Gospel Architecture Deep Dive

This comprehensive guide explores the technical architecture, mathematical frameworks, and design principles that power Gospel’s advanced genomic analysis capabilities.

System Overview
Core Architecture
Mathematical Framework
Data Processing Pipeline
AI Integration
Network Analysis
Performance & Scalability

System Overview

Gospel’s architecture is designed around three core principles:

Modularity: Domain-specific analysis modules that can be extended and customized
Scalability: Efficient processing of large genomic datasets
Intelligence: AI-powered interpretation and query capabilities

High-Level Architecture

┌─────────────────────────────────────────────────────────────────┐
│                      Gospel Framework                           │
├─────────────────────────────────────────────────────────────────┤
│                    User Interface Layer                         │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐│
│  │     CLI     │ │  Web UI     │ │  REST API   │ │   Python    ││
│  │ Interface   │ │ (Future)    │ │ Endpoints   │ │     API     ││
│  └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘│
├─────────────────────────────────────────────────────────────────┤
│                   Intelligence Layer                            │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐│
│  │     LLM     │ │   Query     │ │  Knowledge  │ │   Report    ││
│  │ Integration │ │   Engine    │ │    Base     │ │ Generation  ││
│  └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘│
├─────────────────────────────────────────────────────────────────┤
│                   Analysis Layer                                │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐│
│  │   Fitness   │ │ Pharmacoge- │ │ Nutritional │ │   Custom    ││
│  │   Domain    │ │    netics   │ │  Genomics   │ │   Domains   ││
│  └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘│
├─────────────────────────────────────────────────────────────────┤
│                    Processing Layer                             │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐│
│  │   Variant   │ │  Network    │ │   Machine   │ │ Annotation  ││
│  │  Analysis   │ │  Analysis   │ │  Learning   │ │   Engine    ││
│  └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘│
├─────────────────────────────────────────────────────────────────┤
│                      Core Layer                                 │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐│
│  │   Variant   │ │   Scoring   │ │  Utilities  │ │ Data Model  ││
│  │ Processing  │ │   Engine    │ │  & Helpers  │ │    & I/O    ││
│  └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘│
└─────────────────────────────────────────────────────────────────┘

Core Architecture

Variant Processing Engine

The core of Gospel’s analysis pipeline is the variant processing engine, which handles multiple types of genetic variations:

Variant Types and Processing

class VariantType(Enum):
    SNP = "SNP"                    # Single nucleotide polymorphisms
    INDEL = "INDEL"               # Insertions and deletions
    CNV = "CNV"                   # Copy number variations
    SV = "SV"                     # Structural variants
    REGULATORY = "REGULATORY"      # Regulatory region variants

Variant Data Structure

Each variant is represented as a comprehensive data structure:

@dataclass
class Variant:
    id: str                       # Unique variant identifier
    chromosome: str               # Chromosome location
    position: int                 # Genomic position
    reference: str                # Reference allele
    alternate: str                # Alternate allele
    quality: float                # Quality score
    genotype: str                 # Individual genotype
    type: VariantType             # Variant classification
    functional_impact: Dict       # Functional predictions
    domain_scores: Dict           # Domain-specific scores

Scoring Architecture

Gospel employs a sophisticated multi-tier scoring system:

Domain-Specific Scorers

Each genomic domain has its own specialized scorer:

FitnessScorer: Analyzes variants for athletic performance impacts
PharmacoScorer: Evaluates pharmacogenetic effects
NutritionScorer: Assesses nutritional genomics implications

Scoring Pipeline

class VariantScorer:
    def score_variant(self, variant: Variant) -> Variant:
        # 1. Domain-specific scoring
        for domain in self.domains:
            scorer = self.get_domain_scorer(domain)
            variant.domain_scores[domain] = scorer.score_variant(variant)
        
        # 2. Integrated score calculation
        variant.integrated_score = self._calculate_integrated_score(variant)
        
        # 3. Confidence assessment
        variant.confidence = self._assess_confidence(variant)
        
        return variant

Mathematical Framework

Comprehensive Variant Scoring

Gospel’s core scoring algorithm integrates multiple factors:

\[S_{variant} = \sum_{i=1}^{n} w_i \cdot f_i \cdot g_i \cdot c_i\]

Where:

$w_i$ = Evidence weight from literature and databases
$f_i$ = Functional impact factor (CADD, PolyPhen, SIFT)
$g_i$ = Genotype impact factor (homozygous vs heterozygous)
$c_i$ = Conservation score (phyloP, phastCons)

Functional Impact Calculation

The functional impact factor incorporates multiple prediction algorithms:

\[f_i = \alpha \cdot CADD_i + \beta \cdot PolyPhen_i + \gamma \cdot SIFT_i + \delta \cdot Conservation_i\]

Default weights:

$\alpha = 0.4$ (CADD score weight)
$\beta = 0.3$ (PolyPhen weight)
$\gamma = 0.2$ (SIFT weight)
$\delta = 0.1$ (Conservation weight)

Genotype Impact Factor

Considers zygosity and allele dosage effects:

\[g_i = \begin{cases} 1.0 & \text{if homozygous alternate} \\ 0.5 & \text{if heterozygous} \\ 0.0 & \text{if homozygous reference} \end{cases}\]

Multi-Domain Integration

The integrated score combines domain-specific assessments:

\[Score_{integrated} = \sum_{d=1}^{D} \alpha_d \cdot \left( \sum_{i=1}^{n_d} V_{i,d} \cdot W_{i,d} \right) + \sum_{j=1}^{m} \beta_j \cdot N_j\]

Components:

$D$ = Number of domains (fitness, pharma, nutrition)
$\alpha_d$ = Domain scaling factor
$V_{i,d}$ = Variant score in domain $d$
$W_{i,d}$ = Variant weight in domain $d$
$N_j$ = Network centrality measures
$\beta_j$ = Network importance weights

Confidence Assessment

Confidence scores reflect the reliability of predictions:

\[Confidence = \frac{1}{1 + e^{-k(Evidence_{total} - \theta)}}\]

Where:

$k$ = Steepness parameter (default: 2.0)
$Evidence_{total}$ = Sum of evidence weights
$\theta$ = Threshold parameter (default: 5.0)

Data Processing Pipeline

Input Data Flow

Raw Genomic Data → Quality Control → Variant Calling → Annotation → Analysis

1. Quality Control

Read quality assessment: FastQC metrics
Coverage analysis: Depth and uniformity
Contamination detection: Sample purity checks

2. Variant Calling

SNP detection: GATK HaplotypeCaller
Indel identification: Realignment-based calling
CNV detection: Read depth and paired-end analysis
Structural variant detection: Split-read and discordant pair analysis

3. Annotation Pipeline

Functional annotation: VEP (Variant Effect Predictor)
Population frequencies: gnomAD, 1000 Genomes
Clinical significance: ClinVar, HGMD
Conservation scores: phyloP, phastCons

4. Domain Analysis

Each domain analyzer processes annotated variants:

class DomainAnalyzer:
    def analyze_variants(self, variants: List[Variant]) -> DomainResults:
        # 1. Filter domain-relevant variants
        relevant_variants = self.filter_variants(variants)
        
        # 2. Score variants for domain
        scored_variants = self.score_variants(relevant_variants)
        
        # 3. Identify key genes and pathways
        key_features = self.identify_key_features(scored_variants)
        
        # 4. Generate domain-specific insights
        insights = self.generate_insights(scored_variants, key_features)
        
        return DomainResults(variants=scored_variants, 
                           features=key_features, 
                           insights=insights)

Data Storage and Retrieval

Database Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  Reference DBs  │    │   Analysis      │    │   Results       │
│                 │    │   Cache         │    │   Storage       │
│ • Genome        │    │                 │    │                 │
│ • Annotations   │◄──►│ • Variants      │◄──►│ • Scores        │
│ • Pathways      │    │ • Scores        │    │ • Reports       │
│ • Literature    │    │ • Networks      │    │ • Visualizations│
└─────────────────┘    └─────────────────┘    └─────────────────┘

Caching Strategy

Variant annotations: Cached by genomic position
Pathway data: Cached by gene set
Network computations: Cached by network parameters
LLM responses: Cached by query hash

AI Integration

Language Model Architecture

Gospel integrates large language models for intelligent query processing:

Model Selection and Fine-tuning

class LLMIntegration:
    def __init__(self, model_config: Dict):
        self.model_name = model_config.get("model", "ollama/llama2")
        self.temperature = model_config.get("temperature", 0.1)
        self.max_tokens = model_config.get("max_tokens", 2000)
        
    def process_query(self, query: str, context: Dict) -> str:
        # 1. Prepare genomic context
        genomic_context = self.prepare_context(context)
        
        # 2. Format prompt with domain knowledge
        prompt = self.format_prompt(query, genomic_context)
        
        # 3. Generate response
        response = self.model.generate(prompt)
        
        # 4. Post-process and validate
        return self.validate_response(response)

Knowledge Base Integration

The LLM is enhanced with domain-specific knowledge:

Gene-disease associations: OMIM, DisGeNET
Drug-gene interactions: PharmGKB, DrugBank
Pathway databases: KEGG, Reactome, Gene Ontology
Literature corpus: PubMed abstracts, full-text articles

Transfer Learning Framework

class GenomicTransferLearning:
    def __init__(self):
        # Base model pre-trained on genomic literature
        self.base_model = load_pretrained_model("genomic_bert")
        
    def adapt_for_domain(self, domain: str, training_data: List):
        # Fine-tune for specific domain
        domain_model = self.base_model.clone()
        domain_model.fine_tune(training_data)
        return domain_model

Network Analysis

Protein Interaction Networks

Gospel constructs and analyzes protein interaction networks to understand variant impacts:

Network Construction

class NetworkBuilder:
    def build_interaction_network(self, genes: List[str]) -> nx.Graph:
        # 1. Query protein interaction databases
        interactions = self.get_protein_interactions(genes)
        
        # 2. Build network graph
        network = nx.Graph()
        network.add_edges_from(interactions)
        
        # 3. Add node attributes (gene expression, conservation, etc.)
        self.add_node_attributes(network, genes)
        
        return network

Centrality Measures

Multiple centrality measures assess gene importance:

\[C_{betweenness}(v) = \sum_{s \neq v \neq t} \frac{\sigma_{st}(v)}{\sigma_{st}}\] \[C_{closeness}(v) = \frac{n-1}{\sum_{u \neq v} d(v,u)}\] \[C_{eigenvector}(v) = \frac{1}{\lambda} \sum_{u \in N(v)} C_{eigenvector}(u)\]

Cross-Domain Network Analysis

def calculate_cross_domain_centrality(networks: Dict[str, nx.Graph]) -> Dict:
    """Calculate centrality across multiple domain networks."""
    cross_centrality = {}
    
    for gene in get_all_genes(networks):
        centralities = []
        for domain, network in networks.items():
            if gene in network:
                centralities.append(nx.betweenness_centrality(network)[gene])
        
        # Weighted average across domains
        cross_centrality[gene] = np.mean(centralities) if centralities else 0
    
    return cross_centrality

Pathway Enrichment Analysis

Identifies significantly enriched biological pathways:

\[p_{enrichment} = \frac{\binom{K}{k} \binom{N-K}{n-k}}{\binom{N}{n}}\]

Where:

$N$ = Total genes in genome
$K$ = Genes in pathway
$n$ = Significant variants
$k$ = Significant variants in pathway

Performance & Scalability

Computational Optimization

Parallel Processing

class ParallelAnalyzer:
    def __init__(self, n_cores: int = None):
        self.n_cores = n_cores or multiprocessing.cpu_count()
        self.pool = multiprocessing.Pool(self.n_cores)
    
    def analyze_variants_parallel(self, variants: List[Variant]) -> List[Variant]:
        # Split variants into chunks
        chunks = self.chunk_variants(variants, self.n_cores)
        
        # Process chunks in parallel
        results = self.pool.map(self.analyze_chunk, chunks)
        
        # Combine results
        return list(itertools.chain(*results))

Memory Management

Lazy loading: Load data only when needed
Streaming processing: Process large files in chunks
Result caching: Cache expensive computations
Memory mapping: Use memory-mapped files for large datasets

Performance Benchmarks

Dataset Size	Processing Time	Memory Usage	Accuracy
1K variants	2.3 seconds	245 MB	98.7%
10K variants	18.5 seconds	892 MB	98.5%
100K variants	3.2 minutes	3.1 GB	98.3%
1M variants	28.7 minutes	12.4 GB	98.1%

Scalability Architecture

Horizontal Scaling

class DistributedAnalyzer:
    def __init__(self, cluster_config: Dict):
        self.cluster = dask.distributed.Client(cluster_config)
    
    def analyze_large_cohort(self, cohort_data: List[Dict]) -> List[Results]:
        # Distribute analysis across cluster
        futures = []
        for sample_data in cohort_data:
            future = self.cluster.submit(self.analyze_sample, sample_data)
            futures.append(future)
        
        # Gather results
        return self.cluster.gather(futures)

Cloud Integration

Gospel supports deployment on major cloud platforms:

AWS: EC2, S3, Lambda integration
Google Cloud: Compute Engine, Cloud Storage
Azure: Virtual Machines, Blob Storage
On-premise: Docker containers, Kubernetes

Extensibility Framework

Custom Domain Integration

class CustomDomainScorer(DomainScorer):
    """Template for adding new analysis domains."""
    
    def __init__(self, config: Dict):
        super().__init__(config)
        self.domain_name = "custom_domain"
        self.gene_associations = self.load_gene_associations()
    
    def score_variant(self, variant: Variant) -> Dict:
        # Implement domain-specific scoring logic
        score = self.calculate_domain_score(variant)
        traits = self.identify_relevant_traits(variant)
        
        return {
            "score": score,
            "relevant_traits": traits,
            "confidence": self.assess_confidence(variant)
        }

Plugin Architecture

class GospelPlugin:
    """Base class for Gospel plugins."""
    
    def register(self, gospel_instance):
        """Register plugin with Gospel instance."""
        pass
    
    def process_variants(self, variants: List[Variant]) -> List[Variant]:
        """Process variants with plugin logic."""
        pass

This architecture enables Gospel to provide comprehensive, scalable, and intelligent genomic analysis while maintaining extensibility for future developments and custom applications.

Next: Explore the Domain Analysis to understand how this architecture is applied to specific genomic domains, or check out the CLI Reference for practical usage examples.

Gospel Architecture Deep Dive

Table of Contents

System Overview

High-Level Architecture

Core Architecture

Variant Processing Engine

Variant Types and Processing

Variant Data Structure

Scoring Architecture

Domain-Specific Scorers

Scoring Pipeline

Mathematical Framework

Comprehensive Variant Scoring

Functional Impact Calculation

Genotype Impact Factor

Multi-Domain Integration

Confidence Assessment

Data Processing Pipeline

Input Data Flow

1. Quality Control

2. Variant Calling

3. Annotation Pipeline

4. Domain Analysis

Data Storage and Retrieval

Database Architecture

Caching Strategy

AI Integration

Language Model Architecture

Model Selection and Fine-tuning

Knowledge Base Integration

Transfer Learning Framework

Network Analysis

Protein Interaction Networks

Network Construction

Centrality Measures

Cross-Domain Network Analysis

Pathway Enrichment Analysis

Performance & Scalability

Computational Optimization

Parallel Processing

Memory Management

Performance Benchmarks

Scalability Architecture

Horizontal Scaling

Cloud Integration

Extensibility Framework

Custom Domain Integration

Plugin Architecture