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_by_id`	Get HPO term details	`hp_id`
`HPO_get_term_genes`	Genes associated with HPO term	`hp_id`
`HPO_get_term_diseases`	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_info_by_efoId`	Disease details	`efoId`
`OpenTargets_get_disease_associated_targets`	Genes for disease	`efoId`
`OpenTargets_get_associated_diseases_by_target_ensemblId`	Diseases for gene	`ensemblId`

Phase 3: Gene Panel

Gene Information

Tool	Purpose	Key Parameters
`MyGene_query_genes`	Search genes	`q`, `species`
`MyGene_get_gene_by_id`	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_gene_expression`	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`
`ExAC_get_constraint_metrics`	Constraint data	`gene`

Phase 4: Variant Interpretation

ClinVar Tools

Tool	Purpose	Key Parameters
`ClinVar_search_variants`	Search variants	`query`
`ClinVar_get_variant_by_id`	Get variant details	`id` (not `variant_id`)
`ClinVar_get_variant_classifications`	Classification history	`id`

Parameter Note: Use id not variant_id for ClinVar lookups.

Population Frequency

Tool	Purpose	Key Parameters
`gnomAD_get_variant_frequencies`	Allele frequencies	`variant_id`
`gnomAD_get_variant_annotations`	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`
`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

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:

Run all predictors (AlphaMissense, CADD, EVE for missense; SpliceAI for splice)
If ≥2 concordant damaging → Strong PP3 support
If ≥2 concordant benign → BP4 support
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
`reactome_search_pathways`	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.reactome_search_pathways(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_protein_features`	Sequence features	`accession`
`Pfam_get_domains`	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_term_diseases(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_frequencies(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_protein_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_diseases`	Literature search

Expression & Regulatory

Primary	Fallback 1	Fallback 2
`CELLxGENE_get_expression_data`	`GTEx_get_median_gene_expression`	`HPA_get_gene_expression`
`ChIPAtlas_enrichment_analysis`	`ENCODE_search_experiments`	Literature search

Pathway Analysis

Primary	Fallback 1	Fallback 2
`kegg_get_gene_info`	`reactome_search_pathways`	`OpenTargets_pathways`
`intact_search_interactions`	`STRING_interactions`	Literature search

Variant Annotation

Primary	Fallback 1	Fallback 2
`ClinVar_get_variant`	`gnomAD_get_variant`	Literature search
`gnomAD_get_variant_frequencies`	`ExAC_frequencies`	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_domains`	`UniProt_features`

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_by_id`	`variant_id=123`	`id=123`
`OpenTargets_*`	`ensemblID`	`ensemblId` (camelCase)
`GTEx_get_median_gene_expression`	`ensembl_id`	`gencode_id` (versioned)
`gnomAD_get_variant_frequencies`	`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

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Rare Disease Diagnosis - Tool Reference

Phase 1: Phenotype Standardization

HPO Tools

Phase 2: Disease Matching

Orphanet Tools (UPDATED)

OMIM Tools (UPDATED)

DisGeNET Tools (NEW)

ClinGen - Gene-Disease Validity (NEW)

OpenTargets Disease Tools

Phase 3: Gene Panel

Gene Information

Expression Validation

Constraint Scores

Phase 4: Variant Interpretation

ClinVar Tools

Population Frequency

Pathogenicity Prediction (ENHANCED)

CADD API (NEW)

AlphaMissense (NEW)

EVE (NEW)

SpliceAI - Splice Variant Prediction (NEW)

Phase 3.5: Expression & Regulatory Context (NEW)

CELLxGENE - Single-Cell Expression

ChIPAtlas - Transcription Factor Binding

ENCODE - Regulatory Elements

Phase 3.6: Pathway Analysis (NEW)

KEGG - Metabolic & Signaling Pathways

Reactome - Biological Processes

IntAct - Protein-Protein Interactions

Phase 5: Structure Analysis (NVIDIA NIM)

Structure Prediction

Domain Annotation

Phase 6: Literature Evidence (NEW)

PubMed - Published Literature

BioRxiv/MedRxiv - Preprints

OpenAlex - Citation Analysis

Semantic Scholar - AI-Enhanced Search

Workflow Code Examples

Example 1: Full Phenotype-to-Diagnosis

Example 2: Variant Interpretation

Example 3: Structure Analysis for VUS

Fallback Chains

Disease Matching

Expression & Regulatory

Pathway Analysis

Variant Annotation

Pathogenicity Prediction (ENHANCED)

Structure Prediction

Literature

Common Parameter Mistakes

NVIDIA NIM Requirements