author_id
openalex_literature_search	Advanced search	query, filters

Example - Citation analysis:

# Get citation data for paper
work = tu.tools.openalex_search_works(
    query="FBN1 Marfan pathogenic",
    limit=10
)
# Returns: Papers with citation counts, open access status

Semantic Scholar - AI-Enhanced Search

Tool	Purpose	Key Parameters
SemanticScholar_search_papers	AI-ranked search	query, limit

Example:

# AI-enhanced literature search
papers = tu.tools.SemanticScholar_search_papers(
    query="rare disease diagnosis machine learning",
    limit=15
)

---

Workflow Code Examples

Example 1: Full Phenotype-to-Diagnosis

def diagnose_rare_disease(tu, symptoms, patient_id):
    """Complete rare disease diagnostic workflow."""
    
    # Phase 1: Standardize phenotype
    hpo_terms = []
    for symptom in symptoms:
        results = tu.tools.HPO_search_terms(query=symptom)
        if results:
            hpo_terms.append(results[0])
    
    # Phase 2: Match diseases
    candidate_diseases = []
    for hpo in hpo_terms:
        diseases = tu.tools.HPO_get_diseases_by_phenotype(hp_id=hpo['id'])
        candidate_diseases.extend(diseases)
    
    # Rank by frequency
    disease_counts = Counter(d['orpha_id'] for d in candidate_diseases)
    top_diseases = disease_counts.most_common(10)
    
    # Phase 3: Build gene panel
    genes = set()
    for orpha_id, count in top_diseases:
        disease_genes = tu.tools.Orphanet_get_disease_genes(orpha_code=orpha_id)
        genes.update(disease_genes)
    
    return {
        'hpo_terms': hpo_terms,
        'candidate_diseases': top_diseases,
        'gene_panel': list(genes)
    }

Example 2: Variant Interpretation

def interpret_variant(tu, variant_hgvs, gene_symbol):
    """Interpret a variant using ACMG criteria."""
    
    evidence = {}
    
    # PM2: Population frequency
    freq = tu.tools.gnomad_get_variant(variant_id=variant_hgvs)
    if freq['allele_frequency'] < 0.00001:
        evidence['PM2'] = {'strength': 'Moderate', 'reason': 'Absent from gnomAD'}
    
    # PP3: Computational predictions
    cadd = tu.tools.CADD_get_scores(variant=variant_hgvs)
    if cadd['phred_score'] > 25:
        evidence['PP3'] = {'strength': 'Supporting', 'reason': f'CADD={cadd["phred_score"]}'}
    
    # ClinVar
    clinvar = tu.tools.ClinVar_search_variants(query=variant_hgvs)
    if clinvar:
        evidence['ClinVar'] = clinvar[0]['clinical_significance']
    
    return evidence

Example 3: Structure Analysis for VUS

def analyze_vus_structure(tu, uniprot_id, variant_position):
    """Structural analysis for variant of uncertain significance."""
    
    # Get protein sequence
    protein = tu.tools.UniProt_get_entry_by_accession(accession=uniprot_id)
    sequence = protein['sequence']
    
    # Predict structure
    structure = tu.tools.NvidiaNIM_alphafold2(
        sequence=sequence,
        algorithm="mmseqs2"
    )
    
    # Get domain annotations
    domains = tu.tools.InterPro_get_protein_domains(accession=uniprot_id)
    
    # Check if variant in domain
    variant_domain = None
    for domain in domains:
        if domain['start'] <= variant_position <= domain['end']:
            variant_domain = domain
            break
    
    return {
        'structure': structure,
        'plddt_at_position': get_plddt(structure, variant_position),
        'domain': variant_domain
    }

---

Fallback Chains

Disease Matching

Primary	Fallback 1	Fallback 2
Orphanet_search_diseases	OMIM_search	DisGeNET_search_disease
Orphanet_get_genes	OMIM_get_gene_map	DisGeNET_get_disease_genes
OMIM_get_clinical_synopsis	Orphanet_get_disease	OpenTargets
DisGeNET_search_gene	OpenTargets_get_diseases_phenotypes_by_target_ensembl	Literature search

Expression & Regulatory

Primary	Fallback 1	Fallback 2
CELLxGENE_get_expression_data	GTEx_get_median_gene_expression	HPA_get_rna_expression_by_source
ChIPAtlas_enrichment_analysis	ENCODE_search_experiments	Literature search

Pathway Analysis

Primary	Fallback 1	Fallback 2
kegg_get_gene_info	ReactomeContent_search	KEGG_get_gene_pathways
intact_search_interactions	STRING_interactions	Literature search

Variant Annotation

Primary	Fallback 1	Fallback 2
ClinVar_get_variant_details	gnomad_get_variant	Literature search
gnomad_get_variant	gnomad_get_variant	1000 Genomes

Pathogenicity Prediction (ENHANCED)

Primary	Fallback 1	Fallback 2
AlphaMissense_get_variant_score	CADD_get_variant_score	EVE_get_variant_score
CADD_get_variant_score	myvariant CADD field	PolyPhen-2
EVE_get_variant_score	VEP with EVE plugin	REVEL

Structure Prediction

Primary	Fallback 1	Fallback 2
NvidiaNIM_alphafold2	alphafold_get_prediction	NvidiaNIM_esmfold
InterPro_get_protein_domains	Pfam_get_protein_annotations	UniProt_get_features_by_accession

Literature

Primary	Fallback 1	Fallback 2
PubMed_search_articles	EuropePMC_search_articles	SemanticScholar_search_papers
EuropePMC_search_articles (source='PPR')	web_search (site:biorxiv.org)	Skip preprints
openalex_search_works	Crossref_search_works	PubMed

---

Common Parameter Mistakes

Tool	Wrong	Correct
MyGene_query_genes	gene="FBN1"	q="FBN1"
ClinVar_get_variant_details	variant_id=123	id=123
OpenTargets_*	ensemblID	ensemblId (camelCase)
GTEx_get_median_gene_expression	ensembl_id	gencode_id (versioned)
gnomad_get_variant	variant="c.123A>G"	variant_id="1-123-A-G"

---

NVIDIA NIM Requirements

API Key: NVIDIA_API_KEY environment variable required

Check availability:

import os
nvidia_available = bool(os.environ.get("NVIDIA_API_KEY"))

Rate limits: 40 RPM (1.5 second minimum between calls)

Async operations: AlphaFold2 may return 202, requiring polling:

# Initial call may return 202
result = tu.tools.NvidiaNIM_alphafold2(sequence=seq)
if result.get('status') == 'pending':
    # Poll for completion (handled internally by tool)
    pass