Real-World Examples
Real-World Examples
This section provides practical examples demonstrating Gospel’s capabilities across different use cases, from personal genomics to clinical applications and research.
Table of Contents
- Personal Genomics
- Athletic Performance Optimization
- Clinical Pharmacogenetics
- Nutritional Optimization
- Research Applications
- Integration Examples
Personal Genomics
Complete Personal Genome Analysis
This example demonstrates how to perform a comprehensive personal genome analysis covering all domains.
#!/bin/bash
# personal_genome_analysis.sh
# Step 1: Comprehensive analysis
echo "Starting comprehensive genome analysis..."
gospel analyze \
--vcf personal_genome.vcf \
--domains all \
--include-regulatory \
--include-structural \
--include-networks \
--population EUR \
--min-confidence 0.7 \
--output personal_analysis/ \
--format all
# Step 2: Generate interactive visualizations
echo "Creating visualizations..."
gospel visualize \
--results personal_analysis/ \
--score-distributions \
--protein-networks \
--pathway-enrichment \
--interactive \
--output personal_visualizations/
# Step 3: Query for specific insights
echo "Generating personalized insights..."
gospel query \
--results personal_analysis/ \
--query "What are my top 5 genetic strengths and weaknesses?" \
--format markdown \
--output personal_insights.md
gospel query \
--results personal_analysis/ \
--query "What lifestyle modifications would benefit my genetic profile?" \
--format markdown \
--output lifestyle_recommendations.md
echo "Analysis complete! Check the following files:"
echo "- personal_analysis/summary_report.html"
echo "- personal_visualizations/index.html"
echo "- personal_insights.md"
echo "- lifestyle_recommendations.md"
Family Genomics Analysis
Analyze a family trio to identify inheritance patterns:
#!/bin/bash
# family_analysis.sh
# Analyze each family member
for member in father mother child; do
echo "Analyzing $member..."
gospel analyze \
--vcf family_${member}.vcf \
--domains all \
--population EUR \
--output family_results/${member}/
done
# Compare family results
gospel compare \
--input-dirs family_results/father family_results/mother family_results/child \
--output family_comparison/ \
--inheritance-analysis \
--shared-variants
# Interactive family exploration
gospel query \
--results family_comparison/ \
--interactive \
--query "Show me genetic traits inherited from each parent"
Athletic Performance Optimization
Elite Athlete Profile Analysis
Complete genetic profiling for athletic performance optimization:
#!/usr/bin/env python3
# athlete_profiling.py
from gospel import GospelAnalyzer, FitnessDomain, VisualizationEngine
import json
def analyze_athlete_genome(vcf_file, athlete_name, sport_focus=None):
"""
Comprehensive athlete genome analysis focusing on performance traits.
Args:
vcf_file: Path to athlete's VCF file
athlete_name: Name for output files
sport_focus: Optional sport focus (sprint, endurance, power, mixed)
"""
# Initialize analyzer with fitness focus
analyzer = GospelAnalyzer(
domains=['fitness'],
include_regulatory=True,
include_networks=True,
performance_optimization=True
)
# Run comprehensive analysis
print(f"Analyzing {athlete_name}'s genetic profile...")
results = analyzer.analyze_vcf(
vcf_file=vcf_file,
population='EUR', # Adjust based on athlete's ancestry
output_dir=f'athlete_profiles/{athlete_name}/'
)
# Fitness domain deep dive
fitness_analyzer = FitnessDomain()
fitness_profile = fitness_analyzer.generate_athlete_profile(
variants=results.variants,
sport_focus=sport_focus
)
# Generate sport-specific recommendations
recommendations = fitness_analyzer.generate_training_recommendations(
fitness_profile=fitness_profile,
sport_focus=sport_focus
)
# Create visualization suite
viz_engine = VisualizationEngine()
# Fitness radar chart
viz_engine.create_fitness_radar(
fitness_profile,
output_file=f'athlete_profiles/{athlete_name}/fitness_radar.html'
)
# Training optimization chart
viz_engine.create_training_chart(
recommendations,
output_file=f'athlete_profiles/{athlete_name}/training_plan.html'
)
# Injury risk assessment
injury_analysis = fitness_analyzer.assess_injury_risk(
variants=results.variants
)
# Generate comprehensive report
athlete_report = {
'athlete_name': athlete_name,
'overall_fitness_score': fitness_profile.overall_score,
'component_scores': {
'sprint_power': fitness_profile.sprint_score,
'endurance': fitness_profile.endurance_score,
'strength': fitness_profile.strength_score,
'recovery': fitness_profile.recovery_score,
'injury_risk': injury_analysis.overall_risk
},
'genetic_advantages': fitness_profile.advantages,
'genetic_limitations': fitness_profile.limitations,
'training_recommendations': recommendations.training_plan,
'recovery_protocols': recommendations.recovery_plan,
'injury_prevention': injury_analysis.prevention_strategies,
'optimal_sports': recommendations.sport_recommendations
}
# Save detailed report
with open(f'athlete_profiles/{athlete_name}/athlete_report.json', 'w') as f:
json.dump(athlete_report, f, indent=2)
return athlete_report
# Example usage for different athlete types
if __name__ == "__main__":
# Sprint athlete analysis
sprint_athlete = analyze_athlete_genome(
vcf_file='athletes/sprinter_genome.vcf',
athlete_name='elite_sprinter',
sport_focus='sprint'
)
# Endurance athlete analysis
endurance_athlete = analyze_athlete_genome(
vcf_file='athletes/marathoner_genome.vcf',
athlete_name='elite_marathoner',
sport_focus='endurance'
)
# Mixed sport athlete (e.g., soccer, basketball)
mixed_athlete = analyze_athlete_genome(
vcf_file='athletes/soccer_player_genome.vcf',
athlete_name='soccer_player',
sport_focus='mixed'
)
print("Athlete profiling complete!")
print("Check the athlete_profiles/ directory for results.")
Sport-Specific Training Optimization
#!/bin/bash
# sport_specific_training.sh
# Function to analyze athlete for specific sport
analyze_for_sport() {
local vcf_file=$1
local sport=$2
local athlete_name=$3
echo "Analyzing $athlete_name for $sport performance..."
gospel analyze \
--vcf "$vcf_file" \
--domains fitness \
--sport-focus "$sport" \
--include-regulatory \
--training-optimization \
--injury-risk-assessment \
--recovery-analysis \
--output "sport_analysis/${athlete_name}_${sport}/"
# Generate sport-specific recommendations
gospel query \
--results "sport_analysis/${athlete_name}_${sport}/" \
--query "Provide detailed training recommendations for $sport based on my genetic profile" \
--include-literature \
--output "sport_analysis/${athlete_name}_${sport}/training_plan.md"
# Create performance prediction
gospel query \
--results "sport_analysis/${athlete_name}_${sport}/" \
--query "What is my performance potential in $sport and which aspects should I focus on?" \
--output "sport_analysis/${athlete_name}_${sport}/performance_prediction.md"
}
# Analyze for different sports
analyze_for_sport "athlete.vcf" "sprinting" "john_doe"
analyze_for_sport "athlete.vcf" "marathon" "john_doe"
analyze_for_sport "athlete.vcf" "powerlifting" "john_doe"
analyze_for_sport "athlete.vcf" "soccer" "john_doe"
# Compare performance potential across sports
gospel compare-sports \
--input-dirs sport_analysis/john_doe_*/ \
--output sport_analysis/john_doe_sport_comparison/ \
--create-recommendations
Clinical Pharmacogenetics
Pre-prescription Pharmacogenetic Screening
Clinical workflow for medication safety assessment:
#!/usr/bin/env python3
# clinical_pharma_screening.py
from gospel import PharmacoAnalyzer, ClinicalReportGenerator
import pandas as pd
def clinical_pharma_screening(patient_vcf, patient_id, medication_list=None):
"""
Clinical pharmacogenetic screening for medication safety.
Args:
patient_vcf: Patient's VCF file
patient_id: Unique patient identifier
medication_list: List of medications to specifically analyze
"""
# Initialize pharmacogenetic analyzer
pharma_analyzer = PharmacoAnalyzer(
clinical_mode=True,
include_dosing_guidelines=True,
include_drug_interactions=True
)
# Run pharmacogenetic analysis
print(f"Running pharmacogenetic analysis for patient {patient_id}...")
results = pharma_analyzer.analyze_vcf(
vcf_file=patient_vcf,
output_dir=f'clinical_reports/{patient_id}/'
)
# Analyze specific medications if provided
if medication_list:
medication_analysis = pharma_analyzer.analyze_medications(
variants=results.variants,
medications=medication_list
)
else:
medication_analysis = pharma_analyzer.analyze_all_drug_classes(
variants=results.variants
)
# Generate clinical recommendations
clinical_recommendations = {
'patient_id': patient_id,
'analysis_date': pharma_analyzer.get_analysis_date(),
'cyp_status': {
'CYP2D6': medication_analysis.cyp2d6_status,
'CYP2C19': medication_analysis.cyp2c19_status,
'CYP2C9': medication_analysis.cyp2c9_status,
'CYP3A4': medication_analysis.cyp3a4_status
},
'high_risk_medications': medication_analysis.high_risk_drugs,
'dosing_adjustments': medication_analysis.dosing_recommendations,
'alternative_medications': medication_analysis.alternative_drugs,
'drug_interactions': medication_analysis.potential_interactions,
'monitoring_recommendations': medication_analysis.monitoring_plans
}
# Generate clinical report
report_generator = ClinicalReportGenerator()
clinical_report = report_generator.generate_pharma_report(
patient_id=patient_id,
analysis_results=results,
medication_analysis=medication_analysis,
recommendations=clinical_recommendations
)
# Save clinical report
report_generator.save_report(
report=clinical_report,
output_file=f'clinical_reports/{patient_id}/pharma_report.pdf',
format='clinical_pdf'
)
# Generate pharmacist reference sheet
pharmacist_sheet = report_generator.generate_pharmacist_reference(
patient_id=patient_id,
cyp_status=clinical_recommendations['cyp_status'],
drug_recommendations=clinical_recommendations
)
return clinical_recommendations
# Example: Pre-surgical screening
def pre_surgical_screening(patient_vcf, patient_id):
"""Screen patient before surgery for pain medication compatibility."""
# Common surgical medications
surgical_medications = [
'morphine', 'codeine', 'tramadol', 'fentanyl',
'warfarin', 'clopidogrel', 'aspirin',
'midazolam', 'propofol'
]
recommendations = clinical_pharma_screening(
patient_vcf=patient_vcf,
patient_id=patient_id,
medication_list=surgical_medications
)
# Generate surgical-specific recommendations
surgical_recommendations = {
'pain_management': recommendations.get('pain_medications', {}),
'anticoagulation': recommendations.get('anticoagulants', {}),
'anesthesia': recommendations.get('anesthetics', {}),
'post_op_monitoring': recommendations.get('monitoring_recommendations', {})
}
return surgical_recommendations
# Example usage
if __name__ == "__main__":
# General pharmacogenetic screening
patient_001 = clinical_pharma_screening(
patient_vcf='patients/patient_001.vcf',
patient_id='PATIENT_001'
)
# Pre-surgical screening
surgical_patient = pre_surgical_screening(
patient_vcf='patients/surgical_patient.vcf',
patient_id='SURGICAL_001'
)
print("Clinical pharmacogenetic screening complete!")
Drug Development Support
Pharmacogenetic analysis for clinical trials:
#!/usr/bin/env python3
# clinical_trial_stratification.py
from gospel import PharmacoAnalyzer, CohortAnalyzer
import pandas as pd
def stratify_clinical_trial_cohort(cohort_vcfs, drug_target, trial_name):
"""
Stratify clinical trial participants based on pharmacogenetic profiles.
Args:
cohort_vcfs: List of VCF files for trial participants
drug_target: Target drug/pathway for the trial
trial_name: Name of the clinical trial
"""
pharma_analyzer = PharmacoAnalyzer()
cohort_analyzer = CohortAnalyzer()
participant_profiles = []
# Analyze each participant
for i, vcf_file in enumerate(cohort_vcfs):
participant_id = f"PARTICIPANT_{i+1:03d}"
print(f"Analyzing {participant_id}...")
# Individual pharmacogenetic analysis
results = pharma_analyzer.analyze_vcf(
vcf_file=vcf_file,
drug_focus=drug_target,
output_dir=f'clinical_trials/{trial_name}/{participant_id}/'
)
# Extract relevant pharmacogenetic markers
profile = {
'participant_id': participant_id,
'cyp2d6_phenotype': results.cyp2d6_status,
'cyp2c19_phenotype': results.cyp2c19_status,
'cyp2c9_phenotype': results.cyp2c9_status,
'drug_target_variants': results.drug_target_analysis,
'predicted_response': results.response_prediction,
'recommended_dose': results.dosing_recommendation,
'risk_factors': results.risk_assessment
}
participant_profiles.append(profile)
# Cohort-level analysis
cohort_summary = cohort_analyzer.analyze_cohort(
participant_profiles=participant_profiles,
drug_target=drug_target
)
# Stratification recommendations
stratification = cohort_analyzer.recommend_stratification(
cohort_summary=cohort_summary,
trial_objectives=['efficacy', 'safety', 'dosing']
)
# Generate trial design recommendations
trial_design = {
'total_participants': len(participant_profiles),
'stratification_groups': stratification.groups,
'randomization_strategy': stratification.randomization,
'dosing_schema': stratification.dosing_groups,
'monitoring_requirements': stratification.monitoring_plan,
'expected_outcomes': stratification.outcome_predictions
}
return trial_design, participant_profiles
# Example usage for different trial types
if __name__ == "__main__":
# Cardiovascular drug trial
cv_trial = stratify_clinical_trial_cohort(
cohort_vcfs=[f'cohorts/cv_trial/participant_{i}.vcf' for i in range(1, 101)],
drug_target='warfarin',
trial_name='CV_WARFARIN_TRIAL'
)
# Oncology trial
oncology_trial = stratify_clinical_trial_cohort(
cohort_vcfs=[f'cohorts/oncology/patient_{i}.vcf' for i in range(1, 51)],
drug_target='5-fluorouracil',
trial_name='ONCOLOGY_5FU_TRIAL'
)
Nutritional Optimization
Personalized Nutrition Plan
Complete nutritional genomics analysis with meal planning:
#!/usr/bin/env python3
# personalized_nutrition.py
from gospel import NutritionAnalyzer, MealPlanner, SupplementOptimizer
import json
def create_personalized_nutrition_plan(vcf_file, user_profile):
"""
Create comprehensive personalized nutrition plan based on genetics.
Args:
vcf_file: User's genetic data
user_profile: Additional user information (age, sex, goals, etc.)
"""
# Initialize nutrition analyzer
nutrition_analyzer = NutritionAnalyzer(
include_sensitivities=True,
include_metabolism=True,
include_requirements=True
)
# Run nutritional genomics analysis
print("Analyzing nutritional genetics...")
results = nutrition_analyzer.analyze_vcf(
vcf_file=vcf_file,
output_dir=f'nutrition_plans/{user_profile["user_id"]}/'
)
# Analyze specific nutritional components
macro_analysis = nutrition_analyzer.analyze_macronutrients(
variants=results.variants,
user_profile=user_profile
)
micro_analysis = nutrition_analyzer.analyze_micronutrients(
variants=results.variants
)
sensitivity_analysis = nutrition_analyzer.analyze_food_sensitivities(
variants=results.variants
)
# Generate personalized recommendations
nutrition_recommendations = {
'user_id': user_profile['user_id'],
'genetic_profile': {
'metabolism_type': macro_analysis.metabolism_type,
'caffeine_sensitivity': sensitivity_analysis.caffeine_sensitivity,
'lactose_tolerance': sensitivity_analysis.lactose_tolerance,
'gluten_sensitivity_risk': sensitivity_analysis.gluten_risk,
'alcohol_metabolism': macro_analysis.alcohol_metabolism
},
'macronutrient_ratios': {
'carbohydrates': macro_analysis.optimal_carb_ratio,
'proteins': macro_analysis.optimal_protein_ratio,
'fats': macro_analysis.optimal_fat_ratio
},
'micronutrient_needs': {
'folate': micro_analysis.folate_requirements,
'vitamin_d': micro_analysis.vitamin_d_requirements,
'vitamin_b12': micro_analysis.b12_requirements,
'iron': micro_analysis.iron_requirements
},
'foods_to_emphasize': macro_analysis.beneficial_foods,
'foods_to_limit': sensitivity_analysis.problematic_foods,
'meal_timing': macro_analysis.optimal_meal_timing
}
# Generate supplement recommendations
supplement_optimizer = SupplementOptimizer()
supplement_plan = supplement_optimizer.optimize_supplements(
genetic_profile=nutrition_recommendations['genetic_profile'],
micronutrient_needs=nutrition_recommendations['micronutrient_needs'],
user_profile=user_profile
)
# Create meal plans
meal_planner = MealPlanner()
weekly_meal_plan = meal_planner.create_weekly_plan(
nutrition_recommendations=nutrition_recommendations,
user_preferences=user_profile.get('dietary_preferences', {}),
calorie_target=user_profile.get('calorie_target'),
meal_count=user_profile.get('meals_per_day', 3)
)
# Compile complete nutrition plan
complete_plan = {
'nutrition_recommendations': nutrition_recommendations,
'supplement_plan': supplement_plan,
'weekly_meal_plan': weekly_meal_plan,
'shopping_list': meal_planner.generate_shopping_list(weekly_meal_plan),
'recipe_suggestions': meal_planner.get_recipe_suggestions(nutrition_recommendations)
}
# Save nutrition plan
with open(f'nutrition_plans/{user_profile["user_id"]}/complete_plan.json', 'w') as f:
json.dump(complete_plan, f, indent=2)
return complete_plan
# Example user profiles
if __name__ == "__main__":
# Weight loss focused
weight_loss_user = {
'user_id': 'USER_001',
'age': 35,
'sex': 'female',
'goal': 'weight_loss',
'calorie_target': 1500,
'activity_level': 'moderate',
'dietary_preferences': {
'vegetarian': False,
'gluten_free': False,
'dairy_free': False
},
'meals_per_day': 4
}
weight_loss_plan = create_personalized_nutrition_plan(
vcf_file='users/weight_loss_user.vcf',
user_profile=weight_loss_user
)
# Athletic performance focused
athlete_user = {
'user_id': 'ATHLETE_001',
'age': 28,
'sex': 'male',
'goal': 'athletic_performance',
'sport': 'endurance_running',
'calorie_target': 3500,
'activity_level': 'high',
'meals_per_day': 5
}
athlete_plan = create_personalized_nutrition_plan(
vcf_file='users/athlete_user.vcf',
user_profile=athlete_user
)
print("Personalized nutrition plans created!")
Research Applications
Population Genomics Study
Large-scale population analysis using Gospel:
#!/usr/bin/env python3
# population_genomics_study.py
from gospel import PopulationAnalyzer, StatisticalAnalyzer
import pandas as pd
from multiprocessing import Pool
import numpy as np
def analyze_population_cohort(cohort_data, study_name, research_question):
"""
Analyze large population cohort for genomic research.
Args:
cohort_data: List of dictionaries with VCF files and metadata
study_name: Name of the research study
research_question: Specific research focus
"""
population_analyzer = PopulationAnalyzer()
stats_analyzer = StatisticalAnalyzer()
print(f"Starting population analysis for {study_name}")
print(f"Cohort size: {len(cohort_data)} individuals")
# Parallel processing of individual genomes
def analyze_individual(individual_data):
return population_analyzer.analyze_individual(
vcf_file=individual_data['vcf_file'],
metadata=individual_data['metadata'],
domains=['fitness', 'pharmacogenetics', 'nutrition']
)
# Process cohort in parallel
with Pool() as pool:
individual_results = pool.map(analyze_individual, cohort_data)
# Aggregate population-level results
population_summary = population_analyzer.aggregate_cohort_results(
individual_results=individual_results,
study_focus=research_question
)
# Statistical analysis
statistical_results = stats_analyzer.perform_population_analysis(
population_data=population_summary,
analysis_type=research_question
)
# Generate research insights
research_findings = {
'study_name': study_name,
'cohort_characteristics': population_summary.demographics,
'allele_frequencies': statistical_results.allele_frequencies,
'domain_distributions': statistical_results.domain_score_distributions,
'population_comparisons': statistical_results.population_comparisons,
'significant_associations': statistical_results.gwas_results,
'pathway_enrichment': statistical_results.pathway_analysis,
'clinical_implications': statistical_results.clinical_relevance
}
return research_findings
# Example: Multi-ethnic fitness genomics study
def fitness_genomics_study():
"""Study genetic factors affecting fitness across populations."""
# Simulated cohort data
cohort_data = []
populations = ['EUR', 'AFR', 'EAS', 'AMR', 'SAS']
for pop in populations:
for i in range(100): # 100 individuals per population
individual = {
'vcf_file': f'cohorts/fitness_study/{pop}/individual_{i}.vcf',
'metadata': {
'population': pop,
'age': np.random.randint(18, 65),
'sex': np.random.choice(['male', 'female']),
'sport_category': np.random.choice(['sprint', 'endurance', 'power', 'mixed']),
'elite_status': np.random.choice([True, False], p=[0.2, 0.8])
}
}
cohort_data.append(individual)
# Run population analysis
fitness_study_results = analyze_population_cohort(
cohort_data=cohort_data,
study_name="Multi-Ethnic Fitness Genomics Study",
research_question="fitness_population_genetics"
)
return fitness_study_results
# Example: Pharmacogenetics across populations
def pharma_population_study():
"""Study pharmacogenetic variant frequencies across populations."""
# Focus on key pharmacogenetic genes
target_genes = ['CYP2D6', 'CYP2C19', 'CYP2C9', 'VKORC1', 'SLCO1B1']
cohort_data = []
populations = ['EUR', 'AFR', 'EAS', 'AMR', 'SAS']
for pop in populations:
for i in range(200): # 200 individuals per population
individual = {
'vcf_file': f'cohorts/pharma_study/{pop}/individual_{i}.vcf',
'metadata': {
'population': pop,
'age': np.random.randint(18, 80),
'sex': np.random.choice(['male', 'female']),
'medical_history': np.random.choice(['healthy', 'cardiovascular', 'psychiatric'],
p=[0.6, 0.3, 0.1])
}
}
cohort_data.append(individual)
pharma_study_results = analyze_population_cohort(
cohort_data=cohort_data,
study_name="Global Pharmacogenetics Frequency Study",
research_question="pharmacogenetic_population_frequencies"
)
return pharma_study_results
if __name__ == "__main__":
# Run fitness genomics study
fitness_results = fitness_genomics_study()
print("Fitness genomics study completed!")
# Run pharmacogenetics study
pharma_results = pharma_population_study()
print("Pharmacogenetics population study completed!")
Integration Examples
Healthcare System Integration
Integration with electronic health records (EHR):
#!/usr/bin/env python3
# ehr_integration.py
from gospel import GospelAnalyzer, EHRInterface, ClinicalDecisionSupport
import json
import datetime
class GospelEHRIntegration:
"""Integration layer between Gospel and healthcare systems."""
def __init__(self, ehr_config):
self.ehr_interface = EHRInterface(ehr_config)
self.gospel_analyzer = GospelAnalyzer()
self.clinical_support = ClinicalDecisionSupport()
def process_new_patient_genetics(self, patient_id, vcf_file):
"""Process genetic data for new patient and update EHR."""
# Retrieve patient information from EHR
patient_info = self.ehr_interface.get_patient_info(patient_id)
# Run Gospel analysis
genetic_analysis = self.gospel_analyzer.analyze_vcf(
vcf_file=vcf_file,
domains=['pharmacogenetics', 'nutrition'],
clinical_mode=True
)
# Generate clinical recommendations
clinical_recommendations = self.clinical_support.generate_recommendations(
genetic_analysis=genetic_analysis,
patient_info=patient_info
)
# Format for EHR integration
ehr_genetic_data = {
'patient_id': patient_id,
'analysis_date': datetime.datetime.now().isoformat(),
'pharmacogenetic_profile': {
'cyp2d6_status': genetic_analysis.cyp2d6_phenotype,
'cyp2c19_status': genetic_analysis.cyp2c19_phenotype,
'cyp2c9_status': genetic_analysis.cyp2c9_phenotype,
'high_risk_medications': clinical_recommendations.contraindicated_drugs,
'dosing_adjustments': clinical_recommendations.dose_modifications
},
'nutritional_profile': {
'folate_metabolism': genetic_analysis.mthfr_status,
'vitamin_d_requirements': genetic_analysis.vitamin_d_needs,
'food_sensitivities': genetic_analysis.food_intolerances
},
'clinical_alerts': clinical_recommendations.priority_alerts
}
# Update EHR with genetic data
self.ehr_interface.update_patient_genetics(
patient_id=patient_id,
genetic_data=ehr_genetic_data
)
return ehr_genetic_data
def check_medication_compatibility(self, patient_id, new_medication):
"""Check medication compatibility against patient's genetic profile."""
# Retrieve genetic profile from EHR
genetic_profile = self.ehr_interface.get_patient_genetics(patient_id)
if not genetic_profile:
return {'warning': 'No genetic data available for this patient'}
# Analyze medication compatibility
compatibility = self.clinical_support.check_drug_compatibility(
medication=new_medication,
genetic_profile=genetic_profile
)
# Generate alerts if needed
if compatibility.has_contraindications:
alert = {
'alert_type': 'GENETIC_CONTRAINDICATION',
'severity': compatibility.severity,
'message': compatibility.warning_message,
'recommendations': compatibility.alternative_medications,
'references': compatibility.clinical_guidelines
}
# Send alert to EHR system
self.ehr_interface.send_clinical_alert(patient_id, alert)
return alert
return {'status': 'compatible', 'recommendations': compatibility.dosing_guidance}
# Example usage in clinical workflow
def clinical_workflow_example():
"""Example of Gospel integration in clinical workflow."""
# Initialize integration
ehr_config = {
'ehr_system': 'epic',
'api_endpoint': 'https://ehr.hospital.com/api',
'credentials': 'path/to/credentials.json'
}
gospel_ehr = GospelEHRIntegration(ehr_config)
# Scenario 1: New patient with genetic testing
patient_genetics = gospel_ehr.process_new_patient_genetics(
patient_id='PATIENT_12345',
vcf_file='genetic_tests/patient_12345.vcf'
)
print("Genetic profile integrated into EHR")
# Scenario 2: Prescribing new medication
compatibility_check = gospel_ehr.check_medication_compatibility(
patient_id='PATIENT_12345',
new_medication='warfarin'
)
if compatibility_check.get('alert_type'):
print(f"ALERT: {compatibility_check['message']}")
else:
print("Medication compatible with genetic profile")
if __name__ == "__main__":
clinical_workflow_example()
Research Platform Integration
Integration with research data management platforms:
#!/usr/bin/env python3
# research_platform_integration.py
from gospel import ResearchAnalyzer, DataExporter, QualityController
import pandas as pd
class ResearchPlatformConnector:
"""Connector for integrating Gospel with research platforms."""
def __init__(self, platform_config):
self.platform_config = platform_config
self.research_analyzer = ResearchAnalyzer()
self.data_exporter = DataExporter()
self.quality_controller = QualityController()
def process_research_cohort(self, study_id, cohort_manifest):
"""Process entire research cohort through Gospel pipeline."""
print(f"Processing research cohort for study {study_id}")
# Load cohort manifest
cohort_df = pd.read_csv(cohort_manifest)
# Quality control checks
qc_results = self.quality_controller.assess_cohort_quality(cohort_df)
if not qc_results.passes_qc:
raise ValueError(f"Cohort failed QC: {qc_results.issues}")
# Process each sample
cohort_results = []
for _, sample in cohort_df.iterrows():
sample_id = sample['sample_id']
vcf_file = sample['vcf_path']
# Individual analysis
individual_result = self.research_analyzer.analyze_research_sample(
sample_id=sample_id,
vcf_file=vcf_file,
study_metadata=sample.to_dict()
)
cohort_results.append(individual_result)
# Aggregate cohort-level results
cohort_summary = self.research_analyzer.summarize_cohort(
cohort_results=cohort_results,
study_id=study_id
)
# Export results in research-friendly formats
self.export_research_results(
study_id=study_id,
cohort_summary=cohort_summary,
individual_results=cohort_results
)
return cohort_summary
def export_research_results(self, study_id, cohort_summary, individual_results):
"""Export results in formats suitable for research analysis."""
# PLINK format for GWAS
self.data_exporter.export_plink_format(
study_id=study_id,
results=cohort_summary,
output_dir=f'research_exports/{study_id}/plink/'
)
# R data format
self.data_exporter.export_r_format(
study_id=study_id,
results=cohort_summary,
output_dir=f'research_exports/{study_id}/r_data/'
)
# JSON for web applications
self.data_exporter.export_json_format(
study_id=study_id,
results=cohort_summary,
output_dir=f'research_exports/{study_id}/json/'
)
# CSV for statistical analysis
self.data_exporter.export_csv_format(
study_id=study_id,
results=cohort_summary,
output_dir=f'research_exports/{study_id}/csv/'
)
# Example research study
if __name__ == "__main__":
platform_config = {
'platform': 'terra',
'workspace': 'genomics-research',
'project': 'multi-domain-gwas'
}
connector = ResearchPlatformConnector(platform_config)
# Process large-scale GWAS study
gwas_results = connector.process_research_cohort(
study_id='MULTI_DOMAIN_GWAS_2024',
cohort_manifest='studies/multi_domain_gwas/cohort_manifest.csv'
)
print("Research cohort processing complete!")
These comprehensive examples demonstrate Gospel’s versatility across different use cases, from personal genomics to large-scale research applications. Each example includes complete, runnable code that showcases Gospel’s capabilities and integration potential.
Next: Check out the API Reference for detailed programmatic interface documentation, or explore Contributing to help improve Gospel.