giae 0.2.1

Genome Interpretation & Annotation Engine - An Explainable, Evidence-Centric Framework for Genomic Interpretation

GIAE — Genome Interpretation & Annotation Engine

Explainability-first genome annotation. Every prediction shows its reasoning.

Most genome annotation tools are overconfident. PROKKA, Bakta, and RAST assign a label, hide the evidence, and give you no way to know how certain the prediction is. GIAE takes the opposite approach:

• Interactive HTML Reports: Generate premium, searchable reports for biologists (--format html).
• High-Performance Caching: SQLite-backed local cache for 10x faster re-runs.
• Explainability: Every prediction includes a complete narrative reasoning chain and confidence score.
• Multilayer Evidence: Integrates Prosite motifs, HMMER domains, protein homology (UniProt), and AI (ESM-2).
• Novel Gene Discovery: Dedicated scoring for "Dark Matter" (genes with no homology).
• Automated CI/CD: Pre-configured GitHub Actions for testing and PyPI deployment.
• MkDocs Documentation: Full documentation site hosted on GitHub Pages.

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What Makes GIAE Different

Feature	PROKKA / Bakta / RAST	GIAE
Output	Label only	Label + evidence chain + confidence score
Uncertainty	Hidden	Explicit, calibrated per gene
Conflicting evidence	Silently resolved	Flagged and reported
Unknown genes	"hypothetical protein"	Ranked as research priorities
Reasoning	Opaque	Full reasoning chain in every report

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4-Layer Evidence Pipeline

Genome (.gb / .fasta)
       │
       ▼
┌──────────────────────────────────────────────┐
│  1. PROSITE Motif Scan         weight: 0.80  │  1,298 curated patterns (bundled)
│  2. EBI HMMER / Pfam Domains   weight: 0.90  │  Pfam via EBI web API (online)
│  3. UniProt API Lookup         weight: 1.00  │  Swiss-Prot reviewed entries (online)
│  4. Conflict Detection                        │  flags when sources disagree
└──────────────────────────────────────────────┘
       │
       ▼
  Interpretation + Confidence Score + Novel Gene Report

Confidence is computed from evidence convergence — when PROSITE, Pfam, and UniProt all agree, confidence is HIGH. When they disagree, the conflict is surfaced explicitly rather than silently resolved.

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Installation

Install from source (recommended while in active development):

git clone https://github.com/Ayo-Cyber/GIAE.git
cd GIAE
pip install -e ".[dev]"

Or via pip:

pip install giae

Requirements: Python 3.10+, BioPython, Click, Rich. No local databases required for the base pipeline — PROSITE (1,298 patterns) is bundled.

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Quick Start

# Offline mode: PROSITE patterns only — no network, instant startup
# Lambda phage (92 genes) completes in ~4 seconds
giae interpret lambda_phage.gb --no-uniprot --no-interpro

# Full pipeline: adds EBI HMMER + UniProt API calls
# Lambda phage takes ~6 minutes (network latency dominates)
giae interpret lambda_phage.gb

# Save Markdown report to file
giae interpret lambda_phage.gb --output lambda_report.md

# JSON output for downstream processing
giae interpret lambda_phage.gb --format json --output results.json

# Large genomes: parallel workers reduce wall time significantly
# T4 phage (288 genes) — use --no-uniprot --no-interpro for offline speed
giae interpret T4.gb --workers 4 --no-uniprot --no-interpro

Runtime guide: Offline (PROSITE only) runs in seconds for phage-sized genomes. Online mode (full pipeline) adds ~4–5 seconds per gene for API calls — plan for 5–10 minutes per phage, hours for large bacterial genomes. Use --workers to parallelise.

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Example Output

A well-characterised gene with converging evidence:

Gene: J — Tail fiber protein
Hypothesis:  Tail fiber / host receptor-binding protein
Confidence:  HIGH (0.87)
Category:    structural_protein

Evidence:
  [0.82] PROSITE PS51123 — Phage tail fiber repeat
  [0.94] Pfam PF09255   — Phage_tail_fib (e-value: 2.1e-14)
  [0.90] UniProt P03722 — Tail fiber protein J, Lambda phage (Swiss-Prot reviewed)

Uncertainty sources: none
Competing hypotheses: none above threshold

A dark-matter gene with zero detectable signal:

Gene: B — hypothetical protein (147 aa)
Interpretation: NONE
Novel Gene Category: DARK MATTER
Priority:  HIGH PRIORITY
Reason:    No sequence homology, domains, or motifs detected

Suggested experiments:
  • Recombinant expression and biochemical activity screening
  • Deletion mutant phenotyping to assess essentiality
  • Comparative genomics across related strains
  • Structural characterization by cryo-EM

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Confidence Levels

Every prediction carries a numeric score mapped to a named level:

Level	Score range	Meaning
HIGH	≥ 0.80	Multiple evidence types converge; strong homology or domain hit
MODERATE	0.50 – 0.79	Some convergence; one strong signal or moderate homology
LOW	0.30 – 0.49	Weak or single-type evidence; treat as a lead, not a conclusion
SPECULATIVE	< 0.30	Minimal signal; flagged for review

Scores are adjusted for: evidence diversity (+0.10 for ≥2 types), strong homology (+0.05), high-confidence Pfam domain (+0.08), and penalised for: limited evidence (−0.10), hypothetical homologs (−0.15), single-evidence-type motif-only predictions (capped at 0.85), and conflict (×0.80 penalty).

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7-Phage Benchmark

Benchmarked on seven classic bacteriophage genomes (offline pipeline: PROSITE + Pfam):

Phage	Genome	Genes	Interpreted	Dark Matter
Lambda (λ)	48.5 kb	92	45 (48.9%)	44 (47.8%)
T7	39.9 kb	56	19 (33.9%)	36 (64.3%)
PhiX174	5.4 kb	11	0 (0.0%)	11 (100%)
Phi29	19.3 kb	30	13 (43.3%)	16 (53.3%)
Mu	36.7 kb	56	20 (35.7%)	35 (62.5%)
P22	41.7 kb	69	30 (43.5%)	38 (55.1%)
T4	168.9 kb	288	75 (26.0%)	213 (73.9%)

Median characterization rate: 34.5%

Lambda with the full 4-layer online pipeline: 48.9% — identical to offline. The 44 dark-matter genes remain dark not because databases are small, but because these proteins genuinely have no detectable sequence-based signal. That is the correct answer.

PhiX174 at 0% is expected and correct. Its proteins (viral jelly-roll β-barrel capsid, DNA pilot tube) are structurally unique folds with no sequence-recognizable motifs. Structural homology search (Foldseek/AlphaFold) is next on the roadmap.

All 7 GenBank files and benchmark reports are in case_studies/.

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Novel Gene Discovery

Every run produces a Novel Gene Report — a structured research agenda for genes that couldn't be interpreted:

Novel Gene Discovery
  Dark Matter:  44  (zero evidence from any source)
  Weak Signal:  13  (confidence < 35%)
  Conflicting:   0

Top Research Priorities:
  1. B    — 147 aa  HIGH PRIORITY  dark_matter
  2. ea22 — 113 aa  HIGH PRIORITY  dark_matter
  3. orf  — 98  aa  HIGH PRIORITY  dark_matter

Three novelty categories:

• Dark matter — zero computational evidence from any source
• Weak evidence — some hits, but confidence below threshold (< 35%)
• Conflict — two or more evidence sources contradict each other

Each candidate includes suggested experiments scaled to protein length and category.

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Python API

Use GIAE programmatically for batch processing or integration into pipelines:

from giae.parsers.genbank import parse_genbank
from giae.engine.interpreter import Interpreter

# Load genome
genome = parse_genbank("lambda_phage.gb")

# Run offline pipeline (fast, no network)
interpreter = Interpreter(use_uniprot=False, use_interpro=False)
summary = interpreter.interpret_genome(genome)

print(f"Interpreted {summary.interpreted_genes}/{summary.total_genes} genes")
print(f"Dark matter: {summary.novel_gene_report.dark_matter_count}")

# Inspect individual results
for result in summary.results:
    if result.interpretation:
        print(result.interpretation.get_explanation())

# Quick single-sequence interpretation
interp = interpreter.quick_interpret("MKVLIFFVIALFSSATAAF...", sequence_type="protein")
print(interp)

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CLI Reference

Usage: giae [OPTIONS] COMMAND [ARGS]...

Commands:
  interpret   Interpret a genome file (.gb or .fasta)
  db          Database management (download databases, check status)

giae interpret

Options:
  --output, -o PATH           Write report to file (default: stdout)
  --format, -f [report|json]  Output format (default: report)
  --workers, -w INT           Parallel workers, 1–16 (default: 1)
  --no-uniprot                Skip UniProt API (offline mode)
  --no-interpro               Skip EBI HMMER domain search (offline mode)
  --verbose, -v               Show pipeline details

giae db

Manages optional local databases for the plugin layer. The base pipeline (PROSITE) needs no setup — databases here unlock the local HMMER and BLAST plugins.

# Check what's installed
giae db status

# Download PROSITE (latest from ExPASy — updates the bundled copy)
giae db download prosite

# Download SwissProt for local BLAST (requires BLAST+ installed)
giae db download swissprot

# Download Pfam for local HMMER (requires HMMER3 installed)
giae db download pfam

# Force re-download
giae db download prosite --force

Do you need to run giae db? No, for the base pipeline. PROSITE (1,298 patterns) is bundled. giae db is only needed if you want local BLAST or HMMER plugins, which bypass the EBI web API with locally-installed tools and larger databases.

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Project Structure

GIAE/
├── src/giae/
│   ├── analysis/           # Evidence extraction modules
│   │   ├── motif.py        # PROSITE pattern scanning
│   │   ├── prosite.py      # PROSITE database parser
│   │   ├── uniprot.py      # UniProt REST API client
│   │   ├── interpro.py     # EBI HMMER / InterPro client
│   │   ├── hmmer.py        # Local HMMER plugin
│   │   ├── blast_local.py  # Local BLAST plugin
│   │   └── ai.py           # ESM-2 embedding plugin
│   ├── cli/
│   │   ├── main.py         # CLI entrypoint
│   │   └── db.py           # Database management commands
│   ├── engine/
│   │   ├── interpreter.py  # Main orchestrator
│   │   ├── aggregator.py   # Evidence aggregation & weighting
│   │   ├── hypothesis.py   # Hypothesis generation
│   │   ├── confidence.py   # Confidence scoring
│   │   ├── conflict.py     # Conflict detection
│   │   ├── novelty.py      # Novel gene discovery & ranking
│   │   └── plugin.py       # Plugin manager
│   ├── models/             # Core data models
│   │   ├── genome.py
│   │   ├── gene.py
│   │   ├── protein.py
│   │   ├── evidence.py
│   │   └── interpretation.py
│   ├── output/
│   │   ├── report.py       # Markdown report generator
│   │   ├── reasoning.py    # Reasoning chain formatter
│   │   └── json_export.py  # JSON serialization
│   └── parsers/            # FASTA / GenBank parsers
├── tests/                  # pytest test suite
├── case_studies/           # 7 phage GenBank files + benchmark reports
├── data/prosite/           # Bundled PROSITE database (1,298 patterns)
├── pyproject.toml
└── QUICKSTART.md

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Plugin System

GIAE has a plugin architecture for optional heavy-weight evidence sources. Plugins are auto-detected at startup — if the required binary or database path doesn't exist, the plugin is silently skipped. The base pipeline always runs.

Plugin	Requirement	Evidence Type	Weight
HmmerPlugin	HMMER3 + ~/.giae/hmmer/pfam.hmm	DOMAIN_HIT	0.90
BlastLocalPlugin	BLAST+ + ~/.giae/blast/swissprot	BLAST_HOMOLOGY	1.00
EsmPlugin	PyTorch + ESM-2 model	SEQUENCE_FEATURE	0.50

Install plugin dependencies with giae db download pfam / giae db download swissprot.

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📖 Documentation

Visit the official GIAE documentation site: Ayo-Cyber.github.io/GIAE/

🚀 Advanced Features

Interactive HTML Reports

Generate premium, interactive reports with sortable tables and confidence badges.

giae interpret genome.gb --format html -o report.html

High-Performance Caching

GIAE uses a local SQLite database to cache API responses. This makes subsequent runs up to 10x faster.

# Manage the cache
giae db stats
giae db clear

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Roadmap

• Foldseek / AlphaFold structural search — STRUCTURAL_HOMOLOGY evidence (already in codebase); resolves PhiX174-class cases where sequence-based methods fail completely
• EBI BLAST async API — replace text-based UniProt search with real sequence similarity search
• Bacterial genome scaling — currently validated on phages; next target is 4–6 Mb bacterial genomes
• Comparison mode — diff two genome interpretations side by side

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Contributing

Issues, PRs, and genome challenges welcome.

# Run tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=giae --cov-report=term-missing

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Citation

If you use GIAE in research, please cite:

GIAE — Genome Interpretation & Annotation Engine (v0.2.0)
https://github.com/Ayo-Cyber/GIAE

A formal publication is in preparation.

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License

MIT — see LICENSE.

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GIAE v0.2.0 — Benchmarked on Lambda, T7, PhiX174, Phi29, Mu, P22, and T4.